Search

The delivery of academic statistical collaboration resources can vary among types of institutions and across time. In particular, this variation might occur in the management of infrastructure and the business model, the staffing model and opportunities for staff development. In this manuscript, we present examples of these three themes in modern academic statistical collaboration units and describe key advantages and challenges. [ABSTRACT FROM AUTHOR]

Background: Compared to other providers, nurses spend more time with patients, but the exact quantity and nature of those interactions remain largely unknown. The purpose of this study was to characterize the interactions of nurses at the bedside using continuous surveillance over a year long period. Methods: Nurses' time and activity at the bedside were characterized using a device that integrates the use of obfuscated computer vision in combination with a Bluetooth beacon on the nurses' identification badge to track nurses' activities at the bedside. The surveillance device (AUGi) was installed over 37 patient beds in two medical/surgical units in a major urban hospital. Forty‐nine nurse users were tracked using the beacon. Data were collected 4/15/19–3/15/20. Statistics were performed to describe nurses' time and activity at the bedside. Results: A total of n = 408,588 interactions were analyzed over 670 shifts, with >1.5 times more interactions during day shifts (n = 247,273) compared to night shifts (n = 161,315); the mean interaction time was 3.34 s longer during nights than days (p < 0.0001). Each nurse had an average of 7.86 (standard deviation [SD] = 10.13) interactions per bed each shift and a mean total interaction time per bed of 9.39 min (SD = 14.16). On average, nurses covered 7.43 beds (SD = 4.03) per shift (day: mean = 7.80 beds/nurse/shift, SD = 3.87; night: mean = 7.07/nurse/shift, SD = 4.17). The mean time per hourly rounding (HR) was 69.5 s (SD = 98.07) and 50.1 s (SD = 56.58) for bedside shift report. Discussion: As far as we are aware, this is the first study to provide continuous surveillance of nurse activities at the bedside over a year long period, 24 h/day, 7 days/week. We detected that nurses spend less than 1 min giving report at the bedside, and this is only completed 20.7% of the time. Additionally, hourly rounding was completed only 52.9% of the time and nurses spent only 9 min total with each patient per shift. Further study is needed to detect whether there is an optimal timing or duration of interactions to improve patient outcomes. Clinical Relevance: Nursing time with the patient has been shown to improve patient outcomes but precise information about how much time nurses spend with patients has been heretofore unknown. By understanding minute‐by‐minute activities at the bedside over a full year, we provide a full picture of nursing activity; this can be used in the future to determine how these activities affect patient outcomes. [ABSTRACT FROM AUTHOR]

Studies have shown that stress has contributed to employee turnover and retention problems for agencies, and at the individual level, chronic stress has been associated with coronary heart disease, anxiety, depression, and many other negative effects. In the past, the extent of stress one has felt has been measured by subjective paper-and-pencil instruments; however, recent technological advances have improved our ability to obtain accurate biofeedback assessments from wearable instruments. The Kentucky Child Welfare Workforce Wellness Initiative is the first known study to explore physiological stress in a sample (n = 32) of child welfare professionals using biometric technology (Firstbeat Bodyguard 2) and the first to report that data longitudinally over a four-month period. The study revealed that a variable associated with the strength of the Autonomic Nervous System (RMSSD) remained below the norms for a healthy population as participants experienced consistent and prolonged physiological stress. When examined relatively to the agency's lifting of COVID restrictions and returning to face-to-face service delivery, stress levels began to further rise almost to significant levels (p <.10) and the participants' ability to achieve a state of physiological relaxation significantly decreased. Future research employing biometric technology is also suggested. [ABSTRACT FROM AUTHOR]

