Dataset Information

Impact of Selection Bias on Estimation of Subsequent Event Risk.

ABSTRACT: BACKGROUND:Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown. METHODS AND RESULTS:We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was <10, bias was usually <10% (downward toward the null). Despite the low bias, the probability that a confidence interval included the true effect decreased (undercoverage) with increasing sample size because of increasing precision. Importantly, false-positive rates were not affected by selection bias. CONCLUSIONS:In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.

SUBMITTER: Hu YJ

PROVIDER: S-EPMC5659743 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Impact of Selection Bias on Estimation of Subsequent Event Risk.

Hu Yi-Juan YJ Schmidt Amand F AF Dudbridge Frank F Holmes Michael V MV Brophy James M JM Tragante Vinicius V Li Ziyi Z Liao Peizhou P Quyyumi Arshed A AA McCubrey Raymond O RO Horne Benjamin D BD Hingorani Aroon D AD Asselbergs Folkert W FW Patel Riyaz S RS Long Qi Q

Circulation. Cardiovascular genetics 20171001 5

<h4>Background</h4>Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown.<h4>Methods and results</h4>We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent ev ...[more]

PMID: 28986451

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Project description:BACKGROUND:Regional prevalence estimation requires epidemiologic data with substantial local detail. National health surveys may lack in sufficient local observations due to limited resources. Therefore, corresponding prevalence estimates may not capture regional morbidity patterns with the necessary accuracy. Health insurance records represent alternative data sources for this purpose. Fund-specific member populations have more local observations than surveys, which benefits regional prevalence estimation. However, due to national insurance market regulations, insurance membership can be informative for morbidity. Regional fund-specific prevalence proportions are selective in the sense that the morbidity structure of a fund's member population cannot be extrapolated to the national population. This implies a selection bias that marks a major obstacle for statistical inference. We provide a methodology to adjust fund-specific selectivity and perform regional prevalence estimation from health insurance records. The methodology is applied to estimate regional cohort-referenced diabetes mellitus type 2 prevalence in Germany. METHODS:Records of the German Public Health Insurance Company from 2014 and Diagnosis-Related Group Statistics data are combined within a benchmarked multi-level model. The fund-specific selectivity is adjusted in a two-step procedure. Firstly, the conditional expectation of the insurance company's regional prevalence given related inpatient diagnosis frequencies of its members is quantified. Secondly, the regional prevalence is estimated by extrapolating the conditional expectation using corresponding inpatient diagnosis frequencies of the Diagnosis-Related Group Statistics as benchmarks. Model assumptions are validated via Monte Carlo simulation. Variable selection is performed via multivariate methods. The optimal model fit is determined by analysis of variance. 95% confidence intervals for the estimates are constructed via semiparametric bootstrapping. RESULTS:The national diabetes mellitus type 2 prevalence is estimated at 8.70% with a 95% confidence interval of [8.48%, 9.35%]. This indicates an adjustment of the original fund-specific prevalence from -?32.79 to -?25.93%. The estimated disease distribution shows significant morbidity differences between regions, especially between eastern and western Germany. However, the cohort-referenced estimates suggest that these differences can be partially explained by regional demography. CONCLUSIONS:The proposed methodology allows regional prevalence estimation in remarkable detail despite fund-specific selectivity. This enhances and encourages the use of health insurance records for future epidemiologic studies.

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Impact of Selection Bias on Estimation of Subsequent Event Risk.

Publications

Impact of Selection Bias on Estimation of Subsequent Event Risk.

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