Article

Propensity Score-based Sensitivity Analysis Method for Uncontrolled Confounding

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts 02215, USA.
American journal of epidemiology (Impact Factor: 4.98). 06/2011; 174(3):345-53. DOI: 10.1093/aje/kwr096
Source: PubMed

ABSTRACT The authors developed a sensitivity analysis method to address the issue of uncontrolled confounding in observational studies. In this method, the authors use a 1-dimensional function of the propensity score, which they refer to as the sensitivity function (SF), to quantify the hidden bias due to unmeasured confounders. The propensity score is defined as the conditional probability of being treated given the measured covariates. Then the authors construct SF-corrected inverse-probability-weighted estimators to draw inference on the causal treatment effect. This approach allows analysts to conduct a comprehensive sensitivity analysis in a straightforward manner by varying sensitivity assumptions on both the functional form and the coefficients in the 1-dimensional SF. Furthermore, 1-dimensional continuous functions can be well approximated by low-order polynomial structures (e.g., linear, quadratic). Therefore, even if the imposed SF is practically certain to be incorrect, one can still hope to obtain valuable information on treatment effects by conducting a comprehensive sensitivity analysis using polynomial SFs with varying orders and coefficients. The authors demonstrate the new method by implementing it in an asthma study which evaluates the effect of clinician prescription patterns regarding inhaled corticosteroids for children with persistent asthma on selected clinical outcomes.

Full-text

Available from: Ann Chen Wu, Jun 06, 2015
1 Follower
 · 
201 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were -0.33 (95% CI -0.53, -0.13) and -0.24 (-0.46, -0.02), respectively. The other approaches resulted in smaller n (204-276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from -0.01 to -0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method.
    Journal of Developmental Origins of Health and Disease 08/2014; 5(6):1-13. DOI:10.1017/S2040174414000415 · 0.77 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi-component epidemiological and neurobiological study designed to generate actionable evidence-based recommendations to reduce US Army suicides and increase basic knowledge about the determinants of suicidality. This report presents an overview of the designs of the six components of the Army STARRS. These include: an integrated analysis of the Historical Administrative Data Study (HADS) designed to provide data on significant administrative predictors of suicides among the more than 1.6 million soldiers on active duty in 2004–2009; retrospective case-control studies of suicide attempts and fatalities; separate large-scale cross-sectional studies of new soldiers (i.e. those just beginning Basic Combat Training [BCT], who completed self-administered questionnaires [SAQs] and neurocognitive tests and provided blood samples) and soldiers exclusive of those in BCT (who completed SAQs); a pre-post deployment study of soldiers in three Brigade Combat Teams about to deploy to Afghanistan (who completed SAQs and provided blood samples) followed multiple times after returning from deployment; and a platform for following up Army STARRS participants who have returned to civilian life. Department of Defense/Army administrative data records are linked with SAQ data to examine prospective associations between self-reports and subsequent suicidality. The presentation closes with a discussion of the methodological advantages of cross-component coordination.
    International Journal of Methods in Psychiatric Research 12/2013; 22(4):267-275. DOI:10.1002/mpr.1401 · 3.42 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cost-effectiveness analysis is an important tool that can be applied to the evaluation of a health treatment or policy. When the observed costs and outcomes result from a nonrandomized treatment, making causal inference about the effects of the treatment requires special care. The challenges are compounded when the observation period is truncated for some of the study subjects. This paper presents a method of unbiased estimation of cost-effectiveness using observational study data that is not fully observed. The method-twice-weighted multiple interval estimation of a marginal structural model-was developed in order to analyze the cost-effectiveness of treatment protocols for advanced dementia residents living nursing homes when they become acutely ill. A key feature of this estimation approach is that it facilitates a sensitivity analysis that identifies the potential effects of unmeasured confounding on the conclusions concerning cost-effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 03/2014; 33(7). DOI:10.1002/sim.6017 · 2.04 Impact Factor