Literature DB >> 30786103

Quantifying bias reduction with fixed-duration versus all-available covariate assessment periods.

John G Connolly1, Sebastian Schneeweiss1, Robert J Glynn1, Joshua J Gagne1.   

Abstract

PURPOSE: Implementing a cohort study in longitudinal healthcare databases requires looking back over some covariate assessment period (CAP) preceding cohort entry to measure confounders. We used simulations to compare fixed-duration versus all-available CAPs for confounder adjustment in the presence of differences in available baseline time between exposure groups.
METHODS: We simulated cohorts of 10 000 patients with binary variables for a single confounder, exposure, and outcome. Baseline time was simulated based on the observed distribution in a claims-based comparison of statin users versus nonusers. We compared bias after measuring confounders using fixed-duration and all-available CAPs, both when exposure groups had similar and discrepant amounts of available baseline time.
RESULTS: When the comparison groups had similar amounts of baseline time, an all-available CAP was less biased than a fixed-duration CAP. When baseline time differed between comparison groups, the preferable CAP approach depended on the direction of confounding and which exposure group had higher covariate sensitivity. These findings were consistent in direction across sensitivity analyses.
CONCLUSION: In certain settings of differential available baseline time between exposure groups, the all-available CAP was more biased than the fixed-duration CAP. The relative directions and strengths of confounding and misclassification biases are an important consideration when choosing between a fixed-duration or all-available CAP, but they are often unknown. Therefore, we recommend comparing the amount of available baseline time between exposure groups. When there is a large discrepancy, despite appropriate design choices, we recommend a fixed-duration approach to avoid potential increases in bias because of differential data availability.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  confounder misclassification; confounding; covariate assessment periods; lookback periods; misclassification; pharmacoepidemiology

Mesh:

Substances:

Year:  2019        PMID: 30786103     DOI: 10.1002/pds.4729

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


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