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Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, β-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of β-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (β: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Early detection of polyps is essential to decrease colorectal cancer(CRC) incidence. Therefore, developing an efficient and accurate polyp segmentation technique is crucial for clinical CRC prevention. In this paper, we propose an end-to-end training approach for polyp segmentation that employs diffusion model. The images are considered as priors, and the segmentation is formulated as a mask generation process. In the sampling process, multiple predictions are generated for each input image using the trained model, and significant performance enhancements are achieved through the use of majority vote strategy. Four public datasets and one in-house dataset are used to train and test the model performance. The proposed method achieves mDice scores of 0.934 and 0.967 for datasets Kvasir-SEG and CVC-ClinicDB respectively. Furthermore, one cross-validation is applied to test the generalization of the proposed model, and the proposed methods outperformed previous state-of-the-art(SOTA) models to the best of our knowledge. The proposed method also significantly improves the segmentation accuracy and has strong generalization capability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The increased availability of large clinical datasets together with increasingly sophisticated computing power has facilitated development of numerous risk prediction models for various adverse perioperative outcomes, including acute kidney injury (AKI). The rationale for developing such models is straightforward. However, despite numerous purported benefits, the uptake of preoperative prediction models into clinical practice has been limited. Barriers to implementation of predictive models, including limitations in their discrimination and accuracy, as well as their ability to meaningfully impact clinical practice and patient outcomes, are increasingly recognised. Some of the purported benefits of predictive modelling, particularly when applied to postoperative AKI, might not fare well under detailed scrutiny. Future research should address existing limitations and seek to demonstrate both benefit to patients and value to healthcare systems from implementation of these models in clinical practice., (Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process. To address this issue, we propose an accelerated denoising diffusion probabilistic model via truncated inverse processes (ADDPM) that is specifically designed for medical image segmentation. The inverse process of ADDPM starts from a non-Gaussian distribution and terminates early once a prediction with relatively low noise is obtained after multiple iterations of denoising. We employ a separate powerful segmentation network to obtain pre-segmentation and construct the non-Gaussian distribution of the segmentation based on the forward diffusion rule. By further adopting a separate denoising network, the final segmentation can be obtained with just one denoising step from the predictions with low noise. ADDPM greatly reduces the number of denoising steps to approximately one-tenth of that in vanilla DDPMs. Our experiments on four segmentation tasks demonstrate that ADDPM outperforms both vanilla DDPMs and existing representative accelerating DDPMs methods. Moreover, ADDPM can be easily integrated with existing advanced segmentation models to improve segmentation performance and provide uncertainty estimation. Implementation code: https://github.com/Guoxt/ADDPM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: The potential health effects of airborne polycyclic aromatic hydrocarbons (PAHs) among general population remained extensively unstudied. This study sought to investigate the association of short-term exposure to low-level total and 7 carcinogenic PAHs with mortality risk., Methods: We conducted an individual-level time-stratified case-crossover study in Jiangsu province of eastern China, by investigating over 2 million death cases during 2016-2019. Daily concentrations of total PAH and its 7 carcinogenic species including benzo[a]anthracene (BaA), benzo[a]pyrene (BaP), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), chrysene (Chr), dibenz[a,h]anthracene (DahA), and indeno[1,2,3-cd]pyrene (IcdP), predicted by well-validated spatiotemporal models, were assigned to death cases according to their residential addresses. We estimated mortality risk associated with short-term exposure to increase of an interquartile range (IQR) for aforementioned PAHs using conditional logistic regression., Results: An IQR increase (16.9 ng/m 3 ) in 2-day (the current and prior day) moving average of total PAH concentration was associated with risk increases of 1.90% (95% confidence interval [CI]: 1.71-2.09) in all-cause mortality, 1.90% (95% CI: 1.70-2.10) in nonaccidental mortality, 2.01% (95% CI: 1.72-2.29) in circulatory mortality, and 2.53% (95% CI: 2.03-3.02) in respiratory mortality. Risk increases of cause-specific mortality ranged between 1.42-1.90% for BaA (IQR: 1.6 ng/m 3 ), 1.94-2.53% for BaP (IQR: 1.6 ng/m 3 ), 2.45-3.16% for BbF (IQR: 2.8 ng/m 3 ), 2.80-3.65% for BkF (IQR: 1.0 ng/m 3 ), 1.36-1.77% for Chr (IQR: 1.8 ng/m 3 ), 0.77-1.24% for DahA (IQR: 0.8 ng/m 3 ), and 2.96-3.85% for IcdP (IQR: 1.7 ng/m 3 )., Conclusions: This study provided suggested evidence for heightened mortality risk in relation to short-term exposure to airborne PAHs in general population. Our findings suggest that airborne PAHs may pose a potential threat to public health, emphasizing the need of more population-based evidence to enhance the understanding of health risk under the low-dose exposure scenario., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information. Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk. We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them to produce results in line with, or superior to, parametric spatial ILMs. