King's College London

Research portal

Dose-response modeling in mental health using stein-like estimators with instrumental variables

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1696–1714
JournalStatistics in Medicine
Volume36
Issue number11
Early online date21 Feb 2017
DOIs
Publication statusPublished - 20 May 2017

Documents

King's Authors

Abstract

A mental health trial is analyzed using a dose-response model, in which the number of sessions attended by the patients is deemed indicative of the dose of psychotherapeutic treatment. Here, the parameter of interest is the difference in causal treatment effects between the subpopulations that take part in different numbers of therapy sessions. For this data set, interactions between random treatment allocation and prognostic baseline variables provide the requisite instrumental variables. While the corresponding two-stage least squares (TSLS) estimator tends to have smaller bias than the ordinary least squares (OLS) estimator; the TSLS suffers from larger variance. It is therefore appealing to combine the desirable properties of the OLS and TSLS estimators. Such a trade-off is achieved through an affine combination of these two estimators, using mean squared error as a criterion. This produces the semi-parametric Stein-like (SPSL) estimator as introduced by Judge and Mittelhammer (2004). The SPSL estimator is used in conjunction with multiple imputation with chained equations, to provide an estimator that can exploit all available information. Simulated data are also generated to illustrate the superiority of the SPSL estimator over its OLS and TSLS counterparts. A package entitled SteinIV implementing these methods has been made available through the R platform.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454