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Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroendocrine neoplasms (NENs) are occurring more frequently worldwide. Data from the UK cancer database (National Cancer Registration and Analysis Service (NCRAS)) showed that female patients have better survival with neuroendocrine neoplasms. This study used the U.S. cancer database (Surveillance, Epidemiology, and End Results Program (SEER)) to validate and compare these findings. Sixty-months survival for NENs were calculated for both male and female patients from NCRAS and SEER. The findings from NCRAS were confirmed by the findings from SEER that females survive more than males with NENs, mainly with lung and stomach NENs. The reason behind this is unclear and remains unexplained. Background: Neuroendocrine neoplasms (NENs) are increasing in incidence globally. Previous analysis of the UK cancer database (National Cancer Registration and Analysis Service (NCRAS)) showed a notable female survival advantage in most tumour sites. This study aims to compare NCRAS to the Surveillance, Epidemiology, and End Results Program (SEER) to validate these results using the same statistical methods. Methods: A total of 14,834 and 108,399 patients with NENs were extracted from NCRAS and SEER, respectively. Sixty-months survival for both males and females for each anatomical site of NENs were calculated using restricted mean survival time (RMST) and Kaplan–Meier Survival estimates. The sixty-month RMST female survival advantage (FSA) was calculated. Results: FSA was similar in NCRAS and SEER. The highest FSA occurred in lung and stomach NENs. Conclusions: The data from SEER confirm the findings published by NCRAS. Female survival advantage remains unexplained. [ABSTRACT FROM AUTHOR]

Deep learning has recently demonstrated the ability to predict long-term patient risk and its stratification when trained on imaging data such as chest radiographs. However, existing methods formulate estimating patient risk as a binary classification, typically ignoring or limiting the use of temporal information, and not accounting for the loss of patient follow-up, which reduces the fidelity of estimation and limits the prediction to a certain time horizon. In this paper, we demonstrate that deep survival and time-to-event prediction models can outperform binary classifiers at predicting mortality and risk of adverse health events. In our study, deep survival models were trained to predict risk scores from chest radiographs and patient demographic information in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial (25,433 patient data points used in this paper) for 2-, 5-, and 10-year time horizons. Binary classification models that predict mortality at these time horizons were built as baselines. Compared to the considered alternative, deep survival models improve the Brier score (5-year: 0.0455 [95% CI, 0.0427–0.0482] vs. 0.0555 [95% CI, (0.0535–0.0575)], p < 0.05) and expected calibration error (ECE) (5-year: 0.0110 [95% CI, 0.0080–0.0141] vs. 0.0747 [95% CI, 0.0718–0.0776], p < 0.05) for those fixed time horizons and are able to generate predictions for any time horizon, without the need to retrain the models. Our study suggests that deep survival analysis tools can outperform binary classification in terms of both discriminative performance and calibration, offering a potentially plausible solution for forecasting risk in clinical practice. [ABSTRACT FROM AUTHOR]

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine. [ABSTRACT FROM AUTHOR]

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models—specifically CoxPH, RSF, and DeepHit NN—were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models. [ABSTRACT FROM AUTHOR]

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate's probability of HCV infection during the trial. The decision to recruit considers both the candidate's predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity. [ABSTRACT FROM AUTHOR]

Background: Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival. Methods: We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender. Results: We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality. Discussion: This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times. [ABSTRACT FROM AUTHOR]

Motivation Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes. Results We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally. Availability and implementation A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics. [ABSTRACT FROM AUTHOR]

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies. [ABSTRACT FROM AUTHOR]

Sparse modeling plays a ubiquitous role in modern statistical regression. In particular, high-dimensional survival analysis has drawn a lot of attention as a result of the popularity of microarray studies involving survival data. In this paper, we focus on a scenario where predictors are strongly correlated, also known as grouping effect, which is highly desirable when analysing high-dimensional microarray data. To perform simultaneous variable selection and estimation under this circumstance, we propose the l 2 -regularized best-subsets estimator under the framework of additive hazards models based on a polynomial algorithm for the best subset selection. Moreover, we establish comprehensive statistical properties, including oracle inequalities under estimation loss for the proposed estimator. The proposed method is demonstrated by simulation studies and illustrated by a real data example. [ABSTRACT FROM AUTHOR]

Background: In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored. Results: We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways. Conclusions: Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research. [ABSTRACT FROM AUTHOR]

In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)-type variable selection method, which considers an inverse weighting for of the squared losses, and an information criterion-type variable selection method, which applies an inverse weighting of the survival probability to the power of each density function in the likelihood function. We prove the consistency of the inverse probability of censoring weighted lasso estimator and the maximum inverse probability of censoring weighted likelihood estimator. The performance of the inverse probability of censoring weighted lasso and inverse probability of censoring weighted information criterion are evaluated via a simulation study with six scenarios, and then their variable selection ability is demonstrated using data from two clinical studies. The results confirm that inverse probability of censoring weighted lasso and the inverse probability of censoring weighted likelihood function produce good estimation accuracy and consistent variable selection. We conclude that our two proposed methods are useful variable selection tools for adjusting the censoring information for survival time analyses. [ABSTRACT FROM AUTHOR]

