Robust inference in discrete hazard models for randomized clinical trials

Hazard ratio in clinical trials.

Time-to-event curves analyzed by Cox proportional hazards regression are commonly used to describe the outcome of drug studies. This methodology has the advantage of using all available information, including patients who fail to complete the trial, such as in cancer chemotherapy or human immunodeficiency virus antiviral treatment studies. The goal of treatment in such studies may be to prevent...

Statistical inference of adaptive randomized clinical trials for personalized medicine

Feifang Hu*,1, Yanqing Hu2, Wei Ma3, Lixin Zhang4 & Hongjian Zhu5 Department of Statistics, George Washington University, Washington, DC 20052, USA Department of Statistics, West Virginia University, Morgantown, WV 26506, USA Biogen Idec, 14 Cambridge Center, Cambridge, MA 02142, USA Department of Mathematics, Zhejiang University, Hangzhou 310006, PR China Department of Biostatistics, The Unive...

Blindness in Randomized Controlled Trials

In combination with randomization, blinding or masking is an important factor in randomized controlled trials (RCTs), particularly in trials that assess therapeutic effects. Here an attempt is made to explain blindness and why it is important. In clinical trials, blinding is defined as the condition imposed on a study in which study participants, health care providers and assessors collecting o...

Full Bayesian inference with hazard mixture models

Bayesian nonparametric inferential procedures based on Markov chain Monte Carlo marginal methods typically yield point estimates in the form of posterior expectations. Though very useful and easy to implement in a variety of statistical problems, these methods may suffer from some limitations if used to estimate non-linear functionals of the posterior distribution. The main goal is to develop a...

Causal Inference in Randomized Trials with Noncompliance