Apples and oranges? Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data

Gang Fang; John M Brooks; Elizabeth A Chrischilles

doi:10.1093/aje/kwr283

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Apples and oranges? Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data

Journal article

Open access

Peer reviewed

Apples and oranges? Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data

Gang Fang, John M Brooks and Elizabeth A Chrischilles

American journal of epidemiology, Vol.175(1), pp.60-65

01/01/2012

DOI: 10.1093/aje/kwr283

PMID: 22085626

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Abstract

Instrumental variable (IV) and risk adjustment (RA) estimators, including propensity score adjustments, are both used to alleviate confounding problems in nonexperimental studies on treatment effects, but it is not clear how estimates based on these 2 approaches compare. Methodological considerations have shown that IV and RA estimators yield estimates of distinct types of causal treatment effects regardless of confounding problems. Many investigators have neglected these distinctions. In this paper, the authors use 3 schematic models to explain visually the relations between IV and RA estimates of intended treatment effects as demonstrated in the methodological studies. When treatment effects are homogeneous across a study population or when treatment effects are heterogeneous across the study population but treatment decisions are unrelated to the treatment effects, RA and IV estimates should be equivalent when the respective assumptions are met. In contrast, when treatment effects are heterogeneous and treatment decisions are related to the treatment effects, RA estimates of treatment effect can asymptotically differ from IV estimates, but both are correct even when the respective assumptions are met. Appropriate interpretations of IV or RA estimates can be facilitated by developing conceptual models related to treatment choice and treatment effect heterogeneity prior to analyses.

Effect Modifier, Epidemiologic

Propensity Score

Data Interpretation, Statistical

Confounding Factors (Epidemiology)

Risk Adjustment

Observation

Models, Statistical

Causality

Details

Title: Subtitle: Apples and oranges? Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data
Creators: Gang Fang - Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7573, USA. gang_fang@unc.edu
John M Brooks
Elizabeth A Chrischilles
Resource Type: Journal article
Publication Details: American journal of epidemiology, Vol.175(1), pp.60-65
Publisher: United States
DOI: 10.1093/aje/kwr283
PMID: 22085626
ISSN: 1476-6256
eISSN: 1476-6256
Grant note: U18 HSO16094 / PHS HHS
Language: English
Date published: 01/01/2012
Academic Unit: Pharmacy; Epidemiology
Record Identifier: 9983995036302771

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