Model averaging for treatment effect estimation in subgroups.
Pharm Stat
; 16(2): 133-142, 2017 03.
Article
in En
| MEDLINE
| ID: mdl-27935199
ABSTRACT
In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting "random high" / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early-phase clinical trial.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Clinical Trials as Topic
/
Models, Statistical
/
Bayes Theorem
Type of study:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Pharm Stat
Journal subject:
FARMACOLOGIA
Year:
2017
Type:
Article
Affiliation country:
Switzerland