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Bias from outcome misclassification in immunization schedule safety research.

ABSTRACT: PURPOSE:The Institute of Medicine recommended conducting observational studies of childhood immunization schedule safety. Such studies could be biased by outcome misclassification, leading to incorrect inferences. Using simulations, we evaluated (1) outcome positive predictive values (PPVs) as indicators of bias of an exposure-outcome association, and (2) quantitative bias analyses (QBA) for bias correction. METHODS:Simulations were conducted based on proposed or ongoing Vaccine Safety Datalink studies. We simulated 4 studies of 2 exposure groups (children with no vaccines or on alternative schedules) and 2 baseline outcome levels (100 and 1000/100 000 person-years), with 3 relative risk (RR) levels (RR = 0.50, 1.00, and 2.00), across 1000 replications using probabilistic modeling. We quantified bias from non-differential and differential outcome misclassification, based on levels previously measured in database research (sensitivity > 95%; specificity > 99%). We calculated median outcome PPVs, median observed RRs, Type 1 error, and bias-corrected RRs following QBA. RESULTS:We observed PPVs from 34% to 98%. With non-differential misclassification and true RR = 2.00, median bias was toward the null, with severe bias (median observed RR = 1.33) with PPV = 34% and modest bias (median observed RR = 1.83) with PPV = 83%. With differential misclassification, PPVs did not reflect median bias, and there was Type 1 error of 100% with PPV = 90%. QBA was generally effective in correcting misclassification bias. CONCLUSIONS:In immunization schedule studies, outcome misclassification may be non-differential or differential to exposure. Overall outcome PPVs do not reflect the distribution of false positives by exposure and are poor indicators of bias in individual studies. Our results support QBA for immunization schedule safety research.

SUBMITTER: Newcomer SR

PROVIDER: S-EPMC5800415 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Bias from outcome misclassification in immunization schedule safety research.

Newcomer Sophia R SR Kulldorff Martin M Xu Stan S Daley Matthew F MF Fireman Bruce B Lewis Edwin E Glanz Jason M JM

Pharmacoepidemiology and drug safety 20180102 2

<h4>Purpose</h4>The Institute of Medicine recommended conducting observational studies of childhood immunization schedule safety. Such studies could be biased by outcome misclassification, leading to incorrect inferences. Using simulations, we evaluated (1) outcome positive predictive values (PPVs) as indicators of bias of an exposure-outcome association, and (2) quantitative bias analyses (QBA) for bias correction.<h4>Methods</h4>Simulations were conducted based on proposed or ongoing Vaccine S ...[more]

PMID: 29292551

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Project description:BackgroundThe diagnostic likelihood ratio (DLR) and its utility are well-known in the field of medical diagnostic testing. However, its use has been limited in the context of an outcome validation study. We considered that wider recognition of the utility of DLR would enhance the practices surrounding database studies. This is particularly timely and important since the use of healthcare-related databases for pharmacoepidemiology research has greatly expanded in recent years. In this paper, we aimed to advance the use of DLR, focusing on the planning of a new database study.MethodsTheoretical frameworks were developed for an outcome validation study and a comparative cohort database study; these two were combined to form the overall relationship. Graphical presentations based on these relationships were used to examine the implications of validation study results on the planning of a database study. Additionally, novel uses of graphical presentations were explored using some examples.ResultsPositive DLR was identified as a pivotal parameter that connects the expected positive-predictive value (PPV) with the disease prevalence in the planned database study, where the positive DLR is equal to sensitivity/(1-specificity). Moreover, positive DLR emerged as a pivotal parameter that links the expected risk ratio with the disease risk of the control group in the planned database study. In one example, graphical presentations based on these relationships provided a transparent and informative summary of multiple validation study results. In another example, the potential use of a graphical presentation was demonstrated in selecting a range of positive DLR values that best represented the relevant validation studies.ConclusionsInclusion of the DLR in the results section of a validation study would benefit potential users of the study results. Furthermore, investigators planning a database study can utilize the DLR to their benefit. Wider recognition of the full utility of the DLR in the context of a validation study would contribute meaningfully to the promotion of good practice in planning, conducting, analyzing, and interpreting database studies.

Project description:Background The bias implications of outcome misclassification arising from imperfect capture of mortality in claims-based studies are not well understood. Methods and Results We identified 2 cohorts of patients: (1) type 2 diabetes mellitus (n=8.6 million), and (2) heart failure (n=3.1 million), from Medicare claims (2012-2016). Within the 2 cohorts, mortality was identified from claims using the following approaches: (1) all-place all-cause mortality, (2) in-hospital all-cause mortality, (3) all-place cardiovascular mortality (based on diagnosis codes for a major cardiovascular event within 30 days of death date), or (4) in-hospital cardiovascular mortality, and compared against National Death Index identified mortality. Empirically identified sensitivity and specificity based on observed values in the 2 cohorts were used to conduct Monte Carlo simulations for treatment effect estimation under differential and nondifferential misclassification scenarios. From National Death Index, 1 544 805 deaths (549 996 [35.6%] cardiovascular deaths) in the type 2 diabetes mellitus cohort and 1 175 202 deaths (523 430 [44.5%] cardiovascular deaths) in the heart failure cohort were included. Sensitivity was 99.997% and 99.207% for the all-place all-cause mortality approach, whereas it was 27.71% and 33.71% for the in-hospital all-cause mortality approach in the type 2 diabetes mellitus and heart failure cohorts, respectively, with perfect positive predicted values. For all-place cardiovascular mortality, sensitivity was 52.01% in the type 2 diabetes mellitus cohort and 53.83% in the heart failure cohort with positive predicted values of 49.98% and 54.45%, respectively. Simulations suggested a possibility for substantial bias in treatment effects. Conclusions Approaches to identify mortality from claims had variable performance compared with the National Death Index. Investigators should anticipate the potential for bias from outcome misclassification when using administrative claims to capture mortality.

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Bias from outcome misclassification in immunization schedule safety research.

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Bias from outcome misclassification in immunization schedule safety research.

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