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Estimating the Causal Effect of Treatment in Observational Studies with Survival Time Endpoints and Unmeasured Confounding.

ABSTRACT: Estimation of the effect of a treatment in the presence of unmeasured confounding is a common objective in observational studies. The Two Stage Least Squares (2SLS) Instrumental Variables (IV) procedure is frequently used but is not applicable to time-to-event data if some observations are censored. We develop a simultaneous equations model (SEM) to account for unmeasured confounding of the effect of treatment on survival time subject to censoring. The identification of the treatment effect is assisted by IVs (variables related to treatment but conditional on treatment not to the outcome) and the assumed bivariate distribution underlying the data generating process. The methodology is illustrated on data from an observational study of time to death following endovascular or open repair of ruptured abdominal aortic aneurysm. As the IV and the distributional assumptions cannot be jointly assessed from the observed data, we evaluate the sensitivity of the results to these assumptions.

SUBMITTER: Choi J

PROVIDER: S-EPMC6345269 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Estimating the Causal Effect of Treatment in Observational Studies with Survival Time Endpoints and Unmeasured Confounding.

Choi Jaeun J O'Malley A James AJ

Journal of the Royal Statistical Society. Series C, Applied statistics 20160627 1

Estimation of the effect of a treatment in the presence of unmeasured confounding is a common objective in observational studies. The Two Stage Least Squares (2SLS) Instrumental Variables (IV) procedure is frequently used but is not applicable to time-to-event data if some observations are censored. We develop a simultaneous equations model (SEM) to account for unmeasured confounding of the effect of treatment on survival time subject to censoring. The identification of the treatment effect is a ...[more]

PMID: 30686843

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Project description:BackgroundAlthough it has been established that low socioeconomic status is linked to increased risk of death after stroke, the mechanisms behind this link are still unclear. In this study we aim to shed light on the relationship between income level and survival after stroke by investigating the extent to which differences in stroke severity account for differences in survival.MethodsThe study was based on patients registered in Riksstroke (the Swedish stroke register) with first time ischemic stroke (n = 51,159) or intracerebral hemorrhage (n = 6777) in 2009-2012. We used causal mediation analysis to decompose the effect of low income on 3-month case fatality into a direct effect and an indirect effect due to stroke severity. Since causal mediation analysis relies on strong assumptions regarding residual confounding of the relationships involved, recently developed methods for sensitivity analysis were used to assess the robustness of the results to unobserved confounding.ResultsAfter adjustment for observed confounders, patients in the lowest income tertile had a 3.2% (95% CI: 0.9-5.4%) increased absolute risk of 3-month case fatality after intracerebral hemorrhage compared to patients in the two highest tertiles. The corresponding increase for case fatality after ischemic stroke was 1% (0.4-1.5%). The indirect effect of low income, mediated by stroke severity, was 1.8% (0.7-2.9%) for intracerebral hemorrhage and 0.4% (0.2-0.6%) for ischemic stroke. Unobserved confounders affecting the risk of low income, more severe stroke and case fatality in the same directions could explain the indirect effect, but additional adjustment to observed confounders did not alter the conclusions.ConclusionsThis study provides evidence that as much as half of income-related inequalities in stroke case fatality is mediated through differences in stroke severity. Targeting stroke severity could therefore lead to a substantial reduction in inequalities and should be prioritized. Sensitivity analysis suggests that additional adjustment for a confounder of greater impact than age would be required to considerably alter our conclusions.

Project description:Numerous observational studies suggest that bisphosphonates reduce mortality. This study showed that bisphosphonate use is associated with lower mortality within days of treatment, although the association was not significant until the second week. Such an early association is consistent with confounding, although an early treatment effect cannot be ruled out.IntroductionThe purpose of this study was to examine whether confounding explains why numerous observational studies show that bisphosphonate use is associated with lower mortality. To this end, we examined how soon after treatment initiation a lower mortality rate can be observed. We hypothesized that, due to confounding, the association would be observed immediately.MethodsThis was a retrospective cohort study of hip fracture patients discharged from Swedish hospitals between 1 July 2006 and 31 December 2015. The data covered 260,574 hip fracture patients and were obtained from the Swedish Hip Fracture Register and national registers. Of the 260,574 patients, 49,765 met all eligibility criteria and 10,178 were pair matched (bisphosphonate users to controls) using time-dependent propensity scores. The matching variables were age, sex, diagnoses, prescription medications, type of hip fracture, type of surgical procedure, known or suspected dementia, and physical functioning status.ResultsOver a median follow-up of 2.8 years, 2922 of the 10,178 matched patients died. The mortality rate was 7.9 deaths per 100 person-years in bisphosphonate users and 9.4 deaths in controls, which corresponded to a 15% lower mortality rate in bisphosphonate users (hazard ratio 0.85, 95% confidence interval 0.79-0.91). The risk of death was lower in bisphosphonate users from day 6 of treatment, although the association was not significant until the second week.ConclusionBisphosphonate use was associated with lower mortality within days of treatment initiation. This finding is consistent with confounding, although an early treatment effect cannot be ruled out.

Dataset Information

Estimating the Causal Effect of Treatment in Observational Studies with Survival Time Endpoints and Unmeasured Confounding.

Publications

Estimating the Causal Effect of Treatment in Observational Studies with Survival Time Endpoints and Unmeasured Confounding.

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