Contemporary works in change-point survival models mainly focus on an unknown universal change-point shared by the whole study population. However, in some situations, the change-point is plausibly individual-specific, such as when it corresponds to the telomere length or menopausal age. Also, maximum-likelihood-based inference for the fixed change-point parameter is notoriously complicated. The asymptotic distribution of the maximum-likelihood estimator is non-standard, and computationally intensive bootstrap techniques are commonly used to retrieve its sampling distribution. This article is motivated by a breast cancer study, where the disease-free survival time of the patients is postulated to be regulated by the menopausal age, which is unobserved. As menopausal age varies across patients, a fixed change-point survival model may be inadequate. Therefore, we propose a novel proportional hazards model with a random change-point. We develop a nonparametric maximum-likelihood estimation approach and devise a stable expectation–maximization algorithm to compute the estimators. Because the model is regular, we employ conventional likelihood theory for inference based on the asymptotic normality of the Euclidean parameter estimators, and the variance of the asymptotic distribution can be consistently estimated by a profile-likelihood approach. A simulation study demonstrates the satisfactory finite-sample performance of the proposed methods, which yield small bias and proper coverage probabilities. The methods are applied to the motivating breast cancer study. [ABSTRACT FROM AUTHOR]

We examined the role of receptor profiles and other prognostic factors in survival outcomes after stereotactic radiosurgery (SRS) for brain metastases in breast cancer patients, to help improve selection of candidates for SRS. We included 149 consecutive patients who received SRS between 2012 and 2019 at the University Hospital of Copenhagen, Rigshospitalet, Denmark. Overall survival (OS) following SRS was determined through the Kaplan–Meier method, while CNS progression-free survival (CNS-PFS) was determined through competing risk analysis. Prognostic factors for both OS and CNS-PFS were evaluated through uni- and multivariate Cox regression and Fine-Gray models, respectively. The proportional hazards assumptions were tested through Schoenfeld residuals, and non-proportionality was accounted for by the inclusion of time-dependent variables. Median OS was 14.8 months for the entire cohort and was as follows for the four receptor profiles: 33.3 months for ER+/HER2+ (ER: estrogen receptor, HER2: human epidermal growth factor receptor 2), 11.0 months for ER+/HER2−, 17.7 months for ER−/HER2+, and 5.3 months for ER−/HER2−. In the multivariate model, the ER−/HER2− receptor profile (hazard ratio (HR): 2.00, 95% confidence interval (CI): 1.09–3.67) and the presence of extracranial visceral metastases (HR: 2.90, 95% CI: 1.53–5.50) were associated with worse OS. The ER+/HER2+ receptor profile (HR: 0.43, 95% CI: 0.19–0.96) and 5+ lines of treatment (HR: 0.40, 95% CI: 0.20–0.82) were both associated with improved OS. For CNS-PFS, 5+ lines of treatment (sub-distributional hazard ratio (SHR): 2.88, 95% CI: 1.06–7.81) was associated with worse CNS-PFS, while extracranial visceral metastases (SHR: 0.54, 95% CI: 0.30–0.97) was associated with reduced risk of CNS progression – which is primarily due to patients with extracranial metastases dying before developing new CNS progression. Extracranial visceral disease and the ER−/HER2− receptor profile were associated with poor survival outcomes following SRS. [ABSTRACT FROM AUTHOR]

Survival analysis models allow for predicting the probability of an event over time. The specificity of the survival analysis data includes the distribution of events over time and the proportion of classes. Late events are often rare and do not correspond to the main distribution and strongly affect the quality of the models and quality assessment. In this paper, we identify four cases of excessive sensitivity of survival analysis metrics and propose methods to overcome them. To set the equality of observation impacts, we adjust the weights of events based on target time and censoring indicator. According to the sensitivity of metrics, A U P R C (area under Precision-Recall curve) is best suited for assessing the quality of survival models, and other metrics are used as loss functions. To evaluate the influence of the loss function, the B a g g i n g model uses ones to select the size and hyperparameters of the ensemble. The experimental study included eight real medical datasets. The proposed modifications of I B S (Integrated Brier Score) improved the quality of B a g g i n g compared to the classical loss functions. In addition, in seven out of eight datasets, the B a g g i n g with new loss functions outperforms the existing models of the scikit-survival library. [ABSTRACT FROM AUTHOR]

The claudin multigene family is associated with various aberrant physiological and cellular signaling pathways. However, the association of claudins with survival prognosis, signaling pathways, and diagnostic efficacy in colon cancer remains poorly understood. Methods: Through the effective utilization of various bioinformatics methods, including differential gene expression analysis, gene set enrichment analysis protein-protein interaction (PPI) network analysis, survival analysis, single sample gene set enrichment analysis (ssGSEA), mutational variance analysis, and identifying receiver operating characteristic curve of claudins in The Cancer Genome Atlas colon adenocarcinoma (COAD). Results: We found that: CLDN2, CLDN1, CLDN14, CLDN16, CLDN18, CLDN9, CLDN12, and CLDN6 are elevated in COAD. In contrast, the CLDN8, CLDN23, CLDN5, CLDN11, CLDN7, and CLDN15 are downregulated in COAD. By analyzing the public datasets GSE15781 and GSE50760 from NCBI-GEO (https://www.ncbi. nlm.nih.gov/geo/), we have confirmed that CLDN1, CLDN2, and CLDN14 are significantly upregulated and CLDN8 and CLDN23 are significantly downregulated in normal colon, colon adenocarcinoma tumor, and liver metastasis of colon adenocarcinoma tissues from human samples. Various claudins are mutated and found to be associated with diagnostic efficacy in COAD. Conclusion: The claudin gene family is associated with prognosis, immune regulation, signaling pathway regulations, and diagnosis of COAD. These findings may provide new molecular insight into claudins in the treatment of colon cancer. [ABSTRACT FROM AUTHOR]

For survival analysis in comparative coronavirus disease 2019 trials, the routinely used hazard ratio may not provide a meaningful summary of the treatment effect. The mean survival time difference/ratio is an intuitive, assumption-free alternative. However, for short-term studies, landmark mortality rate differences/ratios are more clinically relevant and should be formally analyzed and reported. [ABSTRACT FROM AUTHOR]

