Economics at your fingertips  

Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death

Maria Josefsson and Michael J. Daniels

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 2, 398-414

Abstract: Causal inference with observational longitudinal data and time‐varying exposures is often complicated by time‐dependent confounding and attrition. The G‐computation formula is one approach for estimating a causal effect in this setting. The parametric modelling approach typically used in practice relies on strong modelling assumptions for valid inference and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi‐parametric G‐computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then, use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health and ageing and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.

Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2021-05-12
Handle: RePEc:bla:jorssc:v:70:y:2021:i:2:p:398-414