This study addresses the question to which extent individual online self-presentations become more similar globally, due globalisation and digitalisation, or whether ethno-racial identity predisposes individuals' online self-presentation. That is, we examined the degree to which individuals varying in ethno-racial identity converge or diverge in online self-presentation. A large-scale content analysis was conducted by collecting selfies on Instagram (i.e. #selfietime; N = 3881). Using facial recognition software, selfies were allotted into a specific ethno-racial identity based on race/ethnicity-related appearance features (e.g. Asian, Black, Hispanic, and White identity) as a proxy for externally imposed ethno-racial identity. Results provided some evidence for convergence in online self-construction among selfie-takers, but generally revealed that self-presentations diverge as a function of ethno-racial identity. That is, results showed more convergence between ethno-racial identity for portraying selfies with objectified elements, whereas divergence in online self-presentations occurred for portraying contextualised selves and filter usage. In all, this study examined the complexity of online self-presentation. Here, we extend earlier cross-cultural research by exploring the convergence-divergence paradigm for the role of externally imposed ethno-racial identity in online self-presentation. Findings imply that ethno-racial identity characteristics remain important in manifestations of online self-presentations. [ABSTRACT FROM AUTHOR]

The clinician's ability to provide evidence-based orthodontics is directly related to their adeptness at dissecting published research, which in turn is heavily dependent on the proper understanding of the tenets of research design and biostatistics. Using relevant clinical examples, we discuss key principles affecting the translation of the results of single research studies into clinical practice, including effect modifiers and confounders, study design, central tendency and outliers. The application of the same scrutiny to higher level meta-epidemiological evidence is also illustrated in addition to a detailed discussion of the applicability and limitations of prediction studies. We conclude by highlighting how the particulars of publishing undermine the ultimate goal of transparency in dissemination of research findings and how they may restrict the ability to critically scrutinize and build upon seminal landmark studies that, just like other research, are limited by the original research design and statistical protocols employed. [ABSTRACT FROM AUTHOR]

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84–197 subjects in each study with 562–888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini–Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used. [ABSTRACT FROM AUTHOR]

Statistical analysis, while at first glance an objective way to extract insights from data, remains at its core a human endeavor. Elements of subjectivity are introduced by the many decisions that go into the selection of a statistical method. Such subjectivity may harm the evidentiary value of results from statistical analyses. Standardization of statistical methods decreases the degrees of freedom available to researchers and may thus be seen as a way to increase the objectivity of the analysis. Here, we argue that standardization of methods is not only impossible because statistical methods rely on assumptions that need to be considered on a case‐by‐case basis but also undesirable because it may block innovation. We propose that the entheseal changes field is better served by standardization of reporting and discuss how reporting guidelines may be developed based on examples from biostatistics. [ABSTRACT FROM AUTHOR]

In this introduction to the special issue, Adaptive Tools for Resilient Bones: Biostatistical Approaches to Past Physical Activity in Osteoarchaeology, we discuss the outcome of the workshop held in Leiden (the Netherlands; November 18–19, 2021). We review statistical approaches to entheseal changes and present a series of new contributions to this field. These research, commentary, and review articles present different statistical approaches to entheseal changes and reflect the current state of research in the field. [ABSTRACT FROM AUTHOR]

Overdispersion models have been extensively studied for correlated normal and binomial data but much less so for correlated multinomial data. In this work, we describe a multinomial overdispersion model that leads to the specification of the first two moments of the outcome and allows the estimation of the global parameters using generalized estimating equations (GEE). We introduce a Global Blinding Index as a target parameter and illustrate the application of the GEE method to its estimation from (1) a clinical trial with clustering by practitioner and (2) a meta-analysis on psychiatric disorders. We examine the impact of a small number of clusters, high variability in cluster sizes, and the magnitude of the intraclass correlation on the performance of the GEE estimators of the Global Blinding Index using the data simulated from different models. We compare these estimators with the inverse-variance weighted estimators and a maximum-likelihood estimator, derived under the Dirichlet-multinomial model. Our results indicate that the performance of the GEE estimators was satisfactory even in situations with a small number of clusters, whereas the inverse-variance weighted estimators performed poorly, especially for larger values of the intraclass correlation coefficient. Our findings and illustrations may be instrumental for practitioners who analyze clustered multinomial data from clinical trials and/or meta-analysis.

