# Bayesian Methods for Comparative Effectiveness Research ## expand\_more ## Bayesian Methods for Comparative Effectiveness Research [5R01GM111339-04 ](https://reporter.nih.gov/search/BLI2dVl66UWzJl4f3OXliA/project-details/9415338) 03/2015 - 01/2020 **Specific Aims** Health information growth has created unprecedented opportunities to evaluate the effectiveness of therapies in large and broadly representative patient populations. Such evaluations are critical to understanding whether newly approved treatments, found to be efficacious in clinical trials, are safe and effective when applied in routine care. Extracting sound evidence from large observational data is now at the forefront of health care policy decisions. For instance, movement away from a strict biomedical perspective to one that is wider for coverage of new medical technologies has resulted in increasing use of coverage with evidence development, a mechanism that links financial support for newly approved medical technologies to evidence regarding those most likely to benefit. Statistical methods for the estimation of causal effects in large observational data are numerous, but substantial analytical challenges remain, especially when the number of patient-level covariates is very large (p). First, large administrative data now provide novel opportunities to study treatment effect heterogeneity, however, statistical methods for estimating average causal effects in sub-populations are scarce and underdeveloped in observational data (Aims 1 and 2). Second, rigorous methods for estimating causal effects rely on choosing the right approach for confounding adjustment, a daunting and unresolved task when dealing with a very large p and treatment effect heterogeneity (Aim 1). Third, selective inference – the process of drawing inference on a subset of parameters that is selected because the parameters seem interesting after viewing the data – is common with big data and can hamper the replicability of findings (Aim 1). Fourth, instrumental variables methods often assume constant treatment effects, which is unlikely to hold in many health services applications, and use of a single causal parameter when unmeasured confounders moderate treatment effects is limiting (Aim 2). We have at least three goals: a) development of new statistical methods that are scalable to very large p and will overcome the limitations listed above (Aims 1 and 2); b) creation of new knowledge in comparative effectiveness research (CER) for cardiovascular disease and cancer (Aim 3); and c) dissemination of methods with open source software and reproducible CER analyses (Aim 4). New methods will be validated with theoretical arguments and simulation studies. Our ongoing interdisciplinary collaborations, which provide a wealth of clinical knowledge and access to contemporary clinical registry, administrative, and clinical trial data, supply the substantive questions that motivate our methods development. As part of this proposal, we will also address pressing CER questions (Aim 3), providing tools so others can replicate findings (Aim 4). The Specific Aims are: **Aim 1:** To develop new Bayesian methods for causal inference in large observational data to: 1) estimate average causal effects accounting for model uncertainty in the specification of measured confounders; and 2) characterize heterogeneous treatment effects by estimating subgroup-specific causal effects while accounting for uncertainty in the subgroup identification.The new approaches will not condition on a single model, but rather average across multiple model specifications according to empirical support for confounding adjustment and existence of heterogeneous effects. **Aim 2:** To develop new Bayesian methods for assessing treatment effects in the presence of unmeasured confounders that moderate treatment effects. Using instrumental variables, we will 1) identify the distributions of essential causal parameters (e.g., average, local average, and marginal treatment effects) and 2) link these parameters to population subgroups by systematically relaxing selection bias assumptions. Our methods will provide a unified framework for estimation of key causal parameters and for empirically assessing assumptions required for inference. **Aim 3:** To apply the new methods to three observational studies and to randomized trial data to provide new and fully reproducible knowledge in the areas of new medical devices, surgical procedures, and pharmaceutical treatments in cardiovascular disease and cancer. **Aim 4:** To develop flexible, efficient, robust, well documented, user-friendly R libraries and SAS macros so newly developed methods can be disseminated and CER findings can be reproduced by others. Our new methods, their applications to large administrative and clinical registry data, and their dissemination will allow the entire research