Estimation of treatment effect in a subpopulation: An empirical Bayes approach

Changyu Shen, Xiaochun Li, Jaesik Jeong

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.

Original languageEnglish (US)
JournalJournal of Biopharmaceutical Statistics
DOIs
StateAccepted/In press - Nov 11 2015

Fingerprint

Empirical Bayes
Treatment Effects
Confidence interval
Confidence Intervals
Randomized Clinical Trial
Randomized Trial
Multiple Comparisons
Target
Health Services Needs and Demand
Therapeutics
Sample Size
Uncertainty
Simplicity
Data analysis
Person
Randomized Controlled Trials
Simulation Study
Tend
Costs and Cost Analysis
Costs

Keywords

  • Causal inference
  • empirical bayes
  • heterogeneity in treatment effect
  • subgroup analysis

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology
  • Statistics and Probability

Cite this

Estimation of treatment effect in a subpopulation : An empirical Bayes approach. / Shen, Changyu; Li, Xiaochun; Jeong, Jaesik.

In: Journal of Biopharmaceutical Statistics, 11.11.2015.

Research output: Contribution to journalArticle

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