Fooled by randomness. The misleading effect of treatment crossover in randomized trials of therapies with marginal treatment benefit.

2021 ◽  
pp. 1-7
Author(s):  
Vicente Valentí ◽  
Paula Jiménez-Fonseca ◽  
Pavlos Msaouel ◽  
Ramón Salazar ◽  
Alberto Carmona-Bayonas
Keyword(s):  
Randomized Trials ◽  
Treatment Benefit ◽  
Effect Of Treatment

scholarly journals A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks

Biostatistics ◽  
2020 ◽  
Author(s):  
Yanxun Xu ◽  
Daniel Scharfstein ◽  
Peter Müller ◽  
Michael Daniels
Keyword(s):  
Competing Risks ◽  
Randomized Trials ◽  
Causal Effect ◽  
Simulation Studies ◽  
Principal Stratification ◽  
Bayesian Nonparametric ◽  
Cancer Trial ◽  
Nonparametric Approach ◽  
Effect Of Treatment ◽  
Using Data

Summary We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.

Power for Detecting Treatment by Moderator Effects in Two- and Three-Level Cluster Randomized Trials

2016 ◽  
Vol 41 (6) ◽  
pp. 605-627 ◽  
Author(s):  
Jessaca Spybrook ◽  
Benjamin Kelcey ◽  
Nianbo Dong
Keyword(s):  
Statistical Power ◽  
Randomized Trials ◽  
A Priori ◽  
Individual Characteristics ◽  
Cluster Randomized Trials ◽  
Moderator Effects ◽  
Effect Of Treatment ◽  
Cluster Randomized ◽  
Level Cluster ◽  
The Impact

Recently, there has been an increase in the number of cluster randomized trials (CRTs) to evaluate the impact of educational programs and interventions. These studies are often powered for the main effect of treatment to address the “what works” question. However, program effects may vary by individual characteristics or by context, making it important to also consider power to detect moderator effects. This article presents a framework for calculating statistical power for moderator effects at all levels for two- and three-level CRTs. Annotated R code is included to make the calculations accessible to researchers and increase the regularity in which a priori power analyses for moderator effects in CRTs are conducted.

1115: The Effect of Treatment Strategy on Stone Comminution Efficiency in Shock Wave Lithotripsy

2004 ◽  
Vol 171 (4S) ◽  
pp. 294-294
Author(s):  
Yufeng Zhou ◽  
Charles G. Marguet ◽  
Michaella E. Maloney ◽  
Franklin H. Cocks ◽  
Glenn M. Preminger ◽  
...  
Keyword(s):  
Shock Wave ◽  
Treatment Strategy ◽  
Shock Wave Lithotripsy ◽  
Effect Of Treatment
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