Grant Number:	7RC4CA155809-02 Interpret this number
Primary Investigator:	Basu, Anirban
Organization:	University Of Washington
Project Title:	Advancing Instrumental Variable Methods in Comparative Effectiveness Research
Fiscal Year:	2010

DESCRIPTION (provided by applicant): 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.




2SLS versus 2SRI: Appropriate methods for rare outcomes and/or rare exposures.
Authors: Basu A. , Coe N.B. , Chapman C.G. .
Source: Health Economics, 2018 06; 27(6), p. 937-955.
EPub date: 2018-03-26 00:00:00.0.
PMID: 29577493
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Heterogeneity in the impact of type of schooling on adult health and lifestyle.
Authors: Basu A. , Jones A.M. , Dias P.R. .
Source: Journal Of Health Economics, 2017-11-24 00:00:00.0; 57, p. 1-14.
EPub date: 2017-11-24 00:00:00.0.
PMID: 29179025
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Welfare implications of learning through solicitation versus diversification in health care.
Authors: Basu A. .
Source: Journal Of Health Economics, 2015 Jul; 42, p. 165-73.
PMID: 25966453
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Effectiveness of androgen-deprivation therapy and radiotherapy for older men with locally advanced prostate cancer.
Authors: Bekelman J.E. , Mitra N. , Handorf E.A. , Uzzo R.G. , Hahn S.A. , Polsky D. , Armstrong K. .
Source: Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology, 2015-03-01 00:00:00.0; 33(7), p. 716-22.
EPub date: 2015-03-01 00:00:00.0.
PMID: 25559808
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Are Elderly Patients With Clinically Localized Prostate Cancer Overtreated? Exploring Heterogeneity in Survival Effects.
Authors: Basu A. , Gore J.L. .
Source: Medical Care, 2015 Jan; 53(1), p. 79-86.
PMID: 25397964
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Long-term costs of introducing HPV-DNA post-treatment surveillance to national cervical cancer screening in Ireland.
Authors: Agapova M. , Duignan A. , Smith A. , O'Neill C. , Basu A. .
Source: Expert Review Of Pharmacoeconomics & Outcomes Research, 2015; 15(6), p. 999-1005.
PMID: 26377838
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Can we make smart choices between OLS and contaminated IV methods?
Authors: Basu A. , Chan K.C. .
Source: Health Economics, 2014 Apr; 23(4), p. 462-72.
PMID: 23765683
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Heterogeneity in action: the role of passive personalization in comparative effectiveness research.
Authors: Basu A. , Jena A.B. , Goldman D.P. , Philipson T.J. , Dubois R. .
Source: Health Economics, 2014 Mar; 23(3), p. 359-73.
PMID: 24123568
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Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence.
Authors: Borah B.J. , Basu A. .
Source: Health Economics, 2013 Sep; 22(9), p. 1052-70.
PMID: 23616446
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Radical cystectomy versus bladder-preserving therapy for muscle-invasive urothelial carcinoma: examining confounding and misclassification biasin cancer observational comparative effectiveness research.
Authors: Bekelman J.E. , Handorf E.A. , Guzzo T. , Evan Pollack C. , Christodouleas J. , Resnick M.J. , Swisher-McClure S. , Vaughn D. , Ten Have T. , Polsky D. , et al. .
Source: Value In Health : The Journal Of The International Society For Pharmacoeconomics And Outcomes Research, 2013 Jun; 16(4), p. 610-8.
PMID: 23796296
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Patient-centered or 'central' patient: Raising the veil of ignorance over randomization.
Authors: Basu A. .
Source: Statistics In Medicine, 2012-11-10 00:00:00.0; 31(25), p. 3057-9; discussion 3066-7.
PMID: 23055182
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Private manufacturers' thresholds to invest in comparative effectiveness trials.
Authors: Basu A. , Meltzer D. .
Source: Pharmacoeconomics, 2012-10-01 00:00:00.0; 30(10), p. 859-68.
PMID: 22901018
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Tying comparative effectiveness information to decision-making and the future of comparative effectiveness research designs: the case for antipsychotic drugs.
Authors: Basu A. , Meltzer H.Y. .
Source: Journal Of Comparative Effectiveness Research, 2012 Mar; 1(2), p. 171-80.
PMID: 24237376
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Regression estimators for generic health-related quality of life and quality-adjusted life years.
Authors: Basu A. , Manca A. .
Source: Medical Decision Making : An International Journal Of The Society For Medical Decision Making, 2012 Jan-Feb; 32(1), p. 56-69.
PMID: 22009667
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Estimating Decision-Relevant Comparative Effects Using Instrumental Variables.
Authors: Basu A. .
Source: Statistics In Biosciences, 2011 Sep; 3(1), p. 6-27.
PMID: 22010051
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ESTIMATING TREATMENT EFFECTS ON HEALTHCARE COSTS UNDER EXOGENEITY: IS THERE A 'MAGIC BULLET'?
Authors: Basu A. , Polsky D. , Manning W.G. .
Source: Health Services & Outcomes Research Methodology, 2011-07-01 00:00:00.0; 11(1-2), p. 1-26.
PMID: 22199462
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ESTIMATING PERSON-CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS.
Authors: Basu A. .
Source: Journal Of Applied Econometrics (chichester, England), 2014 June/July; 29(4), p. 671-691.
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