Background: Many studies analyze sexual and reproductive event data using descriptive life tables. Survival analysis has better power to estimate factors associated with age at first sex (AFS), but proportional hazards models may not be right model to use. This study used accelerated failure time (AFT) models, restricted Mean Survival time model (RMST) models, with semi and non-parametric methods to assess age at first sex (AFS), factors associated with AFS, and verify underlying assumptions for each analysis. Methods: Self-reported sexual debut data was used from respondents 15–24 years in eight cross-sectional surveys between 1994–2016, and from adolescents' survey in an observational community study (2019–2020) in northwest Tanzania. Median AFS was estimated in each survey using non-parametric and parametric models. Cox regression, AFT parametric models (exponential, gamma, generalized gamma, Gompertz, Weibull, log-normal and log-logistic), and RMST were used to estimate and identify factors associated with AFS. The models were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC), where lower values represent a better model fit. Results: The results showed that in every survey, the Cox regression model had higher AIC and BIC compared to the other models. Overall, AFT had the best fit in every survey round. The estimated median AFS using the parametric and non-parametric methods were close. In the adolescent survey, log-logistic AFT showed that females and those attending secondary and higher education level had a longer time to first sex (Time ratio (TR) = 1.03; 95% CI: 1.01–1.06, TR = 1.05; 95% CI: 1.02–1.08, respectively) compared to males and those who reported not being in school. Cell phone ownership (TR = 0.94, 95% CI: 0.91–0.96), alcohol consumption (TR = 0.88; 95% CI: 0.84–0.93), and employed adolescents (TR = 0.95, 95% CI: 0.92–0.98) shortened time to first sex. Conclusion: The AFT model is better than Cox PH model in estimating AFS among the young population. [ABSTRACT FROM AUTHOR]

In the context of time‐to‐event analysis, a primary objective is to model the risk of experiencing a particular event in relation to a set of observed predictors. The Concordance Index (C‐Index) is a statistic frequently used in practice to assess how well such models discriminate between various risk levels in a population. However, the properties of conventional C‐Index estimators when applied to left‐truncated time‐to‐event data have not been well studied, despite the fact that left‐truncation is commonly encountered in observational studies. We show that the limiting values of the conventional C‐Index estimators depend on the underlying distribution of truncation times, which is similar to the situation with right‐censoring as discussed in Uno et al. (2011) [On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30(10), 1105–1117]. We develop a new C‐Index estimator based on inverse probability weighting (IPW) that corrects for this limitation, and we generalize this estimator to settings with left‐truncated and right‐censored data. The proposed IPW estimators are highly robust to the underlying truncation distribution and often outperform the conventional methods in terms of bias, mean squared error, and coverage probability. We apply these estimators to evaluate a predictive survival model for mortality among patients with end‐stage renal disease. [ABSTRACT FROM AUTHOR]

Aims To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value. Methods and results Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06–1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19–1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001). Conclusion Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint blockades (ICBs) therapy has produced durable clinical responses in many cancer types, but only a fraction of patients can benefit from ICB treatment. Previous studies have reported multiple transcriptomic biomarkers to predict ICB responses and improve treatment precision in various cancer types. However, a timely and unbiased assessment of these biomarkers has yet to be conducted due to the lack of large-scale uniformly curated ICB-treated datasets. To address the needs, we developed ICB-Portal, a comprehensive resource about ICB including RNA-seq data of 29 datasets from public sources and standardized metadata of each study through a uniform pre-processing, 48 biomarker scores associated with ICB response, results of a systematic benchmark analysis evaluating the efficacy, and generalization ability for each biomarker in various scenarios such as different cancer types, anti-bodies, biopsy time, and combinatory treatments with other drugs by a standardized bioinformatics workflow and an online benchmark platform. Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research. [ABSTRACT FROM AUTHOR]

The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan–Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards, a log-rank test is commonly performed to investigate whether there is a difference in survival or, depending on additional covariates, a Cox proportional hazard model is used. However, in numerous trials these approaches fail due to the presence of non-proportional hazards, resulting in difficulties of interpreting the hazard ratio and a loss of power. When considering equivalence or non-inferiority trials, the commonly performed log-rank based tests are similarly affected by a violation of this assumption. Here we propose a parametric framework to assess equivalence or non-inferiority for survival data. We derive pointwise confidence bands for both, the hazard ratio and the difference of the survival curves. Further we propose a test procedure addressing non-inferiority and equivalence by directly comparing the survival functions at certain time points or over an entire range of time. Once the model's suitability is proven the method provides a noticeable power benefit, irrespectively of the shape of the hazard ratio. On the other hand, model selection should be carried out carefully as misspecification may cause type I error inflation in some situations. We investigate the robustness and demonstrate the advantages and disadvantages of the proposed methods by means of a simulation study. Finally, we demonstrate the validity of the methods by a clinical trial example. [ABSTRACT FROM AUTHOR]

Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. However, use of EHR data is hindered by a lack of direct event time observations. Occurrence times of relevant diagnostic codes or target disease mentions in clinical notes are at best a good approximation of the true disease onset time. On the other hand, extracting precise information on the exact event time requires laborious manual chart review and is sometimes altogether infeasible due to a lack of detailed documentation. Current status labels—binary indicators of phenotype status during follow-up—are significantly more efficient and feasible to compile, enabling more precise survival curve estimation given limited resources. Existing survival analysis methods using current status labels focus almost entirely on supervised estimation, and naive incorporation of unlabeled data into these methods may lead to biased estimates. In this article, we propose Semisupervised Calibration of Risk with Noisy Event Times (SCORNET), which yields a consistent and efficient survival function estimator by leveraging a small set of current status labels and a large set of informative features. In addition to providing theoretical justification of SCORNET, we demonstrate in both simulation and real-world EHR settings that SCORNET achieves efficiency akin to the parametric Weibull regression model, while also exhibiting semi-nonparametric flexibility and relatively low empirical bias in a variety of generative settings. [ABSTRACT FROM AUTHOR]

