Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods

@article{Ye2020InferenceOA,
  title={Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods},
  author={Ting Ye and Yanyao Yi and Jun Shao},
  journal={arXiv: Methodology},
  year={2020}
}
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theoretical developments on inference after covariate-adaptive randomization are mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate levels… 

Tables from this paper

Inference after covariate-adaptive randomisation: aspects of methodology and theory

Covariate-adaptive randomisation has a more than 45 years of history of applications in clinical trials, in order to balance treatment assignments across prognostic factors that may have influence on

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

Regression analysis for covariate‐adaptive randomization: A robust and efficient inference perspective

This paper investigates several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials and proposes consistent non-parametric variance estimators, demonstrating that all these regression-based estimators robustly estimate thereatment effect, albeit with possibly different efficiency.

Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment

Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined

Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach

Robust Permutation Test for Equality of Distributions under Covariate-Adaptive Randomization

Though stratified randomization achieves more balance on baseline covariates than pure randomization, it does affect the way we conduct inference. This paper considers the classical two-sample

Blocking, rerandomization, and regression adjustment in randomized experiments with high-dimensional covariates

This paper proposes several methods that combine the blocking, rerandomization, and regression adjustment techniques in randomized experiments with high-dimensional covariates and establishes the asymptotic properties of the proposed Lasso-adjusted average treatment effect estimators and outline conditions under which these estimators are more efficient than the unadjusted estimators.

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

We study how to improve efficiency via regression adjustments with additional covariates under covariate-adaptive randomizations (CARs) when subject compliance is imperfect. We first establish the

Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations

This paper examines regression-adjusted estimation and inference of unconditional quantile treatment effects (QTEs) under covariate-adaptive randomizations (CARs). Datasets from field experiments

Covariate-adjusted Fisher randomization tests for the average treatment effect

References

SHOWING 1-10 OF 41 REFERENCES

Robust tests for treatment effect in survival analysis under covariate‐adaptive randomization

Covariate‐adaptive randomization is popular in clinical trials with sequentially arrived patients for balancing treatment assignments across prognostic factors that may have influence on the

Inference Under Covariate-Adaptive Randomization

In inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization, which means randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum, is studied.

Inference under Covariate-Adaptive Randomization with Multiple Treatments

This paper studies inference in randomized controlled trials with covariate‐adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the

Validity of Tests under Covariate‐Adaptive Biased Coin Randomization and Generalized Linear Models

It is shown that the simple t-test without using any covariate is conservative under covariate-adaptive biased coin randomization in terms of its Type I error rate, and that a valid test using the bootstrap can be constructed.

Using Randomization Tests to Preserve Type I Error With Response-Adaptive and Covariate-Adaptive Randomization.

A theory for testing hypotheses under covariate-adaptive randomization

The covariate-adaptive randomization method was proposed for clinical trials long ago but little theoretical work has been done for statistical inference associated with it. Practitioners often apply

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Covariate-adaptive designs are often implemented to balance important covariates in clinical trials. However, the theoretical properties of conventional testing hypotheses are usually unknown under

Dynamic randomization and a randomization model for clinical trials data

  • L. Kaiser
  • Mathematics
    Statistics in medicine
  • 2012
A randomization model for clinical trials data with arbitrary randomization methodology is developed, with treatment effect estimators and standard error estimators valid from a randomization perspective.

Asymptotic properties of covariate-adaptive randomization

Balancing treatment allocation for influential covariates is critical in clinical trials. This has become increasingly important as more and more biomarkers are found to be associated with different

Randomization by minimization for unbalanced treatment allocation

BCM is the preferable method for randomization in clinical trials involving unequal treatment allocations and the choice of different distance metrics slightly affects the performance of the minimization and may be optimized according to the specific feature of trials.