5 research outputs found
Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective
Linear regression is arguably the most fundamental statistical model;
however, the validity of its use in randomized clinical trials, despite being
common practice, has never been crystal clear, particularly when stratified or
covariate-adaptive randomization is used. In this paper, we investigate several
of the most intuitive and commonly used regression models for estimating and
inferring the treatment effect in randomized clinical trials. By allowing the
regression model to be arbitrarily misspecified, we demonstrate that all these
regression-based estimators robustly estimate the treatment effect, albeit with
possibly different efficiency. We also propose consistent non-parametric
variance estimators and compare their performances to those of the model-based
variance estimators that are readily available in standard statistical
software. Based on the results and taking into account both theoretical
efficiency and practical feasibility, we make recommendations for the effective
use of regression under various scenarios. For equal allocation, it suffices to
use the regression adjustment for the stratum covariates and additional
baseline covariates, if available, with the usual ordinary-least-squares
variance estimator. For unequal allocation, regression with
treatment-by-covariate interactions should be used, together with our proposed
variance estimators. These recommendations apply to simple and stratified
randomization, and minimization, among others. We hope this work helps to
clarify and promote the usage of regression in randomized clinical trials
carat: An R Package for Covariate-Adaptive Randomization in Clinical Trials
Covariate-adaptive randomization is gaining popularity in clinical trials because they enable the generation of balanced allocations with respect to covariates. Over the past decade, substantial progress has been made in both new innovative randomization procedures and the theoretical properties of associated inferences. However, these results are scattered across the literature, and a single tool kit does not exist for use by clinical trial practitioners and researchers to conduct and evaluate these methods. The R package carat is proposed to address this need. It facilitates a broad range of covariate-adaptive randomization and testing procedures, such as the most common and classical methods, and also reflects recent developments in the field. The package contains comprehensive evaluation and comparison tools for use in both randomization procedures and tests. This enables power analysis to be conducted to assist the planning of a covariate-adaptive clinical trial. The package also implements a command-line interface to allow for an interactive allocation procedure, which is typically the case in real-world applications. In this paper, the features and functionalities of carat are presented
Age-related change in muscle strength, muscle mass, and fat mass between the dominant and non-dominant upper limbs
BackgroundAny form of physical activity is recommended for the older adults to maintain their physical function; however, the effect of daily activities on muscle function still needs to be investigated. Humans always use one dominant hand to perform tasks, providing a natural situation for research on the effect of daily activities on muscle function.MethodsFive hundred and twenty-six healthy adults were recruited from the community in Beijing. Muscle strength was assessed using a handgrip dynamometer, lean mass, fat mass, bone area and bone mineral content of upper limbs were assessed using dual-energy X ray-absorptiometry. The results were compared between the dominant and non-dominant upper limbs.ResultsThe dominant upper limb had better muscle strength, lean mass, bone area and bone mineral content than the non-dominant side. The difference in muscle strength and lean mass between the two upper limbs decreased with the advanced age. In older age, fat mass of upper limbs increased in men, but not in women.ConclusionDaily activities can maintain better muscle function in the dominant upper limb than in the non-dominant side; however, the delaying effect on age-related decline in muscle function was limited
A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond
We consider the problem of estimating and inferring treatment effects in
randomized experiments. In practice, stratified randomization, or more
generally, covariate-adaptive randomization, is routinely used in the design
stage to balance the treatment allocations with respect to a few variables that
are most relevant to the outcomes. Then, regression is performed in the
analysis stage to adjust the remaining imbalances to yield more efficient
treatment effect estimators. Building upon and unifying the recent results
obtained for ordinary least squares adjusted estimators under
covariate-adaptive randomization, this paper presents a general theory of
regression adjustment that allows for arbitrary model misspecification and the
presence of a large number of baseline covariates. We exemplify the theory on
two Lasso-adjusted treatment effect estimators, both of which are optimal in
their respective classes. In addition, nonparametric consistent variance
estimators are proposed to facilitate valid inferences, which work irrespective
of the specific randomization methods used. The robustness and improved
efficiency of the proposed estimators are demonstrated through a simulation
study and a clinical trial example. This study sheds light on improving
treatment effect estimation efficiency by implementing machine learning methods
in covariate-adaptive randomized experiments