Prognostic models are useful tools for assessing a patient's risk of experiencing adverse health events. In practice, these models must be validated before implementation to ensure that they are clinically useful. The concordance index (C‐Index) is a popular statistic that is used for model validation, and it is often applied to models with binary or survival outcome variables. In this paper, we summarize existing criticism of the C‐Index and show that many limitations are accentuated when applied to survival outcomes, and to continuous outcomes more generally. We present several examples that show the challenges in achieving high concordance with survival outcomes, and we argue that the C‐Index is often not clinically meaningful in this setting. We derive a relationship between the concordance probability and the coefficient of determination under an ordinary least squares model with normally distributed predictors, which highlights the limitations of the C‐Index for continuous outcomes. Finally, we recommend existing alternatives that more closely align with common uses of survival models. [ABSTRACT FROM AUTHOR]

The restricted mean survival time (RMST), which evaluates the expected survival time up to a pre-specified time point τ , has been widely used to summarize the survival distribution due to its robustness and straightforward interpretation. In comparative studies with time-to-event data, the RMST-based test has been utilized as an alternative to the classic log-rank test because the power of the log-rank test deteriorates when the proportional hazards assumption is violated. To overcome the challenge of selecting an appropriate time point τ , we develop an RMST-based omnibus Wald test to detect the survival difference between two groups throughout the study follow-up period. Treating a vector of RMSTs at multiple quantile-based time points as a statistical functional, we construct a Wald χ 2 test statistic and derive its asymptotic distribution using the influence function. We further propose a new procedure based on the influence function to estimate the asymptotic covariance matrix in contrast to the usual bootstrap method. Simulations under different scenarios validate the size of our RMST-based omnibus test and demonstrate its advantage over the existing tests in power, especially when the true survival functions cross within the study follow-up period. For illustration, the proposed test is applied to two real datasets, which demonstrate its power and applicability in various situations. [ABSTRACT FROM AUTHOR]

The restricted mean survival time (RMST) evaluates the expectation of survival time truncated by a prespecified time point, because the mean survival time in the presence of censoring is typically not estimable. The frequentist inference procedure for RMST has been widely advocated for comparison of two survival curves, while research from the Bayesian perspective is rather limited. For the RMST of both right‐ and interval‐censored data, we propose Bayesian nonparametric estimation and inference procedures. By assigning a mixture of Dirichlet processes (MDP) prior to the distribution function, we can estimate the posterior distribution of RMST. We also explore another Bayesian nonparametric approach using the Dirichlet process mixture model and make comparisons with the frequentist nonparametric method. Simulation studies demonstrate that the Bayesian nonparametric RMST under diffuse MDP priors leads to robust estimation and under informative priors it can incorporate prior knowledge into the nonparametric estimator. Analysis of real trial examples demonstrates the flexibility and interpretability of the Bayesian nonparametric RMST for both right‐ and interval‐censored data. [ABSTRACT FROM AUTHOR]

Oesophageal adenocarcinoma is a poor prognosis cancer and the molecular features underpinning response to treatment remain unclear. We investigate whole genome, transcriptomic and methylation data from 115 oesophageal adenocarcinoma patients mostly from the DOCTOR phase II clinical trial (Australian New Zealand Clinical Trials Registry-ACTRN12609000665235), with exploratory analysis pre-specified in the study protocol of the trial. We report genomic features associated with poorer overall survival, such as the APOBEC mutational and RS3-like rearrangement signatures. We also show that positron emission tomography non-responders have more sub-clonal genomic copy number alterations. Transcriptomic analysis categorises patients into four immune clusters correlated with survival. The immune suppressed cluster is associated with worse survival, enriched with myeloid-derived cells, and an epithelial-mesenchymal transition signature. The immune hot cluster is associated with better survival, enriched with lymphocytes, myeloid-derived cells, and an immune signature including CCL5, CD8A, and NKG7. The immune clusters highlight patients who may respond to immunotherapy and thus may guide future clinical trials. It remains critical to understand the genomic events in response to treatment of oesophageal adenocarcinoma (OAC). Here, the authors perform a multi-omics analysis of OAC patients from the DOCTOR phase II clinical trial, finding genomic features and immune clusters associated with survival. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancers in the urinary bladder and kidney are common in men and rarer in women. Cigarette smoking is a shared risk factor for both of these cancers. Some 50 years ago, survival in these cancers was low, and it was worse for kidney than bladder cancer. In the present study, we could show improvement in survival for these cancers in the Nordic countries, and similar improvements have also been observed in other countries. Kidney cancer survival improved remarkably well, as 50 years ago, only 20–30% of the patients survived 5 years, but currently, some 75% survive 5 years. In male bladder cancer, 5-year survival is still somewhat better than survival in kidney cancer, but female kidney cancer survival has caught up with that of bladder cancer. The reasons for this positive development for both of these cancers is earlier diagnosis as patients with blood in urine are readily taken for examinations. Additionally, treatment has become more efficient, and continuously new medications are being introduced. Kidney and bladder cancers share etiology and relatively good recent survival, but long-term studies are rare. We analyzed survival for these cancers in Denmark, Finland, Norway (NO), and Sweden (SE) over a 50-year period (1971–2020). Relative 1- and 5-year survival data were obtained from the NORDCAN database, and we additionally calculated conditional 5/1-year survival. In 2016–2020, 5-year survivals for male kidney (79.0%) and bladder (81.6%) cancers were best in SE. For female kidney cancer, NO survival reached 80.0%, and for bladder cancer, SE survival reached 76.1%. The magnitude of 5-year survival improvements during the 50-year period in kidney cancer was over 40% units; for bladder cancer, the improvement was over 20% units. Survival in bladder cancer was worse for women than for men, particularly in year 1. In both cancers, deaths in the first year were approximately as many as in the subsequent 4 years. We could document an impressive development for kidney cancer with tripled male and doubled female 5-year survival in 50 years. Additionally, for bladder cancer, a steady improvement was recorded. The current challenges are to curb early mortality and target treatment to reduce long-term mortality. [ABSTRACT FROM AUTHOR]

Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time‐to‐event outcomes. In non‐randomized studies, unadjusted estimates can lead to biased depictions due to confounding. Multiple methods to adjust survival curves for confounders exist. However, it is currently unclear which method is the most appropriate in which situation. Our goal is to compare forms of inverse probability of treatment weighting, the G‐Formula, propensity score matching, empirical likelihood estimation and augmented estimators as well as their pseudo‐values based counterparts in different scenarios with a focus on their bias and goodness‐of‐fit. We provide a short review of all methods and illustrate their usage by contrasting the survival of smokers and non‐smokers, using data from the German Epidemiological Trial on Ankle‐Brachial‐Index. Subsequently, we compare the methods using a Monte‐Carlo simulation. We consider scenarios in which correctly or incorrectly specified models for describing the treatment assignment and the time‐to‐event outcome are used with varying sample sizes. The bias and goodness‐of‐fit is determined by taking the entire survival curve into account. When used properly, all methods showed no systematic bias in medium to large samples. Cox regression based methods, however, showed systematic bias in small samples. The goodness‐of‐fit varied greatly between different methods and scenarios. Methods utilizing an outcome model were more efficient than other techniques, while augmented estimators using an additional treatment assignment model were unbiased when either model was correct with a goodness‐of‐fit comparable to other methods. These "doubly‐robust" methods have important advantages in every considered scenario. [ABSTRACT FROM AUTHOR]

Background: The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint. Results: To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect. Conclusions: Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Background: Treatment effect is typically summarized in terms of relative risk reduction or number needed to treat ("conventional effect summary"). Restricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in event‐free days. Older adults' preference between the two effect summary measures has not been studied. Methods: We conducted a mixed methods study using a quantitative survey and qualitative semi‐structured interviews. For the survey, we enrolled 102 residents with hypertension at five senior housing facilities (mean age 81.3 years, 82 female, 95 white race). We randomly assigned respondents to either RMST‐based (n = 49) or conventional decision aid (n = 53) about the benefits and harms of intensive versus standard blood pressure‐lowering strategies and compared decision conflict scale (DCS) responses (range: 0 [no conflict] to 100 [maximum conflict]; <25 is associated with implementing decisions). We used a purposive sample of 23 survey respondents stratified by both their random assignment and DCS from the survey. Inductive qualitative thematic analysis explored complementary perspectives on preferred ways of summarizing treatment effects. Results: The mean (standard deviation) total DCS was 22.0 (14.3) for the conventional decision aid group and 16.7 (14.1) for the RMST‐based decision aid group (p = 0.06), but the proportion of participants with a DCS <25 was higher in the RMST‐based group (26 [49.1%] vs 34 [69.4%]; p = 0.04). Qualitative interviews suggested that, regardless of effect summary measure, older individuals' preference depended on their ability to clearly comprehend quantitative information, clarity of presentation in the visual aid, and inclusion of desired information. Conclusions: When choosing a blood pressure‐lowering strategy, older adults' perceived uncertainty may be reduced with a time‐based effect summary, although our study was underpowered to detect a statistically significant difference. Given highly variable individual preferences, it may be useful to present both conventional and RMST‐based information in decision aids. [ABSTRACT FROM AUTHOR]

Background: Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability. Methods: We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models. Results: In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86–0.87 at 5 years, 0.79–0.81 at 10 years) than using baseline or last observed CS data (0.80–0.86 at 5 years, 0.73–0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering. Conclusion: Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods. Trial registration: ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000. [ABSTRACT FROM AUTHOR]

In a clinical trial with a time‐to‐event endpoint the treatment effect can be measured in various ways. Under proportional hazards all reasonable measures (such as the hazard ratio and the difference in restricted mean survival time) are consistent in the following sense: Take any control group survival distribution such that the hazard rate remains above zero; if there is no benefit by any measure there is no benefit by all measures, and as the magnitude of treatment benefit increases by any measure it increases by all measures. Under nonproportional hazards, however, survival curves can cross, and the direction of the effect for any pair of measures can be inconsistent. In this paper we critically evaluate a variety of treatment effect measures in common use and identify flaws with them. In particular, we demonstrate that a treatment's benefit has two distinct and independent dimensions which can be measured by the difference in the survival rate at the end of follow‐up and the difference in restricted mean survival time, and that commonly used measures do not adequately capture both dimensions. We demonstrate that a generalized hazard difference, which can be estimated by the difference in exposure‐adjusted subject incidence rates, captures both dimensions, and that its inverse, the number of patient‐years of follow‐up that results in one fewer event (the NYNT), is an easily interpretable measure of the magnitude of clinical benefit. [ABSTRACT FROM AUTHOR]

