Semiparametric estimation of treatment effect in a pretest-posttest study.
Inference on treatment effects in a pretest-posttest study is a routine objective in medicine, public health, and other fields. A number of approaches have been advocated. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. By representing the situation in terms of counterfactual random variables, we exploit recent developments in the literature on missing data and causal inference, to derive the class of all consistent treatment effect estimators, identify the most efficient such estimator, and outline strategies for implementation of estimators that may improve on popular methods. We demonstrate the methods and their properties via simulation and by application to a data set from an HIV clinical trial.
Duke Scholars
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Related Subject Headings
- Statistics, Nonparametric
- Statistics & Probability
- Randomized Controlled Trials as Topic
- Models, Statistical
- Humans
- HIV Infections
- Computer Simulation
- Clinical Trials as Topic
- CD4 Lymphocyte Count
- Biometry
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Statistics, Nonparametric
- Statistics & Probability
- Randomized Controlled Trials as Topic
- Models, Statistical
- Humans
- HIV Infections
- Computer Simulation
- Clinical Trials as Topic
- CD4 Lymphocyte Count
- Biometry