Article

Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results

Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
The International Journal of Biostatistics (Impact Factor: 0.74). 02/2007; 3(1):Article 14. DOI: 10.2202/1557-4679.1072
Source: PubMed

ABSTRACT

Observational studies of drugs and medical procedures based on administrative data are increasingly used to inform regulatory and clinical decisions. However, the validity of such studies is often questioned because available data may not contain measurements of many important prognostic variables that guide treatment decisions. Recently, approaches to this problem have been proposed that use instrumental variables (IV) defined at the level of an individual health care provider or aggregation of providers. Implicitly, these approaches attempt to estimate causal effects by using differences in medical practice patterns as a quasi-experiment. Although preference-based IV methods may usefully complement standard statistical approaches, they make assumptions that are unfamiliar to most biomedical researchers and therefore the validity of such analyses can be hard to evaluate. Here, we propose a simple framework based on a single unobserved dichotomous variable that can be used to explore how violations of IV assumptions and treatment effect heterogeneity may bias the standard IV estimator with respect to the average treatment effect in the population. This framework suggests various ways to anticipate the likely direction of bias using both empirical data and commonly available subject matter knowledge, such as whether medications or medical procedures tend to be overused, underused, or often misused. This approach is described in the context of a study comparing the gastrointestinal bleeding risk attributable to different non-steroidal anti-inflammatory drugs.

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    • "Instrumental variables and sickle cell trait. Instrumental variables (IVs) is an alternative method to estimate the causal effect of an exposure on the outcome when there is unmeasured confounding, provided that a valid instrument is available (Angrist, Imbens, and Rubin, 1996; Hernán and Robins, 2007; Brookhart and Schneeweiss, 2007; Cheng, Qin, and Zhang, 2009; Swanson and Hernán, 2013; Baiocchi, Cheng, and Small, 2014). A valid instrument is a variable that (A1) is associated with the exposure, (A2) has no direct pathways to the outcome, and (A3) is not associated with any unmeasured confounders after controlling for the unmeasured confounders (See Figure 1 and Section 2.3). "
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    ABSTRACT: Most previous studies of the causal relationship between malaria and stunting have been studies where potential confounders are controlled via regression-based methods, but these studies may have been biased by unobserved confounders. Instrumental variables (IV) regression offers a way to control for unmeasured confounders where, in our case, the sickle cell trait can be used as an instrument. However, for the instrument to be valid, it may still be important to account for measured confounders. The most commonly used instrumental variable regression method, two-stage least squares, relies on parametric assumptions on the effects of measured confounders to account for them. Additionally, two-stage least squares lacks transparency with respect to covariate balance and weighing of subjects and does not blind the researcher to the outcome data. To address these drawbacks, we propose an alternative method for IV estimation based on full matching. We evaluate our new procedure on simulated data and real data concerning the causal effect of malaria on stunting among children. We estimate that the risk of stunting among children with the sickle cell trait decrease by 0.22 times the average number of malaria episodes prevented by the sickle cell trait, a substantial effect of malaria on stunting (p-value: 0.011, 95% CI: 0.044, 1).
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    • "S. A. SWANSON AND M. A. HERN´AN (Brookhart and Schneeweiss, 2007 "
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    ABSTRACT: We appreciated Imbens' summary and reflections on the state of instrumental variable (IV) methods from an econometrician's perspective. His review was much needed as it clarified several issues that have been historically a source of confusion when individuals from different disciplines discussed IV methods. Among the many topics covered by Imbens, we would like to focus on the common choice of the local average treatment effect (LATE) over the "global" average treatment effect (ATE) in IV analyses of epidemiologic data. As Imbens acknowledges, this choice of the LATE as an estimand has been contentious (Angrist, Imbens and Rubin, 1996; Robins and Greenland, 1996; Deaton, 2010; Imbens, 2010; Pearl, 2011). Several authors have questioned the usefulness of the LATE for informing clinical practice and policy decisions, because it only pertains to an unknown subset of the population of interest: the so-called "compliers". To make things worse, many studies do not even report the expected proportion of compliers in the study population (Swanson and Hernán, 2013). Other authors have wondered whether the LATE is advocated for simply because of the relatively weaker assumptions required for its identification, analogous to the drunk who stays close to the lamp post and declares whatever he finds under its light is what he was looking for all along (Deaton, 2010). Here we explore the limitations of the LATE in the context of epidemiologic and public health research. First we discuss the relevance of LATE as an effect measure and conclude that it is not our primary choice. Second, we discuss the tenability of the monotonicity condition and conclude that this assumption is not a plausible one in many common settings. Finally, we propose further alternatives to the LATE, beyond those discussed by Imbens, that refocus on the global ATE in the population of interest.
    Preview · Article · Oct 2014 · Statistical Science
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    • "L'utilisation d'un score de propension dans les cas où les facteurs d'ajustement sont nombreux est encouragée.101102103104105106107108109110111112113114115D'autres méthodes permettent de comparer deux groupes de patients, comme l'utilisation d'une variable instrumentale,116117118119120121mais elles se basent sur des hypothèses difficilement vérifiables et leur utilisation ne peut être actuellement privilégiée.[122]Il faut souligner que toutes ces techniques statistiques ne permettent de prendre en compte que les facteurs de confusion connus, présupposant donc que ces facteurs de confusion soient également correctement mesurés au cours de l'étude. "
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