Simple Summary: Short, dysfunctional telomeres represent the genetic biomarkers of cancer. Studies in early-stage non-small-cell lung cancer (NSCLC) have shown that telomere length and telomerase levels are correlated with survival. In patients with advanced NSCLC, telomere status has not yet been investigated, and its clinical significance remains unknown. We studied telomere length and the expression of telomerase and shelterin genes in a cohort of 79 patients with advanced NSCLC, and evaluated these parameters as potential prognostic and predictive factors. Telomere shortening, high levels of telomerase and aberrant expression of shelterin genes TRF2, RAP1 and TIN2 were significantly correlated with shorter survival. Furthermore, a worse response to immunotherapy was observed in patients with shorter telomeres. The determination of telomere parameters in advanced NSCLC could be useful for individualized treatment decisions. Telomere length appears to correlate with survival in early non-small-cell lung cancer (NSCLC), but the prognostic impact of telomere status in advanced NSCLC remains undetermined. Our purpose was to evaluate telomere parameters as prognostic and predictive biomarkers in advanced NSCLC. In 79 biopsies obtained before treatment, we analyzed the telomere length and expression of TERT and shelterin complex genes (TRF1, TRF2, POT1, TPP1, RAP1, and TIN2), using quantitative PCR. Non-responders to first-line chemotherapy were characterized by shorter telomeres and low RAP1 expression (p = 0.0035 and p = 0.0069), and tended to show higher TERT levels (p = 0.058). In multivariate analysis, short telomeres were associated with reduced event-free (EFS, p = 0.0023) and overall survival (OS, p = 0.00041). TERT and TRF2 overexpression correlated with poor EFS (p = 0.0069 and p = 0.00041) and OS (p = 0.0051 and p = 0.007). Low RAP1 and TIN2 expression-levels were linked to reduced EFS (p = 0.00032 and p = 0.0069) and OS (p = 0.000051 and p = 0.02). Short telomeres were also associated with decreased survival after nivolumab therapy (p = 0.097). Evaluation of telomere status in advanced NSCLC emerges as a useful biomarker that allows for the selection of patient groups with different clinical evolutions, to establish personalized treatment. [ABSTRACT FROM AUTHOR]

Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset maintenance, lowering operating costs, and enabling the expansion of renewable energy sources. Most fault prognosis models proposed thus far for hydroelectric generating units are based on signal decomposition and regression models. In the specific case of SHPs, there is a high occurrence of data being censored, since the operation is not consistently steady and can be repeatedly interrupted due to transmission problems or scarcity of water resources. To overcome this, we propose a two-step, data-driven framework for SHP prognosis based on time series feature engineering and survival modeling. We compared two different strategies for feature engineering: one using higher-order statistics and the other using the Tsfresh algorithm. We adjusted three machine learning survival models—CoxNet, survival random forests, and gradient boosting survival analysis—for estimating the concordance index of these approaches. The best model presented a significant concordance index of 77.44%. We further investigated and discussed the importance of the monitored sensors and the feature extraction aggregations. The kurtosis and variance were the most relevant aggregations in the higher-order statistics domain, while the fast Fourier transform and continuous wavelet transform were the most frequent transformations when using Tsfresh. The most important sensors were related to the temperature at several points, such as the bearing generator, oil hydraulic unit, and turbine radial bushing. [ABSTRACT FROM AUTHOR]

Background and objective: The prognostic value of age and other non-hematological factors in predicting outcomes in older patients with newly diagnosed acute myeloid leukemia (AML) undergoing antileukemic therapy is not well understood. We performed a systematic review to determine the association between these factors and mortality and health-related quality of life or fatigue among these patients. Methods: We searched Medline and Embase through October 2021 for studies in which researchers quantified the relationship between age, comorbidities, frailty, performance status, or functional status; and mortality and health-related quality of life or fatigue in older patients with AML receiving antileukemic therapy. We assessed the risk of bias of the included studies using the Quality in Prognostic Studies tool, conducted random-effects meta-analyses, and assessed the quality of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation approach. Results: We included 90 studies. Meta-analysis showed that age (per 5-year increase, HR 1.16 95% CI 1.11–1.21, high-quality evidence), comorbidities (Hematopoietic Cell Transplantation-specific Comorbidity Index: 3+ VS less than 3, HR 1.60 95% CI 1.31–1.95, high-quality evidence), and performance status (Eastern Cooperative Oncology Group/ World Health Organization (ECOG/WHO): 2+ VS less than 2, HR 1.63 95% CI 1.43–1.86, high-quality evidence; ECOG/WHO: 3+ VS less than 3, HR 2.00 95% CI 1.52–2.63, moderate-quality evidence) were associated with long-term mortality. These studies provided inconsistent and non-informative results on short-term mortality (within 90 days) and quality of life. Conclusion: High-quality or moderate-quality evidence support that age, comorbidities, performance status predicts the long-term prognosis of older patients with AML undergoing antileukemic treatment. [ABSTRACT FROM AUTHOR]

In clinical studies or trials comparing survival times between two treatment groups, the restricted mean lifetime (RML), defined as the expectation of the survival from time 0 to a prespecified time‐point, is often the quantity of interest that is readily interpretable to clinicians without any modeling restrictions. It is well known that if the treatments are not randomized (as in observational studies), covariate adjustment is necessary to account for treatment imbalances due to confounding factors. In this article, we propose a simple doubly‐robust pseudo‐value approach to effectively estimate the difference in the RML between two groups (akin to a metric for estimating average causal effects), while accounting for confounders. The proposed method combines two general approaches: (a) group‐specific regression models for the time‐to‐event and covariate information, and (b) inverse probability of treatment assignment weights, where the RMLs are replaced by the corresponding pseudo‐observations for survival outcomes, thereby mitigating the estimation complexities in presence of censoring. The proposed estimator is double‐robust, in the sense that it is consistent if at least one of the two working models remains correct. In addition, we explore the potential of available machine learning algorithms in causal inference to reduce possible bias of the causal estimates in presence of a complex association between the survival outcome and covariates. We conduct extensive simulation studies to assess the finite‐sample performance of the pseudo‐value causal effect estimators. Furthermore, we illustrate our methodology via application to a dataset from a breast cancer cohort study. The proposed method is implementable using the R package drRML, available in GitHub. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success. Analysis of more than 150 Phase 3 oncology clinical trials supports parametric statistical analysis, significantly increasing the precision of small early-phase trials and relating deviations from the Cox proportional hazards model to trial duration. [ABSTRACT FROM AUTHOR]

