Journal of clinical epidemiology · 2020
New methodological approaches were able to effectively reduce immeasurable time bias in case-only designs.
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Jeong Han Eol, Oh In-Sun, Lee Hyesung, Filion Kristian B, Shin Ju-Young
Аннотация
OBJECTIVES: The objective of this study was to assess approaches to reduce immeasurable time bias in case-crossover (CCO), case-time-control (CTC), and case-case-time-control (CCTC) designs.
STUDY DESIGN AND SETTING: We used Korea's health care database that has inpatient and outpatient prescriptions and an empirical example of benzodiazepines and mortality among the elderly. We defined our unbiased exposure setting using all prescriptions and a pseudo-outpatient setting using outpatient records only. In the pseudo-outpatient setting, we assessed 10 approaches of restricting, adjusting, stratifying, or weighting on hospitalization-related factors. We conducted conditional logistic regression to estimate odds ratio (OR) with 95% confidence intervals (CI), where an approach was considered effective when its OR was within the unbiased exposure setting OR's 95% CI.
RESULTS: Immeasurable time bias negatively biased the unbiased exposure setting's OR in all three case-only designs, overestimating the protective effect of benzodiazepines on mortality. Of the 10 approaches examined, stratifying the proportion of hospitalized time in 0.01 intervals most effectively repaired the bias in the CCO (OR 1.25, 95% CI 1.10-1.43) and CTC analyses (1.11, 0.95-1.30); no approach was effective in the CCTC analysis.
CONCLUSION: Stratifying the proportion of hospitalized time in 0.01 intervals best approximated the unbiased exposure setting estimate by overcoming the significant impact of immeasurable time bias in CCO and CTC designs.
STUDY DESIGN AND SETTING: We used Korea's health care database that has inpatient and outpatient prescriptions and an empirical example of benzodiazepines and mortality among the elderly. We defined our unbiased exposure setting using all prescriptions and a pseudo-outpatient setting using outpatient records only. In the pseudo-outpatient setting, we assessed 10 approaches of restricting, adjusting, stratifying, or weighting on hospitalization-related factors. We conducted conditional logistic regression to estimate odds ratio (OR) with 95% confidence intervals (CI), where an approach was considered effective when its OR was within the unbiased exposure setting OR's 95% CI.
RESULTS: Immeasurable time bias negatively biased the unbiased exposure setting's OR in all three case-only designs, overestimating the protective effect of benzodiazepines on mortality. Of the 10 approaches examined, stratifying the proportion of hospitalized time in 0.01 intervals most effectively repaired the bias in the CCO (OR 1.25, 95% CI 1.10-1.43) and CTC analyses (1.11, 0.95-1.30); no approach was effective in the CCTC analysis.
CONCLUSION: Stratifying the proportion of hospitalized time in 0.01 intervals best approximated the unbiased exposure setting estimate by overcoming the significant impact of immeasurable time bias in CCO and CTC designs.