Causal Inference for Continuous Multiple TimePoint Interventions

Simple item page
dc.contributor.authorSchomaker, Michael
dc.contributor.authorMcIlleron, Helen
dc.contributor.authorDenti, Paolo
dc.contributor.authorDíaz, Iván
dc.date.accessioned2024-12-16T08:18:14Z
dc.date.available2024-12-16T08:18:14Z
dc.date.issued2024-12
dc.description.abstractThere are limited options to estimate the treatment effects of variables which are continuous and measured at multiple time points, particularly if the true dose-response curve should be estimated as closely as possible. However, these situations may be of relevance: in pharmacology, one may be interested in how outcomes of people living with-and treated for-HIV, such as viral failure, would vary for time-varying interventions such as different drug concentration trajectories. A challenge for doing causal inference with continuous interventions is that the positivity assumption is typically violated. To address positivity violations, we develop projection functions, which reweigh and redefine the estimand of interest based on functions of the conditional support for the respective interventions. With these functions, we obtain the desired dose-response curve in areas of enough support, and otherwise a meaningful estimand that does not require the positivity assumption. We develop -computation type plug-in estimators for this case. Those are contrasted with g-computation estimators which are applied to continuous interventions without specifically addressing positivity violations, which we propose to be presented with diagnostics. The ideas are illustrated with longitudinal data from HIV positive children treated with an efavirenz-based regimen as part of the CHAPAS-3 trial, which enrolled children years in Zambia/Uganda. Simulations show in which situations a standard g-computation approach is appropriate, and in which it leads to bias and how the proposed weighted estimation approach then recovers the alternative estimand of interest.
dc.description.sponsorshipMichael Schomaker is supported by the German Research Foundations (DFG) Heisenberg Programm (grants 465412241 and 465412441)
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9728
dc.language.isoen
dc.publisherWiley Subscription Services, Inc
dc.titleCausal Inference for Continuous Multiple TimePoint Interventions
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Statistics in Medicine - 2024 - Schomaker - Causal Inference for Continuous Multiple Time Point Interventions.pdf
Size:
3.14 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Medical and Health Sciences