Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures.
J R Stat Soc Ser C Appl Stat
; 72(4): 976-991, 2023 Aug.
Article
en En
| MEDLINE
| ID: mdl-37662554
ABSTRACT
In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to characterize how treatment effects manifest over time. We propose modified Q-learning with generalized estimating equations to address these limitations and apply it to the M-bridge trial, which evaluates adaptive interventions to prevent problematic drinking among college freshmen. Simulation studies demonstrate our proposed method improves efficiency and robustness.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
Idioma:
En
Revista:
J R Stat Soc Ser C Appl Stat
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos