Related Experiment Video
Updated: May 2, 2026
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Sequential cohort design applying propensity score matching to analyze the comparative effectiveness of atorvastatin
Arja Helin-Salmivaara1, Piia Lavikainen2, Emma Aarnio3
1Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland; Unit of Primary Health Care, Hospital District of Helsinki and Uusimaa, Helsinki, Finland.
Insights
Sequential cohort design (SCD) with propensity score (PS) matching effectively analyzed atorvastatin versus simvastatin for cardiovascular event (CVE) prevention. Both methods showed similar results, with atorvastatin reducing CVE incidence by 11-12%.
Area of Science:
- Pharmacoeconomics and Outcomes Research
- Epidemiology
- Biostatistics
Background:
- Channeling bias can confound comparative effectiveness research when calendar time is a proxy for patient or physician preferences.
- Sequential cohort design (SCD) with propensity score (PS) matching is a proposed method to mitigate this bias.
- This study investigated channeling of atorvastatin and simvastatin in Finland and applied the SCD PS approach to assess their comparative effectiveness in preventing cardiovascular events (CVEs).
Purpose of the Study:
- To evaluate the comparative effectiveness of atorvastatin versus simvastatin in preventing cardiovascular events (CVEs).
- To explore the utility of the sequential cohort design (SCD) with propensity score (PS) matching for analyzing real-world data and mitigating channeling bias.
Main Methods:
- Patients aged 45-75 initiating atorvastatin or simvastatin between 1998-2006 in Finland were identified.
- Propensity scores (PS) for receiving atorvastatin over simvastatin were estimated for 17 six-month periods.
- Atorvastatin (10 mg) and simvastatin (20 mg) initiators were matched 1:1 on PS, both across the entire cohort and within each sequential period. Cox regression models were used to estimate hazard ratios (HR) for CVEs.
Main Results:
- Atorvastatin (10 mg) was associated with an 11%-12% lower incidence of CVEs compared to simvastatin (20 mg).
- Hazard ratio (HR) estimates for CVEs were consistent across different analytical approaches: conventional Cox model (0.88), PS matching across all periods with confounder adjustment (0.89), and SCD PS matching within sequential periods (0.88).
- A traditional PS matched analysis without further adjustment yielded a different HR (0.80).
Conclusions:
- The SCD PS approach yielded effect estimates comparable to traditional PS matching with confounder adjustment, suggesting its validity in addressing channeling bias.
- While effective, the SCD PS method may be less efficient than traditional methods.
- Traditional PS matching without outcome model adjustment produced estimates that deviated further from the null hypothesis.
Background:
Sequential cohort design (SCD) applying matching for propensity scores (PS) in accrual periods has been proposed to mitigate bias caused by channeling when calendar time is a proxy for strong confounders. We studied the channeling of patients according to atorvastatin and simvastatin initiation in Finland, starting from the market introduction of atorvastatin in 1998, and explored the SCD PS approach to analyzing the comparative effectiveness of atorvastatin versus simvastatin in the prevention of cardiovascular events (CVE).
Methods:
Initiators of atorvastatin or simvastatin use in the 45-75-year age range in 1998-2006 were characterized by their propensity of receiving atorvastatin over simvastatin, as estimated for 17 six-month periods. Atorvastatin (10 mg) and simvastatin (20 mg) initiators were matched 1∶1 on the PS, as estimated for the whole cohort and within each period. Cox regression models were fitted conventionally, and also for the PS matched cohort and the periodically PS matched cohort, to estimate the hazard ratios (HR) for CVEs.
Findings:
Atorvastatin (10 mg) was associated with a 11%-12% lower incidence of CVE in comparison with simvastatin (20 mg). The HR estimates were the same for a conventional Cox model (0.88, 95% confidence interval 0.85-0.91), for the analysis in which the PS was used to match across all periods and the Cox model was adjusted for strong confounders (0.89, 0.85-0.92), and for the analysis in which PS matching was applied within sequential periods (0.88, 0.84-0.92). The HR from a traditional PS matched analysis was 0.80 (0.77-0.83).
Conclusions:
The SCD PS approach produced effect estimates similar to those obtained in matching for PS within the whole cohort and adjusting the outcome model for strong confounders, but at the cost of efficiency. A traditional PS matched analysis without further adjustment in the outcome model produced estimates further away from unity.
Related Concept Videos
01:20Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
01:15Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
01:15Bioavailability Study Design: Healthy Subjects Versus Patients
01:26Coronary Artery Disease IV: Preventive Measures
01:20Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...