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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2018

Open Access 01-12-2018 | Research article

Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting

Authors: Vahé Asvatourian, Clélia Coutzac, Nathalie Chaput, Caroline Robert, Stefan Michiels, Emilie Lanoy

Published in: BMC Medical Research Methodology | Issue 1/2018

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Abstract

Background

Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurements of biomarkers are repeated at fixed time points during treatment and, therefore, this method needs to be extended. The purpose of this paper is to extend the first step of the IDA, the Peter Clarks (PC)-algorithm, to a time-dependent exposure in the context of a binary outcome.

Methods

We generalised the so-called “PC-algorithm” to take into account the chronological order of repeated measurements of the exposure and proposed to apply the IDA with our new version, the chronologically ordered PC-algorithm (COPC-algorithm). The extension includes Firth’s correction. A simulation study has been performed before applying the method for estimating causal effects of time-dependent immunological biomarkers on toxicity, death and progression in patients with metastatic melanoma.

Results

The simulation study showed that the completed partially directed acyclic graphs (CPDAGs) obtained using COPC-algorithm were structurally closer to the true CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more accurate when they were estimated based on CPDAGs obtained using COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing non-chronological arrows with a variable measured at a time t pointing to a variable measured at a time t´ where t´ < t. Bidirected edges were less present in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was less variability in causal effects estimated from these CPDAGs. In the example, a threshold of the per-comparison error rate of 0.5% led to the selection of an interpretable set of biomarkers.

Conclusions

The COPC-algorithm provided CPDAGs that keep the chronological structure present in the data and thus allowed to estimate lower bounds of the causal effect of time-dependent immunological biomarkers on early toxicity, premature death and progression.
Appendix
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Metadata
Title
Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting
Authors
Vahé Asvatourian
Clélia Coutzac
Nathalie Chaput
Caroline Robert
Stefan Michiels
Emilie Lanoy
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2018
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-018-0527-5

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