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm. [ABSTRACT FROM AUTHOR]

Introduction: According to the Master Plan 2020, science education will play a critical role in future medical curricula. Science modules have already been implemented at many locations. Other medical faculties will follow in the next few years, as legislation is expected to make recommendations of the national competence-based learning objectives curriculum for medicine (NKLM) mandatory. This article aims to present an implementation example from epidemiology and biometry as a contribution to the didactic discussions within the data sciences in medicine. Project description: We report on our experiences with a data analysis project for second-year medical students, which has been compulsory at the Faculty of Medicine and Health Sciences since 2019. The project is intended to train the scientific skills required from the subjects of epidemiology and biometry for student research projects. Emphasis is placed on responsible data handling, transparency, and reproducibility. For example, the writing of a statistical analysis plan is required prior to data access. Improved standardization of materials, optional use of the English language, and digital support will be implemented to help manage the project when student numbers increase. Discussion: The experience from five years is very positive, although a formal evaluation of the learning success is still pending. Current challenges concern staffing, additional time and supervision requirements for those students who do statistical programming with R, and improved integration into the medical curriculum. [ABSTRACT FROM AUTHOR]

Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e.g., 30-day readmission rate) to a normative standard (e.g., national "average"). Profiling methods exist mostly for non time-varying patient outcomes. However, for patients on dialysis, a unique population which requires continuous medical care, methodologies to monitor patient outcomes continuously over time are particularly relevant. Thus, we introduce a novel time-dynamic profiling (TDP) approach to assess the time-varying 30-day readmission rate. TDP is used to estimate, for the first time, the risk-standardized time-dynamic 30-day hospital readmission rate, throughout the time period that patients are on dialysis. We develop the framework for TDP by introducing the standardized dynamic readmission ratio as a function of time and a multilevel varying coefficient model with facility-specific time-varying effects. We propose estimation and inference procedures tailored to the problem of TDP and to overcome the challenge of high-dimensional parameters when examining thousands of dialysis facilities.

This paper contributes to the ongoing reassessment of the controversy between William Bateson and Karl Pearson by characterising what we call "Batesonian Mendelism" and "Pearsonian biometry" as coherent and competing scientific outlooks. Contrary to the thesis that such a controversy stemmed from diverging theoretical commitments on the nature of heredity and evolution, we argue that Pearson's and Bateson's alternative views on those processes ultimately relied on different appraisals of the methodological value of the statistical apparatus developed by Francis Galton. Accordingly, we contend that Bateson's belief in the primacy of cross-breeding experiments over statistical analysis constituted a minimal methodological unifying condition ensuring the internal coherence of Batesonian Mendelism. Moreover, this same belief implied a view of the study of heredity and evolution as an experimental endeavour and a conception of heredity and evolution as fundamentally discontinuous processes. Similarly, we identify a minimal methodological unifying condition for Pearsonian biometry, which we characterise as the view that experimental methods had to be subordinate to statistical analysis, according to methodological standards set by biometrical research. This other methodological commitment entailed conceiving the study of heredity and evolution as subsumable under biometry and primed Pearson to regard discontinuous hereditary and evolutionary processes as exceptions to a statistical norm. Finally, we conclude that Batesonian Mendelism and Pearsonian biometry represented two potential versions of a single genetics-based evolutionary synthesis since the methodological principles and the phenomena that played a central role in the former were also acknowledged by the latter—albeit as fringe cases—and conversely. [ABSTRACT FROM AUTHOR]

