Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of instrumental variables methods by overcoming three important barriers to adoption of these powerful methods for CER. Appropriate use of IV methods for CER hinges on selecting good instruments and appropriate estimation. A good instrument must 1) induce substantial variation in treatment choices (i.e. be """"""""strong"""""""") but 2) not affect outcomes except through treatment choices (i.e. the """"""""exclusion restriction""""""""). While the consequences of using weak instruments have been investigated, the consequences of violating the exclusion restriction are not well understood. Even under the traditional assumption of a homogenous treatment effect, several new IV approaches are being developed. Knowing which method is appropriate for any particular application remains challenging. The default has been to use two-stage least squares, but many situations common to CER require alternative approaches such as near-far matching or two-stage residual inclusion. This application aims to address these challenges with applying instrumental variables analysis with a goal of providing applied practitioners of CER with appropriate guidance. Results of IV analyses may be generalized to the wrong subpopulations if treatment effects are heterogeneous as these effects become dependent on the analyst's choice of IV(s) and are difficult to interpret for clinical and policy purposes. We will also develop novel IV approaches that address treatment effect heterogeneity and generate interpretable results for CER. Many current applications of CER do not take full advantage of recent IV methodological advances, due to unavailability of readily implementable software or statistical code, resulting in delays in the translation of the science of IV analysis to practice. Therefore, we will develop relevant statistical code to help practitioners implement these methods using common statistical software packages and illustrate the methods through empirical examples in prostate cancer and cardiovascular disease.

Public Health Relevance

Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of IV methods by overcoming three important barriers to adoption of these powerful methods for CER.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
High Impact Research and Research Infrastructure Programs—Multi-Yr Funding (RC4)
Project #
1RC4CA155809-01
Application #
8036881
Study Section
Special Emphasis Panel (ZRG1-HDM-C (56))
Program Officer
Mariotto, Angela B
Project Start
2010-09-28
Project End
2010-12-31
Budget Start
2010-09-28
Budget End
2010-12-31
Support Year
1
Fiscal Year
2010
Total Cost
$19,453
Indirect Cost
Name
University of Chicago
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Basu, Anirban (2015) Welfare implications of learning through solicitation versus diversification in health care. J Health Econ 42:165-73
Agapova, Maria; Duignan, Andrea; Smith, Alan et al. (2015) Long-term costs of introducing HPV-DNA post-treatment surveillance to national cervical cancer screening in Ireland. Expert Rev Pharmacoecon Outcomes Res 15:999-1005
Basu, Anirban; Gore, John L (2015) Are Elderly Patients With Clinically Localized Prostate Cancer Overtreated? Exploring Heterogeneity in Survival Effects. Med Care 53:79-86
Bekelman, Justin E; Mitra, Nandita; Handorf, Elizabeth A et al. (2015) Effectiveness of androgen-deprivation therapy and radiotherapy for older men with locally advanced prostate cancer. J Clin Oncol 33:716-22
Basu, Anirban; Jena, Anupam B; Goldman, Dana P et al. (2014) Heterogeneity in action: the role of passive personalization in comparative effectiveness research. Health Econ 23:359-73
Basu, Anirban; Chan, Kwun Chuen Gary (2014) Can we make smart choices between OLS and contaminated IV methods? Health Econ 23:462-72
Basu, Anirban (2014) ESTIMATING PERSON-CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS. J Appl Econ (Chichester Engl) 29:671-691
Borah, Bijan J; Basu, Anirban (2013) Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence. Health Econ 22:1052-70
Bekelman, Justin E; Handorf, Elizabeth A; Guzzo, Thomas et al. (2013) Radical cystectomy versus bladder-preserving therapy for muscle-invasive urothelial carcinoma: examining confounding and misclassification biasin cancer observational comparative effectiveness research. Value Health 16:610-8
Basu, Anirban; Manca, Andrea (2012) Regression estimators for generic health-related quality of life and quality-adjusted life years. Med Decis Making 32:56-69

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