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rob Deardon reports financial support was provided by Natural Sciences and Engineering Research Council of Canada. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rob Deardon reports financial support was provided by Natural Sciences and Engineering Research Council of Canada. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Public health spatial data are often recorded at different spatial scales (or geographic regions/divisions) and over different correlated variables. Motivated by data from the Dartmouth Atlas Project, we consider jointly analyzing average annual percentages of diabetic Medicare enrollees who have taken the hemoglobin A1c and blood lipid tests, observed at the hospital service area (HSA) and county levels, respectively. Capitalizing on bivariate relationships between these two scales is not immediate as counties are not nested within HSAs. It is well known that one can improve predictions by leveraging correlations across both variables and scales. There are very few methods available that simultaneously model multivariate and multiscale correlations. We propose three new hierarchical Bayesian models for bivariate multiscale spatial data, extending spatial random effects, multivariate conditional autoregressive (MCAR), and ordered hierarchical models through a multiscale spatial approach. We simulated data from each of the three models and compared the corresponding predictions, and found the computationally intensive multiscale MCAR model is more robust to model misspecification. In an analysis of 2015 Texas Dartmouth Atlas Project data, we produced finer resolution predictions (partitioning of HSAs and counties) than univariate analyses, determined that the novel multiscale MCAR and OH models were preferable via out-of-sample metrics, and determined the HSA with the highest within-HSA variability of hemoglobin A1c blood testing. Additionally, we compare the univariate multiscale models to the bivariate multiscale models and see clear improvements in prediction over univariate analyses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years' monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Registering the head and estimating the scalp surface are important for various biomedical procedures, including those using neuronavigation to localize brain stimulation or recording. However, neuronavigation systems rely on manually-identified fiducial head targets and often require a patient-specific MRI for accurate registration, limiting adoption. We propose a practical technique capable of inferring the scalp shape and use it to accurately register the subject's head. Our method does not require anatomical landmark annotation or an individual MRI scan, yet achieves accurate registration of the subject's head and estimation of its surface. The scalp shape is estimated from surface samples easily acquired using existing pointer tools, and registration exploits statistical head model priors. Our method allows for the acquisition of non-trivial shapes from a limited number of data points while leveraging their object class priors, surpassing the accuracy of common reconstruction and registration methods using the same tools. The proposed approach is evaluated in a virtual study with head MRI data from 1152 subjects, achieving an average reconstruction root-mean-square error of 2.95 mm, which outperforms a common neuronavigation technique by 2.70 mm. We also characterize the error under different conditions and provide guidelines for efficient sampling. Furthermore, we demonstrate and validate the proposed method on data from 50 subjects collected with conventional neuronavigation tools and setup, obtaining an average root-mean-square error of 2.89 mm; adding landmark-based registration improves this error to 2.63 mm. The simulation and experimental results support the proposed method's effectiveness with or without landmark annotation, highlighting its broad applicability., Competing Interests: Declaration of competing interest GS is also affiliated with Apple; this work is not related to such affiliation. AVP is inventor on patents and patent applications on transcranial magnetic stimulation (TMS) technology; has equity options and serves on the scientific advisory board of Ampa; and has received patent royalties, research funding, travel support, consulting fees, equipment loans, hardware donations, and/or patent application support from Rogue Research, Magstim, MagVenture, BTL Industries, Soterix Medical, Magnetic Tides, and Ampa. JMDM is a consultant for Restor3d. SMG is inventor on patents and patent applications on transcranial magnetic stimulation (TMS) technology. The remaining authors declare that they do not have competing financial interests or personal relationships that could influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Health economic (HE) models are often considered as "black boxes" because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making. This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes., Methods: The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist. Functionalities of PACBOARD are (1) exploring and validating model parameters and outcomes using standardized validation tests and interactive plots, (2) visualizing and investigating the relationship between model parameters and outcomes using metamodeling, and (3) predicting HE outcomes using the fitted metamodel. To test PACBOARD, 2 mock HE models were developed, and errors were introduced in these models, eg, negative costs inputs, utility values exceeding 1. PACBOARD metamodeling predictions of incremental net monetary benefit were validated against the original model's outcomes., Results: PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared with the original model outcomes., Conclusions: PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodeling based on HE models' parameters and outcomes., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024. Published by Elsevier Inc.)