Background: Drug classes are grouped based on their chemical and pharmacological properties, but prescription and illicit drugs differ in other important ways. Potential differences in genetic and environmental influences on the (mis)use of prescription and illicit drugs that are subsumed under the same class should be examined. Opioid and stimulant classes contain prescription and illicit forms differentially associated with salient risk factors (common route of administration, legality), making them useful comparators for addressing this etiological issue. Methods: A total of 2410 individual Australian twins [Mage = 31.77 (s.d. = 2.48); 67% women] were interviewed about prescription misuse and illicit use of opioids and stimulants. Univariate and bivariate biometric models partitioned variances and covariances into additive genetic, shared environmental, and unique environmental influences across drug types. Results: Variation in the propensity to misuse prescription opioids was attributable to genes (41%) and unique environment (59%). Illicit opioid use was attributable to shared (71%) and unique (29%) environment. Prescription stimulant misuse was attributable to genes (79%) and unique environment (21%). Illicit stimulant use was attributable to genes (48%), shared environment (29%), and unique environment (23%). There was evidence for genetic influence common to both stimulant types, but limited evidence for genetic influence common to both opioid types. Bivariate correlations suggested that prescription opioid use may be more genetically similar to prescription stimulant use than to illicit opioid use. Conclusions: Prescription opioid misuse may share little genetic influence with illicit opioid use. Future research may consider avoiding unitary drug classifications, particularly when examining genetic influences. [ABSTRACT FROM AUTHOR]

Purpose: To identify anatomical factors affecting the outcome of dcryocystorhinostomy (DCR).The study included the results of dacryocystography in 73 patients after DCR: 37 cases of failed DCR and 36 cases of successful DCR. Biometric characteristics of the formed ostium were evaluated: the horizontal size of the bony “window” and the soft tissue part of the ostium, the vertical size of the bony “window” and soft tissue ostium, the height of the fragment of the remaining bone above and below the line of the common canaliculus, and the height of the “pocket” formed below the lower edge of the ostium. Statistical analysis was performed using parametric and non-parametric statistical methods. Differences were considered significant at p ≤ 0.05.Intergroup differences were identified in the values of the maximum horizontal size of the bony “window” (p = 0.015), the maximum horizontal size of the soft tissue “window” (p < 0.001), the maximum vertical size of the soft tissue “window” (p < 0.001), and the height of the fragment of the remaining bone below the level of the common canaliculus to the edge of the formed ostium (p = 0.004).The stage of forming the bony “window” influences the success of DCR. Not only the position of the “window” is important, but also the geometric properties of the formed ostium.Methods: To identify anatomical factors affecting the outcome of dcryocystorhinostomy (DCR).The study included the results of dacryocystography in 73 patients after DCR: 37 cases of failed DCR and 36 cases of successful DCR. Biometric characteristics of the formed ostium were evaluated: the horizontal size of the bony “window” and the soft tissue part of the ostium, the vertical size of the bony “window” and soft tissue ostium, the height of the fragment of the remaining bone above and below the line of the common canaliculus, and the height of the “pocket” formed below the lower edge of the ostium. Statistical analysis was performed using parametric and non-parametric statistical methods. Differences were considered significant at p ≤ 0.05.Intergroup differences were identified in the values of the maximum horizontal size of the bony “window” (p = 0.015), the maximum horizontal size of the soft tissue “window” (p < 0.001), the maximum vertical size of the soft tissue “window” (p < 0.001), and the height of the fragment of the remaining bone below the level of the common canaliculus to the edge of the formed ostium (p = 0.004).The stage of forming the bony “window” influences the success of DCR. Not only the position of the “window” is important, but also the geometric properties of the formed ostium.Results: To identify anatomical factors affecting the outcome of dcryocystorhinostomy (DCR).The study included the results of dacryocystography in 73 patients after DCR: 37 cases of failed DCR and 36 cases of successful DCR. Biometric characteristics of the formed ostium were evaluated: the horizontal size of the bony “window” and the soft tissue part of the ostium, the vertical size of the bony “window” and soft tissue ostium, the height of the fragment of the remaining bone above and below the line of the common canaliculus, and the height of the “pocket” formed below the lower edge of the ostium. Statistical analysis was performed using parametric and non-parametric statistical methods. Differences were considered significant at p ≤ 0.05.Intergroup differences were identified in the values of the maximum horizontal size of the bony “window” (p = 0.015), the maximum horizontal size of the soft tissue “window” (p < 0.001), the maximum vertical size of the soft tissue “window” (p < 0.001), and the height of the fragment of the remaining bone below the level of the common canaliculus to the edge of the formed ostium (p = 0.004).The stage of forming the bony “window” influences the success of DCR. Not only the position of the “window” is important, but also the geometric properties of the formed ostium.Conclusion: To identify anatomical factors affecting the outcome of dcryocystorhinostomy (DCR).The study included the results of dacryocystography in 73 patients after DCR: 37 cases of failed DCR and 36 cases of successful DCR. Biometric characteristics of the formed ostium were evaluated: the horizontal size of the bony “window” and the soft tissue part of the ostium, the vertical size of the bony “window” and soft tissue ostium, the height of the fragment of the remaining bone above and below the line of the common canaliculus, and the height of the “pocket” formed below the lower edge of the ostium. Statistical analysis was performed using parametric and non-parametric statistical methods. Differences were considered significant at p ≤ 0.05.Intergroup differences were identified in the values of the maximum horizontal size of the bony “window” (p = 0.015), the maximum horizontal size of the soft tissue “window” (p < 0.001), the maximum vertical size of the soft tissue “window” (p < 0.001), and the height of the fragment of the remaining bone below the level of the common canaliculus to the edge of the formed ostium (p = 0.004).The stage of forming the bony “window” influences the success of DCR. Not only the position of the “window” is important, but also the geometric properties of the formed ostium. [ABSTRACT FROM AUTHOR]

