MOVER confidence intervals for a difference or ratio effect parameter under stratified sampling.
Fieller method
Mantel-Haensze estimator
additive confidence interval approach
additive variance approach
delta method
minimum risk weight
nonconstant effect
restricted mean survival time
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
15 01 2022
15 01 2022
Historique:
revised:
16
08
2021
received:
24
04
2021
accepted:
27
09
2021
pubmed:
22
10
2021
medline:
11
3
2022
entrez:
21
10
2021
Statut:
ppublish
Résumé
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 better than the additive variance approach. Sample SAS code is provided in the Supplementary Material.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
194-207Informations de copyright
© 2021 John Wiley & Sons Ltd.
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