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies., Competing Interests: Declaration of competing interest There are no competing interests for any author., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Spatial patterns are common in infectious disease epidemiology. Disease mapping is essential to infectious disease surveillance. Under a group testing protocol, biomaterial from multiple individuals is physically combined into a pooled specimen, which is then tested for infection. If the pool tests negative, all contributing individuals are generally assumed to be uninfected. If the pool tests positive, the individuals are usually retested to determine who is infected. When the prevalence of infection is low, group testing provides significant cost savings over traditional individual testing by reducing the number of tests required. However, the lack of statistical methods capable of producing maps from group testing data has limited the use of group testing in disease mapping. We develop a Bayesian methodology that can simultaneously map disease prevalence using group testing data and identify risk factors for infection. We illustrate its real-world utility using two datasets from vector-borne disease surveillance., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Stella Self reports financial support was provided by National Institute of Allergy and Infectious Diseases. Brian Herrin reports financial support was provided by National Institute of Allergy and Infectious Diseases. Melissa Nolan reports financial support was provided by University of South Carolina Office of the Vice President for Research. Rongjie Huang reports financial support was provided by Companion Animal Parasite Council. Stella Self reports financial support was provided by National Institute of General Medical Sciences. Alexander McLain reports financial support was provided by National Institute of General Medical Sciences. Melissa Nolan reports financial support was provided by National Institute of Allergy and Infectious Diseases. Stella Self reports a relationship with Companion Animal Parasite Council that includes: consulting or advisory, funding grants, and travel reimbursement. Brian Herrin reports a relationship with Companion Animal Parasite Council that includes: board membership and travel reimbursement. Stella Self reports a relationship with Merck & Co Inc that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Generalized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations., Methods: We compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV., Results: In simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed., Conclusions: The Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jessie K. Edwards reports financial support was provided by National Institutes of Health. Jessie K. Edwards reports a relationship with The University of North Carolina at Chapel Hill that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Quality study monitoring is fundamental to patient safety and data integrity. Regulators and industry consortia have increasingly advocated for risk-based monitoring (RBM) and central statistical monitoring (CSM) for more effective and efficient monitoring. Assessing which statistical methods underpin these approaches can best identify unusual data patterns in multi-center clinical trials that may be driven by potential systematic errors is important., Methods: We assessed various CSM techniques, including cross-tests, fixed-effects, mixed-effects, and finite mixture models, across scenarios with different sample sizes, contamination rates, and overdispersion via simulation. Our evaluation utilized threshold-independent metrics such as the area under the curve (AUC) and average precision (AP), offering a fuller picture of CSM performance., Results: All CSM methods showed consistent characteristics across center sizes or overdispersion. The adaptive finite mixture model outperformed others in AUC and AP, especially at 30% contamination, upholding high specificity unless converging to a single-component model due to low contamination or deviation. The mixed-effects model performed well at lower contamination rates. However, it became conservative in specificity and exhibited declined performance for binary outcomes under high deviation. Cross-tests and fixed-effects methods underperformed, especially when deviation increased., Conclusion: Our evaluation explored the merits and drawbacks of multiple CSM methods, and found that relying on sensitivity and specificity alone is likely insufficient to fully measure predictive performance. The finite mixture method demonstrated more consistent performance across scenarios by mitigating the influence of outliers. In practice, considering the study-specific costs of false positives/negatives with available resources for monitoring is important., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background and Objective: Mediation analysis is used to gain insight into the mechanisms of exposure-outcome effects by dividing this effect into a direct and an indirect effect. One of the problems of mediation analysis is that in many situations, the standard error of the indirect effect is much lower than the standard errors of the total and direct effect. Because this problem is ignored in the epidemiological literature, the purpose of this paper was to illustrate this problem and to provide an advice regarding the statistical testing of indirect effects in mediation analysis., Methods: To illustrate the problem of the estimation of the standard error of the indirect effect two real life datasets and several simulations are used., Results: The paper shows that the problem of estimating the standard error of the indirect effect was most pronounced when the relationship between exposure and mediator and the relationship between mediator and outcome were equally strong. Furthermore, the magnitude of the estimation problem is different for different strengths of the mediation effect., Conclusion: The indirect effect in mediation analysis should not be tested for statistical significance but the importance of mediation should be evaluated by its clinical relevance., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines., Study Design and Setting: R software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200-3000) and signal-to-noise ratios (SNRs, 0.5-4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated., Results: In setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data., Conclusion: Categorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings., Competing Interests: Declaration of competing interest A.R. reports employment with Greifswald University Hospital. S.U. reports employment with Greifswald University Hospital. S.B. reports employment with the University of Applied Sciences Munich. A.R., S.U., and S.B. declare no competing interest., (Copyright © 2024. Published by Elsevier Inc.)