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors.

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors.

Various semiparametric regression models have recently been proposed for the analysis of gap times between consecutive recurrent events. Among them, the semiparametric accelerated failure time (AFT) model is especially appealing owing to its direct interpretation of covariate effects on the gap times. In general, estimation of the semiparametric AFT model is challenging because the rank-based estimating function is a nonsmooth step function. As a result, solutions to the estimating equations do not necessarily exist. Moreover, the popular resampling-based variance estimation for the AFT model requires solving rank-based estimating equations repeatedly and hence can be computationally cumbersome and unstable. In this paper, we extend the induced smoothing approach to the AFT model for recurrent gap time data. Our proposed smooth estimating function permits the application of standard numerical methods for both the regression coefficients estimation and the standard error estimation. Large-sample properties and an asymptotic variance estimator are provided for the proposed method. Simulation studies show that the proposed method outperforms the existing nonsmooth rank-based estimating function methods in both point estimation and variance estimation. The proposed method is applied to the data analysis of repeated hospitalizations for patients in the Danish Psychiatric Center Register.

Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where 2 types of events such as hospital admission and discharge occur alternately over time. The 2 alternating states defined by these recurrent events could each carry important and distinct information about a patient's underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the 2 alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution. Moreover, the estimation procedure, which is based on smooth estimating equations, not only properly addresses challenges such as induced dependent censoring and intercept sampling bias commonly confronted in serial event gap time data but also is more computationally tractable than the existing rank-based methods. The proposed methods are evaluated by simulation studies and illustrated by analyzing psychiatric contacts from the South Verona Psychiatric Case Register.

Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central "hill" of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments.

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identification. Under the proposed framework, we may also estimate the magnitude of the interaction, which leads to the construction of scoring system measuring the individualized treatment effect. The proposed methods are quite flexible and include many recently proposed estimators as special cases. As a result, some estimators originally proposed for randomized clinical trials can be extended to observational studies, and procedures based on the weighting method can be converted to an A-learning method and vice versa. Our approaches also allow straightforward incorporation of regularization methods for high-dimensional data, as well as possible efficiency augmentation and generalization to multiple treatments. We examine the empirical performance of several procedures belonging to the proposed framework through extensive numerical studies.

