Abstract
BackgroundAttrition due to death and non-attendance are common sources of bias in studies of age-related diseases. A simulation study is presented to compare two methods for estimating the survivor average causal effect (SACE) of a binary exposure (sex-specific dietary iron intake) on a binary outcome (age-related macular degeneration, AMD) in this setting.MethodsA dataset of 10,000 participants was simulated 1200 times under each scenario with outcome data missing dependent on measured and unmeasured covariates and survival. Scenarios differed by the magnitude and direction of effect of an unmeasured confounder on both survival and the outcome, and whether participants who died following a protective exposure would also die if they had not received the exposure (validity of the monotonicity assumption). The performance of a marginal structural model (MSM, weighting for exposure, survival and missing data) was compared to a sensitivity approach for estimating the SACE. As an illustrative example, the SACE of iron intake on AMD was estimated using data from 39,918 participants of the Melbourne Collaborative Cohort Study.ResultsThe MSM approach tended to underestimate the true magnitude of effect when the unmeasured confounder had opposing directions of effect on survival and the outcome. Overestimation was observed when the unmeasured confounder had the same direction of effect on survival and the outcome. Violation of the monotonicity assumption did not increase bias. The estimates were similar between the MSM approach and the sensitivity approach assessed at the sensitivity parameter of 1 (assuming no survival bias). In the illustrative example, high iron intake was found to be protective of AMD (adjusted OR 0.57, 95% CI 0.40–0.82) using complete case analysis via traditional logistic regression. The adjusted SACE odds ratio did not differ substantially from the complete case estimate, ranging from 0.54 to 0.58 for each of the SACE methods.ConclusionsOn average, MSMs with weighting for exposure, missing data and survival produced biased estimates of the SACE in the presence of an unmeasured survival-outcome confounder. The direction and magnitude of effect of unmeasured survival-outcome confounders should be considered when assessing exposure-outcome associations in the presence of attrition due to death.
Highlights
Attrition due to death and non-attendance are common sources of bias in studies of age-related diseases
We examine the causal effect of dietary iron intake on age-related macular degeneration (AMD)
In this paper we present a simulation study to compare the performance of two methods employed to estimate the survivor average causal effect (SACE) in the presence of missing binary outcome data due to death and loss to follow-up
Summary
Attrition due to death and non-attendance are common sources of bias in studies of age-related diseases. A simulation study is presented to compare two methods for estimating the survivor average causal effect (SACE) of a binary exposure (sex-specific dietary iron intake) on a binary outcome (age-related macular degeneration, AMD) in this setting. Attrition due to death and loss to follow-up are two major potential sources of bias in observational studies which investigate diseases of ageing. Statistical methods have been proposed to estimate exposure-outcome effects in the presence of each attrition scenario separately, little is known about how the methods compare when both sources of attrition are present. We examine the causal effect of dietary iron intake on age-related macular degeneration (AMD). Despite evidence of elevated levels of iron in the retinal tissue of individuals with AMD, there is little evidence to suggest a link between dietary iron intake levels and the development of AMD [4]
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