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Instrumental variable with competing risk model.

ABSTRACT: In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.

SUBMITTER: Zheng C

PROVIDER: S-EPMC5479873 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Instrumental variable with competing risk model.

Zheng Cheng C Dai Ran R Hari Parameswaran N PN Zhang Mei-Jie MJ

Statistics in medicine 20170108 8

In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semip ...[more]

PMID: 28064466

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Project description:ImportanceLow academic achievement (AA) in childhood and adolescence is associated with increased substance use. Empirical evidence, using longitudinal epidemiologic data, may provide support for interventions to improve AA as a means to reduce risk of drug abuse (DA).ObjectiveTo clarify the nature of the association between adolescent AA and risk of DA by using instrumental variable and co-relative analysis designs.Design, setting, and participantsThis study, assessing nationwide data from individuals born in Sweden between 1971 and 1982, used instrumental variable and co-relative analyses of the association between AA and DA. The instrument was month of birth. Co-relative analyses were conducted in pairs of cousins (263 222 pairs), full siblings (154 295), and monozygotic twins (1623) discordant for AA, with raw results fitted to a genetic model. The AA-DA association was modeled using Cox regression. Data analysis was conducted from October 2017 to January 2018.ExposuresAcademic achievement assessed at 16 years of age (for instrumental variable analyses), and estimated discordance in AA in pairs of monozygotic twins (for co-relative analyses).Main outcomes and measuresDrug abuse registration in national medical, criminal, or pharmacy registries.ResultsThis instrumental variable analysis included 934 462 participants (478 341 males and 456 121 females) with a mean (SD) age of 34.7 (4.3) years at a mean follow-up of 19 years. Earlier month of birth was associated with a linear effect on AA, with the regression coefficient per month equaling -0.0225 SDs (95% CI, -0.0231 to -0.0219). Controlling for AA, month of birth had no association with risk of DA (hazard ratio [HR], 1.000; 95% CI, 0.997-1.004). Lower AA had a significant association with risk of subsequent DA registration (HR per SD, 2.33; 95% CI, 2.30-2.35). Instrumental variable analysis produced a substantial but modestly attenuated association (HR, 2.04; 95% CI, 1.75-2.33). Controlling for modest associations between month of birth and parental educational status and DA risk reduced the association to a HR of 1.92 (95% CI, 1.67-2.22). The genetic model applied to the results of co-relative analyses fitted the observed data well and estimated the AA-DA association in monozygotic twins discordant for AA to equal a HR of 1.79 (95% CI, 1.64-1.92).Conclusions and relevanceTwo different methodological approaches with divergent assumptions both produced results consistent with the hypothesis that the significant association observed between AA at 16 years of age and risk of DA into middle adulthood may be causal. These results provide empirical support for efforts to improve AA as a means to reduce risk of DA.

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Instrumental variable with competing risk model.

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Instrumental variable with competing risk model.

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