Handheld mobile devices store a plethora of sensitive data, such as private emails, personal messages, photos, and location data. Authentication is essential to protect access to sensitive data. However, the majority of mobile devices are currently secured by singlemodal authentication schemes which are vulnerable to shoulder surfing, smudge attacks, and thermal attacks. While some authentication schemes protect against one of these attacks, only few schemes address all three of them. We propose multimodal authentication where touch and gaze input are combined to resist shoulder surfing, as well as smudge and thermal attacks. Based on a series of previously published works where we studied the usability of several user-centred multimodal authentication designs and their security against multiple threat models, we provide a comprehensive overview of multimodal authentication on handheld mobile devices. We further present guidelines on how to leverage multiple input modalities for enhancing the usability and security of user authentication on mobile devices. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify growth-restricted fetuses using biometric parameters and to assess the validity and clinical value of individual ultrasound parameters and ratios, such as transcerebellar diameter/abdominal circumference (TCD/AC), head circumference/abdominal circumference (HC/AC), and femur length/abdominal circumference (FL/AC). In a retrospective single-center cross-sectional study, the biometric data of 9292 pregnancies between the 15th and 42nd weeks of gestation were acquired. Statistical analysis included descriptive data, quantile regression estimating the 10th and 90th percentiles, and multivariable analysis. We obtained clinically noticeable results in predicting small-for-gestational-age (SGA) and fetal growth restriction (FGR) fetuses at advanced weeks of gestation using the AC with a Youden index of 0.81 and 0.96, respectively. The other individual parameters and quotients were less suited to identifying cases of SGA and FGR. The multivariable analysis demonstrated the best results for identifying SGA and FGR fetuses with an area under the curve of 0.95 and 0.96, respectively. The individual ultrasound parameters were better suited to identifying SGA and FGR than the ratios. Amongst these, the AC was the most promising individual parameter, especially at advanced weeks of gestation. However, the highest accuracy was achieved with a multivariable model. [ABSTRACT FROM AUTHOR]

Successful implementation of a scientific study and correct analysis of data obtained is possible with advanced biostatistics knowledge. The aim of this study is to find out efficacy of basic biostatistics program given to medical faculty students and to evaluate students' biostatistics knowledge, attitude and behaviour levels. Medical Faculty students in a Turkish university participated in this study. 123 of the respondents (52.6%) were male and 111 (47.4%) were female, with an average age of 20.2 ± 1.7 years. The survey used included items questioning demographic information, biostatistics knowledge, attitude and behaviours of students and 10 multiple choice questions including the subjects learned during the program. The students filled in this survey before and after training and data obtained were evaluated. Students' positive responses to having biostatistics basic knowledge were 68.0% before training and 95.7% after training. The frequency of knowing the purpose of biostatistics was 81.5% before training and 96.6% after training. While the rate of positive response was 60.9% for population and sample, 63.2% for basic principles in summarizing data, 54.7% for central tendency-location measurements, 51.5% for variability measurements before training, they were found as 95% and higher after training. Positive responses of 70.8% for hypothesis and error types, 48.7% for statistical assumptions, 36.5% for parametric hypothesis tests, 33.0% for nonparametric hypothesis tests and 27.4% for statistical package programs before training were 93.6% and higher after training. Total score obtained from responses to multiple choice questions was 2.5±1.4 before training and 7.5±2.1 after training, which was statistically significant (p<0.001). In this study, biostatistics knowledge, attitudes and behaviours of medical faculty students were evaluated. Biostatistics training needs changes due to latest developments in information technology. Many medical faculties currently teach basic biostatistics concepts and carry out biostatistics training studies to allow critical evaluation during the process. [ABSTRACT FROM AUTHOR]