Supplementary materials: Assessing the performance of physician’s prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study

These are peer-reviewed supplementary materials for the article 'Supplementary materials: Assessing the performance of physician’s prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study' published in the Journal of Comparative Effectiveness Research.Table S1. The performance of IV estimates and OLS estimates in the samples size of 255Table S2. The performance of IV estimates and OLS estimates in the samples size of 620Table S3. The performance of IV estimates and OLS estimates in the sample sizes of 2452TableS4. The performance of IV estimates and OLS estimates in the samples size of 5869Table S5. The performance of ‘true’ prescribing preference in four sample sizesTable S6. The performance of proportional preference in four sample sizesR CodesAim: This simulation study is to assess the utility of physician’s prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.