Background: Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement., Objective: To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors., Data Sources: MEDLINE, EMBASE and Cochrane databases., Eligibility Criteria: We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor., Synthesis Methods: We categorized included studies by the method used to model time-varying predictors., Results: Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy., Conclusions: Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)

Objective: Class imbalance is sometimes considered a problem when developing clinical prediction models and assessing their performance. To address it, correction strategies involving manipulations of the training dataset, such as random undersampling or oversampling, are frequently used. The aim of this article is to illustrate the consequences of these class imbalance correction strategies on clinical prediction models' internal validity in terms of calibration and discrimination performances., Methods: We used both heuristic intuition and formal mathematical reasoning to characterize the relations between conditional probabilities of interest and probabilities targeted when using random undersampling or oversampling. We propose a plug-in estimator that represents a natural correction for predictions obtained from models that have been trained on artificially balanced datasets ("naïve" models). We conducted a Monte Carlo simulation with two different data generation processes and present a real-world example using data from the International Stroke Trial database to empirically demonstrate the consequences of applying random resampling techniques for class imbalance correction on calibration and discrimination (in terms of Area Under the ROC, AUC) for logistic regression and tree-based prediction models., Results: Across our simulations and in the real-world example, calibration of the naïve models was very poor. The models using the plug-in estimator generally outperformed the models relying on class imbalance correction in terms of calibration while achieving the same discrimination performance., Conclusion: Random resampling techniques for class imbalance correction do not generally improve discrimination performance (i.e., AUC), and their use is hard to justify when aiming at providing calibrated predictions. Improper use of such class imbalance correction techniques can lead to suboptimal data usage and less valid risk prediction models., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MP reports having received partial funding from Novartis Pharma and being awarded a research grant from the Center for Stroke Research Berlin (private donations), both outside of the submitted work. Since July 2023, MW has been employed at Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK. JLR reports receiving a grant from Novartis Pharma for a research project on migraine outside of the submitted work. BVC, SK and MVS have no conflicts of interest to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aim: To investigate the usefulness of multistate models (MSM) for determining colorectal cancer (CRC) recurrence rate, to analyse the effect of different factors on tumour recurrence and death, and to assess the impact of recurrence for CRC prognosis., Methods: Observational follow-up study of incident CRC cases disease-free after curative resection in 2006-2013 (n = 994). Recurrence and mortality were analyzed with MSM, as well as covariate effects on transition probabilities., Results: Cumulative incidence of recurrence at 60 months was 13.7%. Five years after surgery, 70.3% of patients were alive and recurrence-free, and 8.4% were alive after recurrence. Recurrence has a negative impact on prognosis, with 5-year CRC-related mortality increasing from 3.8% for those who are recurrence-free 1-year after surgery to 33.6% for those with a recurrence. Advanced stage increases recurrence risk (HR = 1.53) and CRC-related mortality after recurrence (HR = 2.35). CRC-related death was associated with age in recurrence-free patients, and with comorbidity after recurrence. As expected, age≥75 years was a risk factor for non-CRC-related death with (HR = 7.76) or without recurrence (HR = 4.26), while its effect on recurrence risk was not demonstrated., Conclusions: MSM allows detailed analysis of recurrence and mortality in CRC. Recurrence has a negative impact on prognosis. Advanced stage was a determining factor for recurrence and CRC-death after recurrence., Competing Interests: Conflict of interest None., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Prediction models are everywhere in clinical medicine. We use them to assign a diagnosis or a prognosis, and there have been continuous efforts to develop better prediction models. It is important to understand the fundamentals of prediction modelling, thus, we herein describe nine steps to develop and validate a clinical prediction model with the intention of implementing it in clinical practice: Determine if there is a need for a new prediction model; define the purpose and intended use of the model; assess the quality and quantity of the data you wish to develop the model on; develop the model using sound statistical methods; generate risk predictions on the probability scale (0-100%); evaluate the performance of the model in terms of discrimination, calibration, and clinical utility; validate the model using bootstrapping to correct for the apparent optimism in performance; validate the model on external datasets to assess the generalisability and transportability of the model; and finally publish the model so that it can be implemented or validated by others., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: This study aimed to identify the risk factors of carbapenem-resistant Enterobacteriaceae (CRE) acquisition to build a nomogram for CRE acquisition risk prediction and evaluate its performance. Methods: This unmatched case-control study included 352 adult patients (55 patients and 297 controls) admitted to the intensive care unit (ICU) of a 453-bed secondary referral hospital between January 1, 2018, and September 31, 2019, in Busan, South Korea. The nomogram was built with the identified risk factors using multiple logistic regression analysis. Its performance was analyzed using calibration-in-the-large, the slope of the calibration plot, concordance statistic (c-statistic), and the sensitivity and specificity of the training set, subsets, and a new test set. Results: The risk factors of CRE acquisition among ICU patients at a secondary referral hospital were Acute Physiology and Chronic Health Evaluation II score at the time of admission, use of a central venous catheter and a nasogastric tube, as well as use of cephalosporin antibiotics. At 20.0% of the predicted CRE acquisition risk in the training set, the calibration-in-the-large was 0, slope of the calibration plot was 1, c-statistic was .93, sensitivity was 85.5%, and specificity was 84.8%. The performance was relatively good in the subsets and new test set. Conclusion: The nomogram can be used to monitor the CRE acquisition risk for ICU patients who have a similar case mix to patients in the study hospitals. Future studies need to involve more rigorous methodology and larger samples.