Individuals in many observational studies and clinical trials for chronic diseases are enrolled well after onset or diagnosis of their disease. Times to events of interest after enrollment are therefore residual or left-truncated event times. Individuals entering the studies have disease that has advanced to varying extents. Moreover, enrollment usually entails probability sampling of the study population. Finally, event times over a short to moderate time horizon are often of interest in these investigations, rather than more speculative and remote happenings that lie beyond the study period. This research report looks at the issue of delayed entry into these kinds of studies and trials. Time to event for an individual is modelled as a first hitting time of an event threshold by a latent disease process, which is taken to be a Wiener process. It is emphasized that recruitment into these studies often involves length-biased sampling. The requisite mathematics for this kind of sampling and delayed entry are presented, including explicit formulas needed for estimation and inference. Restricted mean survival time (RMST) is taken as the clinically relevant outcome measure. Exact parametric formulas for this measure are derived and presented. The results are extended to settings that involve study covariates using threshold regression methods. Methods adapted for clinical trials are presented. An extensive case illustration for a clinical trial setting is then presented to demonstrate the methods, the interpretation of results, and the harvesting of useful insights. The closing discussion covers a number of important issues and concepts. [ABSTRACT FROM AUTHOR]

The restricted mean survival time measure has gained a lot of interests for designing and analyzing oncology trials with time-to-event endpoints due to its intuitive clinical interpretation and potentially high statistical power. In the non-inferiority trial literature, restricted mean survival time has been used as an alternative measure for reanalyzing a completed trial, which was originally designed and analyzed based on traditional proportional hazard model. However, the reanalysis procedure requires a conversion from the non-inferiority margin measured in hazard ratio to a non-inferiority margin measured by restricted mean survival time difference. An existing conversion method assumes a Weibull distribution for the population survival time of the historical active control group under the proportional hazard assumption using data from a single trial. In this article, we develop a methodology for non-inferiority margin conversion when data from multiple historical active control studies are available, and introduce a Kaplan-Meier estimator-based method for the non-inferiority margin conversion to relax the parametric assumption. We report extensive simulation studies to examine the performances of proposed methods under the Weibull data generative models and a piecewise-exponential data generative model that mimic the tumor recurrence and survival characteristics of advanced colon cancer. This work is motivated to achieve non-inferiority margin conversion, using historical patient-level data from a large colon cancer clinical database, to reanalyze an internationally collaborated non-inferiority study that evaluates 6-month versus 3-month duration of adjuvant chemotherapy in stage III colon cancer patients. [ABSTRACT FROM AUTHOR]

Survival regression is used to estimate the relation between time-to-event and feature variables, and is important in application domains such as medicine, marketing, risk management, and sales management. Nonlinear tree based machine learning algorithms as implemented in libraries such as XGBoost, scikit-learn, LightGBM, and CatBoost are often more accurate in practice than linear models. However, existing state-of-the-art implementations of tree-based models have offered limited support for survival regression. In this work, we implement loss functions for learning accelerated failure time (AFT) models in XGBoost, to increase the support for survival modeling for different kinds of label censoring. We demonstrate with real and simulated experiments the effectiveness of AFT in XGBoost with respect to a number of baselines, in two respects: generalization performance and training speed. Furthermore, we take advantage of the support for NVIDIA GPUs in XGBoost to achieve substantial speedup over multi-core CPUs. To our knowledge, our work is the first implementation of AFT that uses the processing power of NVIDIA GPUs. Starting from the 1.2.0 release, the XGBoost package natively supports the AFT model. The addition of AFT in XGBoost has had significant impact in the open source community, and a few statistics packages now use the XGBoost AFT model. for this article are available online. [ABSTRACT FROM AUTHOR]

In comparative research on time-to-event data for two groups, when two survival curves cross each other, it may be difficult to use the log-rank test and hazard ratio (HR) to properly assess the treatment benefit. Our aim was to identify a method for evaluating the treatment benefits for two groups in the above situation. We quantified treatment benefits based on an intuitive measure called the area between two survival curves (ABS), which is a robust measure of treatment benefits in clinical trials regardless of whether the proportional hazards assumption is violated or two survival curves cross each other. Additionally, we propose a permutation test based on the ABS, and we evaluate the effectiveness and reliability of this test with simulated data. The ABS permutation test is a robust statistical inference method with an acceptable type I error rate and superior power to detect differences in treatment effects, especially when the proportional hazards assumption is violated. The ABS can be used to intuitively quantify treatment differences over time and provide reliable conclusions in complicated situations, such as crossing survival curves. [ABSTRACT FROM AUTHOR]

The role of adjuvant radiotherapy (RT) following gross total resection (GTR) in atypical meningioma (AM) is not well established and its benefit remains unclear. We aim to evaluate the survival benefit of adjuvant RT in AM following GTR. We searched biomedical databases for studies published between January 1964-February 2021 and included studies reporting primary outcomes of 5-year PFS, 5-year OS and had survival curves for restricted mean survival time (RMST) calculations. Data extracted from survival curves were pooled and analyzed using a random-effects model. Hazard ratio (HR) was calculated for sensitivity analysis. We included 12 non-randomized studies comprising 1,078 patients. 803 (74.5%) patients were treated with GTR alone and 275 (25.5%) patients received adjuvant RT. In 9 studies, RT included 3 D conformal RT, intensity modulated RT, or fractionated stereotactic radiotherapy); in 3 studies, stereotactic radiosurgery was also used. Median dose of RT was 59.4 Gy. Adjuvant RT resulted in an increase of 3.9 months for restricted mean PFS truncated at 5 years (95% CI 0.23–7.72; p = 0.037) and a 22% reduction in the hazard of disease progression or death (hazards ratio 0.78; 95% CI 0.46–1.33; p = 0.370). Restricted mean OS, truncated at 5 years, was improved with adjuvant RT by 1.1 months (95% CI 0.37–1.81; p = 0.003) and a 21% reduction in the hazard of death from any cause (HR 0.79; 95% CI 0.51–1.24; p = 0.310). Meta-regression analysis of the RMST of EBRT dose did not reveal any significant difference in PFS or OS between studies reporting median dose of <59.4 Gy vs. ≥ 59.4 Gy. Adjuvant RT following GTR in patients with AM improved restricted mean PFS and OS. While we await the results from ongoing randomized controlled trials, adjuvant RT should be recommended. [ABSTRACT FROM AUTHOR]

Motivation In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear method to derive a risk prediction from a distribution prediction. We survey methods proposed in literature and software and consider their respective advantages and disadvantages. Results Whilst distributions are frequently evaluated by discrimination measures, we find that the method for doing so is rarely described in the literature and often leads to unfair comparisons or 'C-hacking'. We demonstrate by example how simple it can be to manipulate results and use this to argue for better reporting guidelines and transparency in the literature. We recommend that machine learning survival analysis software implements clear transformations between distribution and risk predictions in order to allow more transparent and accessible model evaluation. Availability and implementation The code used in the final experiment is available at https://github.com/RaphaelS1/distribution_discrimination. [ABSTRACT FROM AUTHOR]

Simple Summary: From a breeding and production point of view, the length and quality of life of dairy cows are directly determined (more or less) by voluntary decisions made by the breeders and technical staff in human–animal–environment relationships. In this case, economic conditions are the key roles that greatly complicate the decision processes, especially when it concerns the whole herd (not solely single animals). On the other hand, increasing social pressures on the continuous improvements of animal welfare and pro-environmental agricultural practices, including high-producing dairy cows, can be seen. Therefore, the aim of the present study was to analyze survival curves for cows culled for different reasons over three successive lactations and to determine the effects of various factors on cow survival. The main culling categories were reproductive disorders—40%, udder diseases—13 to 15%, and locomotor system diseases—above 10%. The survival curves for cows from individual culling categories had similar shapes. The greatest influences on the relative culling risks were exerted by: age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required. The aims of the study were: (i) to compare survival curves for cows culled for different reasons over three successive lactations using the Kaplan–Meier estimator; (ii) to determine the effects of breeding documentation parameters on cow survival; (iii) to investigate the similarity between culling categories. The survival times for a subset of 347,939 Holstein-Friesian cows culled between 2017 and 2018 in Poland were expressed in months from calving to culling or the end of lactation. The survival tables were constructed for each culling category and lactation number. The survival curves were also compared. The main culling categories were reproductive disorders—40%, udder diseases—13 to 15%, and locomotor system diseases—above 10%. The survival curves for cows from individual culling categories had similar shapes. A low probability of survival curves for metabolic and digestive system diseases and respiratory diseases was observed in each of the three lactations. The contagious disease category was almost non-existent in the first lactation. The greatest influence on the relative culling risk was exerted by age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the time‐varying cardio‐protective effect of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) using pooled data from eight contemporary cardiovascular outcome trials using the difference in the restricted mean survival time (ΔRMST) as the effect estimate. Material and Methods: Data from eight multinational cardiovascular outcome randomized controlled trials of GLP‐1RAs for type 2 diabetes mellitus were pooled. Flexible parametric survival models were fit from published Kaplan‐Meier plots. The differences between arms in RMST (ΔRMST) were calculated at 12, 24, 36 and 48 months. ΔRMST values were pooled using an inverse variance‐weighted random‐effects model; heterogeneity was tested with Cochran's Q statistic. The endpoints studied were: three‐point major adverse cardiovascular events (MACE), all‐cause mortality, stroke, cardiovascular mortality and myocardial infarction. Results: We included eight large (3183‐14 752 participants, total = 60 080; median follow‐up range: 1.5 to 5.4 years) GLP‐1RA trials. Among GLP‐1RA recipients, we observed an average delay in three‐point MACE of 0.03, 0.15, 0.37 and 0.63 months at 12, 24, 36 and 48 months, respectively. At 48 months, while cardiovascular mortality was comparable in both arms (pooled ΔRMST 0.163 [−0.112, 0.437]; P = 0.24), overall survival was higher (ΔRMST = 0.261 [0.08‐0.43] months) and stroke was delayed (ΔRMST 0.22 [0.15‐0.33]) in patients receiving GLP‐1RAs. Conclusions: Glucagon‐like peptide‐1 receptor agonists may delay the occurrence of MACE by an average of 0.6 months at 48 months, with meaningfully larger gains in patients with cardiovascular disease. This metric may be easier for clinicians and patients to interpret than hazard ratios, which assume a knowledge of absolute risk in the absence of treatment. [ABSTRACT FROM AUTHOR]

Background: Understanding occurrence and timing of second events (recurrence and second primary cancer) is essential for cancer specific survival analysis. However, this information is not readily available in administrative data. Methods: Alberta Cancer Registry, physician claims, and other administrative data were used. Timing of second event was estimated based on our developed algorithm. For validation, the difference, in days between the algorithm estimated and the chart‐reviewed timing of second event. Further, the result of Cox‐regression modeling cancer‐free survival was compared to chart review data. Results: Majority (74.3%) of the patients had a difference between the chart‐reviewed and algorithm‐estimated timing of second event falling within the 0–60 days window. Kaplan–Meier curves generated from the estimated data and chart review data were comparable with a 5‐year second‐event‐free survival rate of 75.4% versus 72.5%. Conclusion: The algorithm provided an estimated timing of second event similar to that of the chart review. [ABSTRACT FROM AUTHOR]