Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting.
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 man...
| Publicado en: | BMC Medical Research Methodology Vol. 18; no. 1 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
7/3/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130594345&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130594345 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 7/3/2018 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 130594345 130594345 NLM29969993 130594345 10.1186/s12874-018-0527-5 NLM29969993 130594345 ppct: 1 formats: tig: atl: Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting. aug: au: Asvatourian, Vahé Coutzac, Clélia Chaput, Nathalie Robert, Caroline Michiels, Stefan Lanoy, Emilie affil: Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM Villejuif France sug: subj: Algorithms Data Analysis, Statistical Models, Statistical Computer Simulation Human Outcome Assessment Methods Time Factors Outcome Assessment Statistics and Numerical Data Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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