Summary: Background: Hypophosphatemic rickets is a rare, genetic syndrome with multisystem involvement. It causes skeletal abnormalities, painful enthesopathies, increased risk of fracture, and short stature; leading to a substantial burden of disease, disability, and worsening of quality of life. To improve health conditions of people living with this disease, it is essential to know its prevalence which is currently unknown in Colombia. This study aimed to estimate the prevalence of hypophosphatemic rickets in Colombia by using a mathematical model and national statistic records. Methods: We executed a model to estimate probabilities of transitions between health, disease, and death states (Markov chains). The model was fed with international prevalences taken from original studies (systematic review) and administrative records’ data from SISPRO (a national health information system) using the International Classification of Diseases (ICD-10) E833 code, vital statistics, and census data. World Health Organization's (WHO) DISMOD II software was used to develop the model. Findings: The estimated overall prevalence of hypophosphatemic rickets in Colombia in 2018 was 2·03 cases per 100 000 people (981 affected people), with a sensitive range of 1·97 to 2·09. The estimated prevalence by sex was 2·61 (645 people) and 1·43 (336 people) cases per 100 000 women and men, respectively. Interpretation: Our overall estimated prevalence shows consistency with original international data. This is the first prevalence estimation of hypophosphatemic rickets in Colombia and will be relevant to support public health decisions for rare diseases and to provide a pre-test probability framework in clinical practice. DISMOD II and the model are useful tools to estimate the prevalence of rare and orphan diseases, when probabilistic studies cannot be carried out. There are limited bibliographic resources worldwide reporting prevalence values supported by original studies. Our study can be used as a cost-effective methodology reference in this regard, especially for Latin America. Funding: Ultragenyx Pharmaceutical, as a donation.

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM 2.5 : diameter ≤ 2.5 μm) and ozone (O 3 ) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM 2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM 2.5 across New Mexico was 0.3 μg/m 3 , though 1 h maximum exceeded 120 μg/m 3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O 3 (MDA8-O 3 ) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O 3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM 2.5 and MDA8-O 3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15-21) and 4 (95 % CI: 3-6) deaths. Additionally, we estimate that wildfire PM 2.5 was responsible for 171 (95 %: 124-217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R 2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R 2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R 2  = 0.87 - 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R 2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 - 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks, particularly in image generation. However, their notable performance heavily relies on labelled datasets, which limits their application in medical images due to the associated high-cost annotations. Current DPM-related methods for lesion detection in medical imaging, which can be categorized into two distinct approaches, primarily rely on image-level annotations. The first approach, based on anomaly detection, involves learning reference healthy brain representations and identifying anomalies based on the difference in inference results. In contrast, the second approach, resembling a segmentation task, employs only the original brain multi-modalities as prior information for generating pixel-level annotations. In this paper, our proposed model - discrepancy distribution medical diffusion (DDMD) - for lesion detection in brain MRI introduces a novel framework by incorporating distinctive discrepancy features, deviating from the conventional direct reliance on image-level annotations or the original brain modalities. In our method, the inconsistency in image-level annotations is translated into distribution discrepancies among heterogeneous samples while preserving information within homogeneous samples. This property retains pixel-wise uncertainty and facilitates an implicit ensemble of segmentation, ultimately enhancing the overall detection performance. Thorough experiments conducted on the BRATS2020 benchmark dataset containing multimodal MRI scans for brain tumour detection demonstrate the great performance of our approach in comparison to state-of-the-art methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Species sensitivity distributions (SSDs) estimated by fitting a statistical distribution to ecotoxicity data are indispensable tools used to derive the hazardous concentration for 5 % of species (HC5) and thereby a predicted no-effect concentration in environmental risk assessment. Whereas various statistical distributions are available for SSD estimation, the fundamental question of which statistical distribution should be used has received limited systematic analysis. We aimed to address this knowledge gap by applying four frequently used statistical distributions (log-normal, log-logistic, Burr type III, and Weibull distributions) to acute and chronic SSD estimation using aquatic toxicity data for 191 and 31 chemicals, respectively. Based on the differences in the corrected Akaike's information criterion (AICc) as well as visual inspection of the fitting of the lower tails of SSD curves, the log-normal SSD was generally better or equally good for the majority of chemicals examined. Together with the fact that the ratios of HC5 values of other alternative SSDs to those of log-normal SSDs generally fell within the range 0.1-10, our findings indicate that the log-normal distribution can be a reasonable first candidate for SSD derivation, which does not contest the existing widespread use of log-normal SSDs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference., Methods: Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation., Results: Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation., Conclusion: Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: To apply a hierarchical