community to address modern CER questions with the highest methodological rigor. These methods will permit routine identification of population subgroups that are most likely to benefit from interventions, and therefore will have a direct and sustained impact on policy making and clinical practice. ## Presentations Adhikari S, Normand S-L T. Non-parametric Bayesian Instrumental Variable Analysis: Comparing effectiveness of radial vs femoral arteries access Percutaneous Coronary Intervention (PCI) in reducing bleeding and vascular complications. EMR-IBS and Italian Region Conference. May 2017. ## Publications Download 10 citations download- [BibTeX](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=tagged) - [Marc](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=marc) - [PubMedId](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=no_pager&number_of_items=12&sort_field=bibcite_year--desc&&content_filter%5B0%5D=682662&content_filter%5B1%5D=651186&content_filter%5B2%5D=677243&content_filter%5B3%5D=661415&content_filter%5B4%5D=637840&content_filter%5B5%5D=614551&content_filter%5B6%5D=612974&content_filter%5B7%5D=608931&content_filter%5B8%5D=614553&content_filter%5B9%5D=722101&&format=ris) ### 2021 Samrachana Adhikari, Sharon-Lise Normand, Jordan Bloom, David Shahian, and Sherri Rose. 2021. “[Revisiting Performance Metrics for Prediction With Rare Outcomes](/publications/revisiting-performance-metrics-prediction-rare-outcomes)”. Stat Methods Med Res, 30, 10, Pp. 2352-66. doi:10.1177/09622802211038754 Samrachana Adhikari, Sharon-Lise Normand, Jordan Bloom, David Shahian, and Sherri Rose. 2021. “[Revisiting Performance Metrics for Prediction With Rare Outcomes](/publications/revisiting-performance-metrics-prediction-rare-outcomes)”. Stat Methods Med Res, 30, 10, Pp. 2352-66. doi:10.1177/09622802211038754 - add\_circle do\_not\_disturb\_on Abstract Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under the... ### 2020 Yun Wang, Noel Eldridge, Mark Metersky, Nancy Sonnenfeld, David Rodrick, Jonathan Fine, Sheila Eckenrode, Deron Galusha, Anila Tasimi, David Hunt, Susannah Bernheim, Sharon-Lise Normand, and Harlan Krumholz. 2020. “[Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States](/publications/association-between-medicare-expenditures-and-adverse-events-patients-acute)”. JAMA Netw Open, 3, 4, Pp. e202142. doi:10.1001/jamanetworkopen.2020.2142 Yun Wang, Noel Eldridge, Mark Metersky, Nancy Sonnenfeld, David Rodrick, Jonathan Fine, Sheila Eckenrode, Deron Galusha, Anila Tasimi, David Hunt, Susannah Bernheim, Sharon-Lise Normand, and Harlan Krumholz. 2020. “[Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States](/publications/association-between-medicare-expenditures-and-adverse-events-patients-acute)”. JAMA Netw Open, 3, 4, Pp. e202142. doi:10.1001/jamanetworkopen.2020.2142 - add\_circle do\_not\_disturb\_on Abstract Importance: Studies have shown that adverse events are associated with increasing inpatient care expenditures, but contemporary data on the association between expenditures and adverse events beyond inpatient care are limited.Objective: To evaluate... Samrachana Adhikari, Sherri Rose, and Sharon-Lise Normand. 2020. “[Nonparametric Bayesian Instrumental Variable Analysis: Evaluating Heterogeneous Effects of Coronary Arterial Access Site Strategies](/publications/nonparametric-bayesian-instrumental-variable-analysis-evaluating-heterogeneous)”. J Am Stat Assoc, 115, 532, Pp. 1635-44. doi:10.1080/01621459.2019.1688663 Samrachana Adhikari, Sherri Rose, and Sharon-Lise Normand. 2020. “[Nonparametric Bayesian Instrumental Variable Analysis: Evaluating Heterogeneous Effects of Coronary Arterial Access Site Strategies](/publications/nonparametric-bayesian-instrumental-variable-analysis-evaluating-heterogeneous)”. J Am Stat Assoc, 115, 532, Pp. 1635-44. doi:10.1080/01621459.2019.1688663 - add\_circle do\_not\_disturb\_on Abstract Percutaneous coronary interventions (PCIs) are nonsurgical procedures to open blocked blood vessels to the heart, frequently using a catheter to place a stent. The catheter can be inserted into the blood vessels using an artery in the groin or an artery... ### 2019 Jacob Spertus, Marcela Horvitz-Lennon, and Sharon-Lise Normand. 2019. “[Bayesian Meta-Analysis of Multiple Continuous Treatments With Individual Participant-Level Data: An Application to Antipsychotic Drugs](/publications/bayesian-meta-analysis-multiple-continuous-treatments-individual-participant)”. Med Decis Making, 39, 5, Pp. 583-92. doi:10.1177/0272989X19856884 Jacob Spertus, Marcela Horvitz-Lennon, and Sharon-Lise Normand. 2019. “[Bayesian Meta-Analysis of Multiple Continuous Treatments With Individual Participant-Level Data: An Application to Antipsychotic Drugs](/publications/bayesian-meta-analysis-multiple-continuous-treatments-individual-participant)”. Med Decis Making, 39, 5, Pp. 583-92. doi:10.1177/0272989X19856884 - add\_circle do\_not\_disturb\_on Abstract Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose... Sarah Anoke, Sharon-Lise Normand, and Corwin Zigler. 