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Some years ago, Snapinn and Jiang[1] considered the interpretation and pitfalls of absolute versus relative treatment effect measures in analyses of time-to-event outcomes. Through specific examples and analytical considerations based solely on the exponential and the Weibull distributions they reach two conclusions: 1) that the commonly used criteria for clinical effectiveness, the ARR (Absolute Risk Reduction) and the median (survival time) difference (MD) directly contradict each other and 2) cost-effectiveness depends only the hazard ratio(HR) and the shape parameter (in the Weibull case) but not the overall baseline risk of the population. Though provocative, the first conclusion does not apply to either the two special cases considered or even more generally, while the second conclusion is strictly correct only for the exponential case. Therefore, the implication inferred by the authors i.e. all measures of absolute treatment effect are of little value compared with the relative measure of the hazard ratio, is not of general validity and hence both absolute and relative measures should continue to be used when appraising clinical evidence.
Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of combinatio
A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Muller (2017, Biometrics, 1355-1365) to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizin
Multi-touch attribution (MTA) estimates the relative contributions of the multiple ads a user may see prior to any observed
We review methods for monitoring multivariate time-between-events (TBE) data. We present some underlying complexities that have been overlooked in the literature. It is helpful to classify multivariate TBE monitoring applications into two fundamental
Uncertainty Quantification (UQ) is an essential step in computational model validation because assessment of the model accuracy requires a concrete, quantifiable measure of uncertainty in the model predictions. The concept of UQ in the nuclear commun