MOVER confidence intervals for a difference or ratio effect parameter under stratified sampling

TL;DR

This paper introduces a flexible MOVER-based framework for constructing confidence intervals for difference and ratio effect parameters in stratified sampling, improving accuracy over standard methods.

Contribution

It develops a general, easy-to-apply MOVER approach for stratified data to estimate confidence intervals for various effect measures, including differences and ratios.

Findings

MOVER CIs outperform standard large sample CIs in simulations

Additive CI approach performs slightly better than additive variance approach

Framework applicable to binary and time-to-event outcomes

Abstract

Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted difference, or the CI for the weighted effect in each group, or the variance for the weighted difference is calculated as the weighted sum of the corresponding stratum-specific statistics. The CI for the ratio is derived by the Fieller and log-ratio methods. The weights can be random quantities under the assumption of a constant effect across strata, but this assumption is not needed for fixed weights. These methods can be easily applied to different endpoints in that they require only the point estimate, CI, and variance estimate for the measure of interest in each group across strata. The methods are illustrated with two real examples. In one example, we derive the MOVER CIs for the risk difference and risk ratio for binary outcomes. In the other example, we compare the restricted mean survival time and milestone survival in stratified analysis of time-to-event outcomes. Simulations show that the proposed MOVER CIs generally outperform the standard large sample CIs, and that the additive CI approach performs slightly better than the additive variance approach.