A more flexible regression-to-the-mean model with possible stratification

Shande Chen; Christopher Cox; Lu Cui

doi:10.2307/2533847

A more flexible regression-to-the-mean model with possible stratification

Shande Chen, Christopher Cox, Lu Cui

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

We consider a regression-to-the-mean model that includes both additive and multiplicative treatment effects. We allow either or both of these treatment effects to be stratified by ranges of the first measurement. We focus on the situation where there is a very large sample on the first measurement and a relatively small subsample for the second measurement is selected, which often occurs in screening trials. We propose some asymptotically efficient estimators for the parameters of the model that are very simple to compute. We begin with a discussion of the full model, and more on tests and estimation for reduced models follows. An example from a large screening trial is discussed.

Original language	English (US)
Pages (from-to)	939-947
Number of pages	9
Journal	Biometrics
Volume	54
Issue number	3
DOIs	https://doi.org/10.2307/2533847
State	Published - Sep 1998
Externally published	Yes

Keywords

Asymptotically efficient estimator
Bivariate normal
Likelihood ratio test
Pseudo MLE
Screening trial
Treatment effects
Wald test

ASJC Scopus subject areas

Statistics and Probability
Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Agricultural and Biological Sciences(all)
Applied Mathematics

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10.2307/2533847

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abstract = "We consider a regression-to-the-mean model that includes both additive and multiplicative treatment effects. We allow either or both of these treatment effects to be stratified by ranges of the first measurement. We focus on the situation where there is a very large sample on the first measurement and a relatively small subsample for the second measurement is selected, which often occurs in screening trials. We propose some asymptotically efficient estimators for the parameters of the model that are very simple to compute. We begin with a discussion of the full model, and more on tests and estimation for reduced models follows. An example from a large screening trial is discussed.",

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AU - Cox, Christopher

AU - Cui, Lu

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AB - We consider a regression-to-the-mean model that includes both additive and multiplicative treatment effects. We allow either or both of these treatment effects to be stratified by ranges of the first measurement. We focus on the situation where there is a very large sample on the first measurement and a relatively small subsample for the second measurement is selected, which often occurs in screening trials. We propose some asymptotically efficient estimators for the parameters of the model that are very simple to compute. We begin with a discussion of the full model, and more on tests and estimation for reduced models follows. An example from a large screening trial is discussed.

KW - Asymptotically efficient estimator

KW - Bivariate normal

KW - Likelihood ratio test

KW - Pseudo MLE

KW - Screening trial

KW - Treatment effects

KW - Wald test

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ER -

A more flexible regression-to-the-mean model with possible stratification

Abstract

Keywords

ASJC Scopus subject areas

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