A note on non-parametric ANCOVA for covariate adjustment in randomized clinical trials

2003 ◽  
Vol 22 (23) ◽  
pp. 3583-3596 ◽  
Author(s):  
Emmanuel Lesaffre ◽  
Stephen Senn
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Covariate Adjustment ◽  
Non Parametric

Generalizing Experimental Findings

2015 ◽  
Vol 3 (2) ◽  
pp. 259-266 ◽  
Author(s):  
Judea Pearl
Keyword(s):  
Clinical Trials ◽  
General Solution ◽  
Causal Inference ◽  
Randomized Clinical Trials ◽  
Full Spectrum ◽  
Formal Treatment ◽  
Experimental Findings ◽  
The Way ◽  
Non Parametric

AbstractThis note examines one of the most crucial questions in causal inference: “How generalizable are randomized clinical trials?” The question has received a formal treatment recently, using a non-parametric setting, and has led to a simple and general solution. I will describe this solution and several of its ramifications, and compare it to the way researchers have attempted to tackle the problem using the language of ignorability. We will see that ignorability-type assumptions need to be enriched with structural assumptions in order to capture the full spectrum of conditions that permit generalizations, and in order to judge their plausibility in specific applications.

Subgroup Analysis and Covariate Adjustment in Randomized Clinical Trials of Traumatic Brain Injury: A Systematic Review

Neurosurgery ◽  
2005 ◽  
Vol 57 (6) ◽  
pp. 1244-1253 ◽  
Author(s):  
Adrían V. Hernández ◽  
Ewout W. Steyerberg ◽  
Gillian S. Taylor ◽  
Anthony Marmarou ◽  
J Dik F. Habbema ◽  
...  
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Clinical Trials ◽  
Brain Injury ◽  
Subgroup Analysis ◽  
Randomized Clinical Trials ◽  
Covariate Adjustment

scholarly journals Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

2021 ◽  
pp. 174077452110285
Author(s):  
Siyun Yang ◽  
Fan Li ◽  
Laine E Thomas ◽  
Fan Li
Keyword(s):  
Clinical Trials ◽  
Propensity Score ◽  
Exercise Training ◽  
Randomized Clinical Trials ◽  
Analysis Of Covariance ◽  
Covariate Adjustment ◽  
Operating Characteristics ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Subgroup Analyses

Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. Methods: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate–subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. Results: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Conclusion: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate–subgroup interactions in the propensity score model.

scholarly journals Restricted mean survival time: Does covariate adjustment improve precision in randomized clinical trials?

2018 ◽  
Vol 15 (2) ◽  
pp. 178-188 ◽  
Author(s):  
Theodore Karrison ◽  
Masha Kocherginsky
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Randomized Clinical Trials ◽  
Linear Models ◽  
Covariate Adjustment ◽  
Survival Times ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Background: Restricted mean survival time is a measure of average survival time up to a specified time point. There has been an increased interest in using restricted mean survival time to compare treatment arms in randomized clinical trials because such comparisons do not rely on proportional hazards or other assumptions about the nature of the relationship between survival curves. Methods: This article addresses the question of whether covariate adjustment in randomized clinical trials that compare restricted mean survival times improves precision of the estimated treatment effect (difference in restricted mean survival times between treatment arms). Although precision generally increases in linear models when prognostic covariates are added, this is not necessarily the case in non-linear models. For example, in logistic and Cox regression, the standard error of the estimated treatment effect does not decrease when prognostic covariates are added, although the situation is complicated in those settings because the estimand changes as well. Because estimation of restricted mean survival time in the manner described in this article is also based on a model that is non-linear in the covariates, we investigate whether the comparison of restricted mean survival times with adjustment for covariates leads to a reduction in the standard error of the estimated treatment effect relative to the unadjusted estimator or whether covariate adjustment provides no improvement in precision. Chen and Tsiatis suggest that precision will increase if covariates are chosen judiciously. We present results of simulation studies that compare unadjusted versus adjusted comparisons of restricted mean survival time between treatment arms in randomized clinical trials. Results: We find that for comparison of restricted means in a randomized clinical trial, adjusting for covariates that are associated with survival increases precision and therefore statistical power, relative to the unadjusted estimator. Omitting important covariates results in less precision but estimates remain unbiased. Conclusion: When comparing restricted means in a randomized clinical trial, adjusting for prognostic covariates can improve precision and increase power.

Empirical likelihood inference in randomized clinical trials

2017 ◽  
Vol 27 (12) ◽  
pp. 3770-3784
Author(s):  
Biao Zhang
Keyword(s):  
Clinical Trials ◽  
Empirical Likelihood ◽  
Treatment Effect ◽  
Regression Models ◽  
Randomized Clinical Trials ◽  
Covariate Adjustment ◽  
Average Treatment ◽  
Likelihood Approach ◽  
Average Treatment Effects ◽  
Specific Working

In individually randomized controlled trials, in addition to the primary outcome, information is often available on a number of covariates prior to randomization. This information is frequently utilized to undertake adjustment for baseline characteristics in order to increase precision of the estimation of average treatment effects; such adjustment is usually performed via covariate adjustment in outcome regression models. Although the use of covariate adjustment is widely seen as desirable for making treatment effect estimates more precise and the corresponding hypothesis tests more powerful, there are considerable concerns that objective inference in randomized clinical trials can potentially be compromised. In this paper, we study an empirical likelihood approach to covariate adjustment and propose two unbiased estimating functions that automatically decouple evaluation of average treatment effects from regression modeling of covariate–outcome relationships. The resulting empirical likelihood estimator of the average treatment effect is as efficient as the existing efficient adjusted estimators1 when separate treatment-specific working regression models are correctly specified, yet are at least as efficient as the existing efficient adjusted estimators1 for any given treatment-specific working regression models whether or not they coincide with the true treatment-specific covariate–outcome relationships. We present a simulation study to compare the finite sample performance of various methods along with some results on analysis of a data set from an HIV clinical trial. The simulation results indicate that the proposed empirical likelihood approach is more efficient and powerful than its competitors when the working covariate–outcome relationships by treatment status are misspecified.

Sign in / Sign up

Export Citation Format

Share Document