2019. “[Approaches to Treatment Effect Heterogeneity in the Presence of Confounding](/publications/approaches-treatment-effect-heterogeneity-presence-confounding)”. Stat Med, 38, 15, Pp. 2797-2815. doi:10.1002/sim.8143 Sarah Anoke, Sharon-Lise Normand, and Corwin Zigler. 2019. “[Approaches to Treatment Effect Heterogeneity in the Presence of Confounding](/publications/approaches-treatment-effect-heterogeneity-presence-confounding)”. Stat Med, 38, 15, Pp. 2797-2815. doi:10.1002/sim.8143 - add\_circle do\_not\_disturb\_on Abstract The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a... ### 2018 Sherri Rose and Sharon-Lise Normand. 2018. “[Double Robust Estimation for Multiple Unordered Treatments and Clustered Observations: Evaluating Drug-Eluting Coronary Artery Stents](/publications/double-robust-estimation-multiple-unordered-treatments-and-clustered)”. Biometrics. doi:10.1111/biom.12927 Sherri Rose and Sharon-Lise Normand. 2018. “[Double Robust Estimation for Multiple Unordered Treatments and Clustered Observations: Evaluating Drug-Eluting Coronary Artery Stents](/publications/double-robust-estimation-multiple-unordered-treatments-and-clustered)”. Biometrics. doi:10.1111/biom.12927 - add\_circle do\_not\_disturb\_on Abstract Postmarket comparative effectiveness and safety analyses of therapeutic treatments typically involve large observational cohorts. We propose double robust machine learning estimation techniques for implantable medical device evaluations where there are... Sebastien Haneuse, José Zubizarreta, and Sharon-Lise Normand. 2018. “[Discussion on ‘Time-Dynamic Profiling With Application to Hospital Readmission Among Patients on Dialysis,’ by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar Kalantar-Zadeh, and Damla Senturk](/publications/discussion-time-dynamic-profiling-application-hospital-readmission-among)”. Biometrics. doi:10.1111/biom.12909 Sebastien Haneuse, José Zubizarreta, and Sharon-Lise Normand. 2018. “[Discussion on ‘Time-Dynamic Profiling With Application to Hospital Readmission Among Patients on Dialysis,’ by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar Kalantar-Zadeh, and Damla Senturk](/publications/discussion-time-dynamic-profiling-application-hospital-readmission-among)”. Biometrics. doi:10.1111/biom.12909 Jacob Spertus and Sharon-Lise Normand. 2018. “[Bayesian Propensity Scores for High-Dimensional Causal Inference: A Comparison of Drug-Eluting to Bare-Metal Coronary Stents](/publications/bayesian-propensity-scores-high-dimensional-causal-inference-comparison-drug)”. Biom J. doi:10.1002/bimj.201700305 Jacob Spertus and Sharon-Lise Normand. 2018. “[Bayesian Propensity Scores for High-Dimensional Causal Inference: A Comparison of Drug-Eluting to Bare-Metal Coronary Stents](/publications/bayesian-propensity-scores-high-dimensional-causal-inference-comparison-drug)”. Biom J. doi:10.1002/bimj.201700305 - add\_circle do\_not\_disturb\_on Abstract High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research... Jacob Spertus, Marcela Horvitz-Lennon, Haley Abing, and Sharon-Lise Normand. 2018. “[Risk of Weight Gain for Specific Antipsychotic Drugs: A Meta-Analysis](/publications/risk-weight-gain-specific-antipsychotic-drugs-meta-analysis)”. NPJ Schizophr, 4, 1, Pp. 12. doi:10.1038/s41537-018-0053-9 Jacob Spertus, Marcela Horvitz-Lennon, Haley Abing, and Sharon-Lise Normand. 2018. “[Risk of Weight Gain for Specific Antipsychotic Drugs: A Meta-Analysis](/publications/risk-weight-gain-specific-antipsychotic-drugs-meta-analysis)”. NPJ Schizophr, 4, 1, Pp. 12. doi:10.1038/s41537-018-0053-9 - add\_circle do\_not\_disturb\_on Abstract People with schizophrenia are at considerably higher risk of cardiometabolic morbidity than the general population. Second-generation antipsychotic drugs contribute to that risk partly through their weight gain effects, exacerbating an already high burden... ### 2016 Jacob Spertus V, Sharon-Lise T Normand, Robert Wolf, Matt Cioffi, Ann Lovett, and Sherri Rose. 2016. “[Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.](/publication/assessing-hospital-performance-after-percutaneous-coronary-intervention-using-big-data)”. Circulation. Cardiovascular Quality and Outcomes, 9, 6, Pp. 659-69. doi:10.1161/CIRCOUTCOMES.116.002826 Jacob Spertus V, Sharon-Lise T Normand, Robert Wolf, Matt Cioffi, Ann Lovett, and Sherri Rose. 2016. “[Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.](/publication/assessing-hospital-performance-after-percutaneous-coronary-intervention-using-big-data)”. Circulation. Cardiovascular Quality and Outcomes, 9, 6, Pp. 659-69. doi:10.1161/CIRCOUTCOMES.116.002826 - add\_circle do\_not\_disturb\_on Abstract BACKGROUND: Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly...