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Instrumental variable analysis with a nonlinear exposure-outcome relationship.

ABSTRACT: BACKGROUND:Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. METHODS:Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure-outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. RESULTS:Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure-outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. CONCLUSIONS:Nonlinear exposure-outcome relations should not be a barrier to instrumental variable analyses. When the exposure-outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure-outcome relation can be estimated.

SUBMITTER: Burgess S

PROVIDER: S-EPMC4222800 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Instrumental variable analysis with a nonlinear exposure-outcome relationship.

Burgess Stephen S Davies Neil M NM Thompson Simon G SG

Epidemiology (Cambridge, Mass.) 20141101 6

<h4>Background</h4>Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables.<h4>Methods</h4>Using simulations, ...[more]

PMID: 25166881

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Project description:OBJECTIVES:To assess the incremental effects of adding extra antihypertensive drugs from a new class to a patient's regimen. DESIGN:Instrumental variable analysis of data from SPRINT (Systolic Blood Pressure Intervention Trial). To account for confounding by indication-when treatments seem less effective if they are administered to sicker patients-randomization status was used as the instrumental variable. Patients' randomization status was either intensive (systolic blood pressure target <120 mm Hg) or standard (systolic blood pressure target <140 mm Hg) treatment. Results from instrumental variable models were compared with those from standard multivariable models. SETTING:Secondary data analysis of a randomized clinical trial conducted at 102 sites in 2010-15. PARTICIPANTS:9092 SPRINT participants with hypertension and increased cardiovascular risk but no history of diabetes or stroke. MAIN OUTCOMES MEASURES:Systolic blood pressure, major cardiovascular events, and serious adverse events. RESULTS:In standard multivariable models not adjusted for confounding by indication, addition of an antihypertensive drug from a new class was associated with modestly lower systolic blood pressure (-1.3 mm Hg, 95% confidence interval -1.6 to -1.0) and no change in major cardiovascular events (absolute risk of events per 1000 patient years, 0.5, 95% confidence interval -1.5 to 2.3). In instrumental variable models, the addition of an antihypertensive drug from a new class led to clinically important reductions in systolic blood pressure (-14.4 mm Hg, -15.6 to -13.3) and fewer major cardiovascular events (absolute risk -6.2, -10.9 to -1.3). Incremental reductions in systolic blood pressure remained large and similar in magnitude for patients already taking drugs from zero, one, two, or three or more drug classes. This finding was consistent across all subgroups of patients. The addition of another antihypertensive drug class was not associated with adverse events in either standard or instrumental variable models. CONCLUSIONS:After adjustment for confounding by indication, the addition of a new antihypertensive drug class led to large reductions in systolic blood pressure and major cardiovascular events among patients at high risk for cardiovascular events but without diabetes. Effects on systolic blood pressure persisted across all levels of baseline drug use and all subgroups of patients.

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Instrumental variable analysis with a nonlinear exposure-outcome relationship.

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Instrumental variable analysis with a nonlinear exposure-outcome relationship.

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