model (HM) that addresses measurement error in regression of the treatment effect on the control group event rate (CR). We compare HM to weighted linear regression (WLR) which is subject to measurement error and mathematical coupling., Study Design and Setting: We reviewed published HMs that address measurement error and implemented a Bayesian version in open-source code to facilitate adoption by meta-analysts. We compared WLR and HM across a very large convenience sample of meta-analyses published in the Cochrane Database of Systematic Reviews., Results: We applied both approaches (WLR and an HM that addresses measurement error) to 3193 meta-analyses that included 33,071 studies (average 10.28 studies per meta-analysis). A statistically significant slope suggesting an association between the treatment effect and CR was demonstrated with both approaches in 568 (17.19%) meta-analyses, with neither approach in 2036 (63.77%) meta-analyses, only with WLS in 229 (7.17%) and only with HM in 360 (11.28%) meta-analyses. The majority of slopes was negative (WLR 85%, HM 83%). In the majority of cases, HM had wider confidence intervals (72.53%) and slopes farther from the null (64.77%)., Conclusion: Approximately 28% of meta-analyses demonstrate a significant association between the treatment effect and CR when HM is used to address measurement error, which can suggest frequent lack of portability of the relative effect across baseline risks. User-friendly open-source code is provided to meta-analysts interested in exploring this association., Competing Interests: Declaration of competing interest H.C. is employed by Pfizer and owns Pfizer stocks., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The present work develops an effective bioadsorbent of cross-linked chitosan-genipin/SiO 2 adsorbent (CHI-GNP/SiO 2 ). The developed CHI-GNP/SiO 2 was employed for the removal of organic dye (reactive orange 16, RO16) from simulated wastewater. The optimization of the fundamental adsorption variables (CHI-GNP/SiO 2 dose, time, and pH) via the Box-Behnken design (BBD) was attained for achieving maximal adsorption capacity and high removal efficiency. The good agreement between the Freundlich isotherms and empirical data of RO16 adsorption by CHI-GNP/SiO 2 indicates that the adsorption process follows a multilayer adsorption mechanism. The reasonable agreement between the pseudo-second-order model and the kinetic data of RO16 adsorption by CHI-GNP/SiO 2 was obtained. The maximum RO16 adsorption capacity (q max ) of CHI-GNP/SiO 2 was identified to be 57.1 mg/g. The adsorption capacity of CHI-GNP/SiO 2 is attributed to its unique surface properties, including its highly porous structure and the presence of functional groups such as amino and hydroxyl groups. According to the results of this investigation, CHI-GNP/SiO 2 has the potential to be an adsorbent for the removal of acidic dyes from wastewater., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods., Methods: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance., Results: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies., Conclusions: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Prognostic models incorporate multiple prognostic factors to estimate the likelihood of future events for individual patients based on their prognostic factor values. Evaluating these models crucially involves conducting studies to assess their predictive performance, like discrimination. Systematic reviews and meta-analyses of these validation studies play an essential role in selecting models for clinical practice., Methods: In this paper, we outline 3 thresholds to determine the target for certainty rating in the discrimination of prognostic models, as observed across a body of validation studies., Results and Conclusion: We propose 3 thresholds when rating the certainty of evidence about a prognostic model's discrimination. The first threshold amounts to rating certainty in the model's ability to classify better than random chance. The other 2 approaches involve setting thresholds informed by other mechanisms for classification: clinician intuition or an alternative prognostic model developed for the same disease area and outcome. The choice of threshold will vary based on the context. Instead of relying on arbitrary discrimination cut-offs, our approach positions the observed discrimination within an informed spectrum, potentially aiding decisions about a prognostic model's practical utility., Competing Interests: Declaration of competing interest All authors declare they did not receive support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years exist or do other relationships or activities that could appear to have influenced the submitted work. All authors are members of the GRADE working group., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Panel data are ubiquitous in scientific fields such as social sciences. Various modeling approaches have been presented for observational causal inference based on such data. Existing approaches typically impose restrictive assumptions on the data-generating process such as Gaussian responses or time-invariant effects, or they can only consider short-term causal effects. To surmount these restrictions, we present the dynamic multivariate panel model (DMPM) that supports time-varying, time-invariant, and individual-specific effects, multiple responses across a wide variety of distributions, and arbitrary dependency structures of lagged responses of any order. We formally demonstrate how DMPM facilitates causal inference within the structural causal modeling framework and we take a Bayesian approach for the estimation of the posterior distributions of the model parameters and causal effects of interest. We demonstrate the use of DMPM by applying the approach to both real and synthetic data., Competing Interests: Declarations of interest Declarations of interest: none., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: To develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique., Study Design and Setting: We followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed., Results: This consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples., Conclusion: We proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste., Competing Interests: Declaration of competing interest The authors declare that they have no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results., Methods: We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data., Results: By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes., Conclusions: We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the 'zero by 30' global mission., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Former Associate Editor, Spatial and Spatio-temporal Epidemiology (Mark Stevenson), (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-K Ridge -d RVFL -R idge ). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials., Methods: Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way., Results: We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting., Conclusion: Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Fabian Falck, Sahra Ghalebikesabi, Stephen Gardiner, Luis Santos, Thomas E. Nichols, Ann-Marie Mallon, Luke Kelly and George Nicholson report that financial support was provided by Novartis Pharmaceuticals Corporation, East Hanover, United States. Marc Vandemeulebroecke reports a relationship with Novartis Pharma AG, Basel, Switzerland that included, during the period of this research, employment and equity or stocks. Cong Zhang, Chengeng Tian and Yu Pang report a relationship with China Novartis Institutes for Bio-medical Research CO., Shanghai, China that included, during the period of this research, employment and equity or stocks. Xuan Zhu, Matthias Kormaksson, Ruvie Martin, Aimee Readie, Gregory Ligozio, Kunal K. Gandhi and David Ohlssen report a relationship with Novartis Pharmaceuticals Corporation, East Hanover, United States that included, during the period of this research, employment and equity or stocks., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: WHO, as requested by its member states, launched the Expanded Programme on Immunization (EPI) in 1974 to make life-saving vaccines available to all globally. To mark the 50-year anniversary of EPI, we sought to quantify the public health impact of vaccination globally since the programme's inception., Methods: In this modelling study, we used a suite of mathematical and statistical models to estimate the global and regional public health impact of 50 years of vaccination against 14 pathogens in EPI. For the modelled pathogens, we considered coverage of all routine and supplementary vaccines delivered since 1974 and estimated the mortality and morbidity averted for each age cohort relative to a hypothetical scenario of no historical vaccination. We then used these modelled outcomes to estimate the contribution of vaccination to globally declining infant and child mortality rates over this period., Findings: Since 1974, vaccination has averted 154 million deaths, including 146 million among children younger than 5 years of whom 101 million were infants younger than 1 year. For every death averted, 66 years of full health were gained on average, translating to 10·2 billion years of full health gained. We estimate that vaccination has accounted for 40% of the observed decline in global infant mortality, 52% in the African region. In 2024, a child younger than 10 years is 40% more likely to survive to their next birthday relative to a hypothetical scenario of no historical vaccination. Increased survival probability is observed even well into late adulthood., Interpretation: Since 1974 substantial gains in childhood survival have occurred in every global region. We estimate that EPI has provided the single greatest contribution to improved infant survival over the past 50 years. In the context of strengthening primary health care, our results show that equitable universal access to immunisation remains crucial to sustain health gains and continue to save future lives from preventable infectious mortality., Funding: WHO., Competing Interests: Declaration of interests CLT and KAMG assert that their employer, Imperial College, receives funding for the Vaccine Impact Modelling Consortium from the Bill & Melinda Gates Foundation; Gavi, the Vaccine Alliance; and the Wellcome Trust. CLT has received consulting fees from GSK for attending an advisory board meeting on CMV vaccines in May, 2022 and is pro bono Chair of the Scientific Advisory Panel of the Meningitis Research Foundation. HF asserts that her employer, London School of Hygiene & Tropical Medicine, receives funding for the Vaccine Impact Modelling Consortium from the Gates Foundation. JFM asserts that his employer, University of Washington, receives grant funding from Gavi and from the Gates Foundation. KB and KMT assert that their organisation Kid Risk holds a cooperative agreement with the US Centers for Disease Control and Prevention and holds grants from the Gates Foundation. MJF asserts that his employer, Penn State University, is a subrecipient of funds from Imperial College London for a grant from Gavi and that he holds grants from the Gates Foundation and the US National Science Foundation. MJ asserts that his employer, London School of Hygiene & Tropical Medicine, receives funding from the UK National Institute of Health Research, RCUK; the Gates Foundation; Gavi; the Wellcome Trust; WHO; the European Commission; the US Centers for Disease Control and Prevention; the Hong Kong Special Administrative Region Government; and the Task Force for Global Health. RAH and SPS assert that their employer, University of Cape Town, receives grant funding from the African Field Epidemiology Network and the US Centers for Disease Control and Prevention. RGW asserts that he receives funding from the Wellcome Trust (grant numbers 218261/Z/19/Z), National Institutes of Health (1R01AI147321-01, G-202303-69963, and R-202309-71190), European and Developing Countries Clinical Trials Partnership (RIA208D-2505B), UK Medical Research Council (CCF17-7779 via SET Bloomsbury), UK Economic and Social Research Council (ES/P008011/1), Bill & Melinda Gates Foundation (INV-004737 and INV-035506), and WHO (2020/985800-0). AL, KLO-B, NB-Z, PL, RCWH, and SYS work for WHO. