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. 2024 Dec 10;43(28):5380-5400.
doi: 10.1002/sim.10246. Epub 2024 Oct 17.

Causal Inference for Continuous Multiple Time Point Interventions

Michael Schomaker  1   2   3 , Helen McIlleron  4   5 , Paolo Denti  4 , Iván Díaz  6
Affiliations

Causal Inference for Continuous Multiple Time Point Interventions

Michael Schomaker et al. Stat Med. .

Abstract

There 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 g $$ g $$ -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 < 13 $$ <13 $$ 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.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Considerations for causal dose–response curves (CDRCs).
FIGURE 2
FIGURE 2
Results of the Monte‐Carlo simulations.
FIGURE 3
FIGURE 3
Directed acyclic graph for the data analysis. The intervention variable is shown in green (efavirenz concentration), the outcome in blue (viral load at the end of follow‐up). Unmeasured variables are colored in gray. Both MEMS and weight are time‐dependent confounders which are affected by prior treatment nodes.
FIGURE 4
FIGURE 4
Results of the data analysis.
FIGURE B1
FIGURE B1
Additional simulation and analysis results.
FIGURE B2
FIGURE B2
Conditional support for intervention strategies of interest, for both the simulation settings and the data analysis.
See this image and copyright information in PMC

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