Time‐related biases in pharmacoepidemiology

Abstract Purpose Observational studies using computerized healthcare databases have become popular to investigate the potential effectiveness of old drugs for new indications. Many of these studies reporting remarkable effectiveness were shown to be affected by different time‐related biases. We describe these biases and illustrate their effects using a cohort of patients treated for chronic obstructive pulmonary disease (COPD). Methods The Quebec healthcare databases were used to form a cohort of 124 030 patients with COPD, 50 years or older, treated between 2000 and 2015. Inhaled corticosteroids (ICS) and long‐acting bronchodilators were used as exposures, with diverse outcomes, including lung cancer, acute myocardial infarction and death, to illustrate protopathic, latency time, immortal time, time‐window, depletion of susceptibles, and immeasurable time biases. Results Protopathic bias affected bronchodilator‐defined cohort entry with an incident rate of lung cancer of 23.9 per 1000 in the first year, compared with around 12.0 in the subsequent years. When latency and immortal times were misclassified, ICS were associated with decreased incidence of lung cancer (hazard ratio [HR] 0.32; 95% CI: 0.30‐0.34), compared with 0.50 (95% CI: 0.48‐0.53) after correcting for immortal time bias and 0.96 (95% CI: 0.91‐1.02) after also correcting for latency time bias. Time‐window, depletion of susceptibles and immeasurable time biases also affected the findings similarly. Conclusions Many observational studies of new indications for older drugs reporting unrealistic effectiveness were affected by avoidable time‐related biases. The apparent effectiveness often disappears with proper design and analysis. Future studies should consider these time‐related issues to avoid severely biased results.