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Competing risks survival data in the presence of partially masked causes are frequently encountered in medical research or clinical trials. When longitudinal biomarkers are also available, it is of great clinical importance to examine associations between the longitudinal biomarkers and the cause-specific survival outcomes. In this article, we propose a cause-specific C-index for joint models of longitudinal and competing risks survival data accounting for masked causes. We also develop a posterior predictive algorithm for computing the out-of-sample cause-specific C-index using Markov chain Monte Carlo samples from the joint posterior of the in-sample longitudinal and competing risks survival data. We further construct the Δ $$ \Delta $$ C-index to quantify the strength of association between the longitudinal and cause-specific survival data, or between the out-of-sample longitudinal and survival data. Empirical performance of the proposed assessment criteria is examined through an extensive simulation study. An in-depth analysis of the real data from large cancer prevention trials is carried out to demonstrate the usefulness of the proposed methodology., (© 2023 John Wiley & Sons Ltd.)

Statistical analysis is a cornerstone of the scientific method and evidence-based medicine, and statisticians serve an increasingly important role in clinical and translational research by providing objective evidence concerning the risks and benefits of novel therapeutics. Researchers rely on statistics and informatics as never before to generate and test hypotheses and to discover patterns of disease hidden within overwhelming amounts of data. Too often, clinicians and biomedical scientists are not adequately proficient in statistics to analyze data or interpret results, and statistical expertise may not be properly incorporated within the research process. We argue for the ethical imperative of statistical standards, and we present ten nontechnical principles that form a conceptual framework for the ethical application of statistics in clinical and translational research. These principles are drawn from the literature on the ethics of data analysis and the American Statistical Association Ethical Guidelines for Statistical Practice., (Copyright © 2011 John Wiley & Sons, Ltd.)