ijsmr
Editor's Choice : Survival Functions in the Presence of Several Events and Competing Risks: Estimation and Interpretation Beyond Kaplan-Meier
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Abstract: Evaluation of a therapeutic strategy is complex when the course of a disease is characterized by the occurrence of different kinds of events. Competing risks arise when the occurrence of specific events prevents the observation of other events. A particular case is semi-competing risks when only fatal events can prevent the observation of the non fatal ones. Kaplan-Meier is the most popular method to estimate overall or event free survival. On the other hand when a subset of events is considered and net survival is of concern, different estimators have been proposed. Kaplan-Meier method can be used only under the independence assumptions otherwise estimators based on multivariate distribution of times are needed. If causes of death are unknown, relative survival can approximate net survival only under specific assumptions on the mortality pattern. Kaplan-Meier method cannot be used to estimate crude cumulative incidence of specific events. The aim of this work is to present the survival functions used in competing risks framework, their non parametric estimators and semi parametric estimators for net survival based on Archimedean Copulas. This would be a help for the reader who is not experienced in competing risks analysis. A simulation study is performed to evaluate performances of net survival estimators. To illustrate survival functions in presence of different causes of death and of different kind of events a numerical example is given, a literature dataset on prostate cancer and a case series of breast cancer patients have been analysed. Keywords: Survival analysis, competing risks, crude cumulative incidence, net survival, relative survival, breast cancer.Download Full Article |
Editor's Choice : Time Profile of Time-Dependent Area Under the ROC Curve for Survival Data
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Abstract: In the setting of survival analysis, the time-dependent area under the receiver operating characteristic curve (AUC) has been proposed as a discrimination measure of interest. In contrast with the diagnostic setting, the definitions of time-dependent sensitivity and specificity are required. This paper evaluates the time-dependent profile of the resulting AUC(t), which has not been previously assessed. We show that, even when the effect of a binary biomarker on the hazard rate is constant, the value of AUC(t) varies over time according to the prevalence of the marker. The Time-profile of the continuous biomarker is illustrated with numerical integration, and data on several prognostic factors in AML are examined. Keywords: Survival analysis, Prognostic models, Time-dependent AUC, Proportional hazards models.Download Full Article |
Editor's Choice : Using Propensity Score Matching in Clinical Investigations: A Discussion and Illustration
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Abstract: Propensity score matching is a useful tool to analyze observational data in clinical investigations, but it is often executed in an overly simplistic manner, failing to use the data in the best possible way. This review discusses current best practices in propensity score matching, outlining the method’s essential steps, including appropriate post-matching balance assessments and sensitivity analyses. These steps are summarized as eight key traits of a propensity matched study. Further, this review illustrates these traits through a case study examining the impact of access site in percutaneous coronary intervention (PCI) procedures on bleeding complications. Through propensity score matching, we find that bleeding occurs significantly less often with radial access procedures, though many other outcomes show no significant difference by access site, a finding that mirrors the results of randomized controlled trials. Lack of attention to methodological principles can result in results that are not biologically plausible. Keywords: Propensity Score Matching, Observational Data, Clinical Investigations. Download Full Article |
Editor's Choice : A Contribution to the Genetic Epidemiology of Structured Populations
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Abstract: A matingsystem, previously derived, which is more general than random mating is defined by the gene frequency q and a parameter F which measures divergence from Hardy-Weinberg proportions commonly used in genetic analysis. F can be viewed as the average coefficient of inbreeding in a population, the use emphasized here. Also it can characterize the variation in gene frequency in a stratified population. Taking q as fixed, the distribution of F over values admissible under the general mating system is derived by simulation. The mating system may be seen to be based on indifference as to choice of mates. This is the first object of the paper. The second uses the derived distribution of F to make a Bayesian estimate of F from a single sample of genotypic counts. Such an estimate has a number of uses in genetic analysis. Keywords: Genetic Equilibrium, Hardy-Weinberg Law, Mate choice indifference, Inbreeding coefficient, Bayesian estimation.Download Full Article |