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Air pollution constitutes a substantial peril to human health, thereby catalyzing the evolution of an array of air quality prediction models. These models span from mechanistic and statistical strategies to machine learning methodologies. The burgeoning field of deep learning has given rise to a plethora of advanced models, which have demonstrated commendable performance. However, previous investigations have overlooked the salience of quantifying prediction uncertainties and potential future interconnections among air monitoring stations. Moreover, prior research typically utilized static predetermined spatial relationships, neglecting dynamic dependencies. To address these limitations, we propose a model named Dynamic Spatial-Temporal Denoising Diffusion Probabilistic Model (DST-DDPM) for air quality prediction. Our model is underpinned by the renowned denoising diffusion model, aiding us in discerning indeterminacy. In order to encapsulate dynamic patterns, we design a dynamic context encoder to generate dynamic adjacency matrices, whilst maintaining static spatial information. Furthermore, we incorporate a spatial-temporal denoising model to concurrently learn both spatial and temporal dependencies. Authenticating our model's performance using a real-world dataset collected in Beijing, the outcomes indicate that our model eclipses other baseline models in terms of both short-term and long-term predictions by 1.36% and 11.62% respectively. Finally, we conduct a case study to exhibit our model's capacity to quantify uncertainties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Meta-analysis is a statistical method used to combine results from multiple studies, providing a quantitative summary of their findings. One of the fundamental decisions in conducting a meta-analysis is choosing an appropriate model to estimate the overall effect size and its CI. In this article, we focus on the common-effect (also referred to as the fixed-effect) model, and in a companion article, the random-effects model. These models are the two prevailing meta-analysis models employed in the literature. In this article, we outline the key assumption underlying the common-effect model, describe different common-effect methods (ie, inverse variance, Peto, and Mantel-Haenszel), and highlight characteristics of the meta-analysis that should be considered when selecting a method. Furthermore, we demonstrate the application of these methods to a dataset. Understanding the common-effect model is important for knowing when to use the model and how to interpret the overall effect size and its CI., Competing Interests: Declaration of competing interest Dr Areti Angeliki Veroniki is a Statistical Associate Editor for the Journal of Clinical Epidemiology, but had no involvement with the peer review process or decision for publication. There are no competing interests for any other author., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Objective: Protocols are invaluable documents for any research study, especially for prediction model studies. However, the mere existence of a protocol is insufficient if key details are omitted. We reviewed the reporting content and details of the proposed design and methods reported in published protocols for prediction model research., Methods: We searched MEDLINE, Embase, and the Web of Science Core Collection for protocols for studies developing or validating a diagnostic or prognostic model using any modeling approach in any clinical area. We screened protocols published between Jan 1, 2022 and June 30, 2022. We used the abstract, introduction, methods, and discussion sections of The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement to inform data extraction., Results: We identified 30 protocols, of which 28 were describing plans for model development and six for model validation. All protocols were open access, including a preprint. 15 protocols reported prospectively collecting data. 21 protocols planned to use clustered data, of which one-third planned methods to account for it. A planned sample size was reported for 93% development and 67% validation analyses. 16 protocols reported details of study registration, but all protocols reported a statement on ethics approval. Plans for data sharing were reported in 13 protocols., Conclusion: Protocols for prediction model studies are uncommon, and few are made publicly available. Those that are available were reasonably well-reported and often described their methods following current prediction model research recommendations, likely leading to better reporting and methods in the actual study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA., Competing Interests: Declaration of competing interest The authors declare no potential conflict of interests., (Copyright © 2023. Published by Elsevier Inc.)

Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space - independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Stefan Zachow reports a relationship with 1000shapes GmbH that includes: board membership and equity or stocks. Bjoern Menze is a member of the Editorial Board of Medical Image Analysis., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Chemical equivalence testing can be used to assess the biocompatibility implications of a materials or manufacturing change for a medical device. This testing can provide a relatively facile means to evaluate whether the change may result in additional or different toxicological concerns. However, one of the major challenges in the interpretation of chemical equivalence data is the lack established criteria for determining if two sets of extractables data are effectively equivalent. To address this gap, we propose a two-part approach based upon a relatively simple statistical model. First, the probability of a false positive conclusion, wherein there is an incorrectly perceived increase for a given analyte in the comparator relative to the baseline device, can be reduced to a prescribed level by establishing an appropriate acceptance criterion for the ratio of the observed means. Second, the probability of a false negative conclusion, where an actual increase in a given analyte cannot be discerned from the test results, can be minimized by specifying a limiting value of applicability based on the margin of safety (MoS) of the analyte. This approach provides a quantitative, statistically motivated method to interpret chemical equivalence data, despite the relatively high intrinsic variability and small number of replicates typically associated with a chemical characterization evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)