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

Yongqiang Tang1

  • 1Department of Biometrics, Grifols, Durham, North Carolina, USA.

Statistics in Medicine
|October 21, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a new method for creating confidence intervals (CIs) in stratified clinical trials. This method, MOVER (method of variance estimates recovery), improves precision for treatment effect estimates.

Keywords:
Fieller methodMantel-Haensze estimatoradditive confidence interval approachadditive variance approachdelta methodminimum risk weightnonconstant effectrestricted mean survival time

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Methods

Background:

  • Stratification is crucial in clinical trials to minimize covariate imbalance and enhance treatment effect precision.
  • Existing methods for confidence intervals (CIs) in stratified analyses may lack optimal precision or applicability across diverse endpoints.

Purpose of the Study:

  • To propose a general framework for constructing confidence intervals (CIs) for difference or ratio effect parameters under stratified sampling using the method of variance estimates recovery (MOVER).
  • To adapt MOVER for both additive variance and additive CI approaches for differences, and for ratio parameters using Fieller and log-ratio methods.
  • To demonstrate the applicability of MOVER CIs across various endpoints, including binary and time-to-event outcomes.

Main Methods:

  • Developed a general framework using the method of variance estimates recovery (MOVER) for stratified sampling.
  • Applied additive variance and additive CI approaches for difference parameters, and Fieller and log-ratio methods for ratio parameters.
  • Illustrated methods with real-world examples for binary outcomes (risk difference, risk ratio) and time-to-event outcomes (restricted mean survival time, milestone survival).

Main Results:

  • Proposed MOVER confidence intervals (CIs) generally outperform standard large sample CIs in stratified analyses.
  • The additive CI approach demonstrated superior performance compared to the additive variance approach.
  • MOVER methods require only point estimates, CIs, and variance estimates from stratum-specific data, facilitating broad application.

Conclusions:

  • The MOVER framework provides a flexible and effective approach for constructing confidence intervals in stratified clinical trials.
  • MOVER CIs offer improved precision and performance over traditional methods, particularly the additive CI approach.
  • The proposed methods are broadly applicable to various endpoints and facilitate robust statistical inference in stratified analyses.