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A distributional approach for causal inference using propensity scores
 Journal of the American Statistical Association
, 2006
"... Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin’s potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal eff ..."
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Cited by 22 (8 self)
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Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin’s potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal effects, assuming no confounding given measured covariates. The other can be used to assess how the estimates might change under various departures from no confounding. Both methods are developed from a nonparametric likelihood perspective. The propensity score plays a central role and is estimated through a parametric model. Under the assumption of no confounding, the joint distribution of covariates and each potential outcome is estimated as a weighted empirical distribution. Expectations from the joint distribution are estimated as weighted averages or, equivalently to first order, regression estimates. The likelihood estimator is at least as efficient and the regression estimator is at least as efficient and robust as existing estimators. Regardless of the noconfounding assumption, the marginal distribution of covariates times the conditional distribution of observed outcome given each treatment assignment and covariates is estimated. For a fixed bound on unmeasured confounding, the marginal distribution of covariates times the conditional distribution of counterfactual outcome given each treatment assignment and covariates is explored to the extreme and then compared with the composite distribution corresponding to observed outcome given the same treatment assignment and covariates. We illustrate the methods by analyzing the data from an observational study on right heart catheterization.
On Estimation of Vaccine Efficacy Using Validation Samples with Selection Bias
, 2006
"... Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini 2001; Halloran et al. 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random. Howe ..."
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Cited by 4 (2 self)
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Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini 2001; Halloran et al. 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random. However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenzalike illness as part of routine influenza surveillance. VE estimates based on such nonMAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating vaccine efficacy in the presence of validation bias. Our work builds on the ideas of Rotnitzky et al. (1998, 2001), Scharfstein et al. (1999, 2003) and Robins et al. (2000). Our methods require expert opinion about the nature of the validation selection bias. In a reanalysis of an influenza vaccine study we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the nonspecific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.
Generalized Additive Selection Models for the Analysis of Studies
"... Rotnitzky, Robins, and Scharfstein (Journal of the American Statistical Association; 1998) developed a methodology for conducting sensitivity analysis of studies in which longitudinal outcome data are subject to potentially nonignorable missingness. In their approach, they specify a class of ful ..."
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Cited by 2 (0 self)
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Rotnitzky, Robins, and Scharfstein (Journal of the American Statistical Association; 1998) developed a methodology for conducting sensitivity analysis of studies in which longitudinal outcome data are subject to potentially nonignorable missingness. In their approach, they specify a class of fully parametric selection models, indexed by a non or weakly identi ed selection bias function which indicates the degree to which missingness depends on potentially unobervable outcomes. Estimation of the parameters of interest proceeds by varying the selection bias function over a range considered plausible by subjectmatter experts. In this paper, we focus on crosssectional, univariate outcome data and extend their approach to a class of semiparametric selection models, using generalized additive restrictions. We propose a back tting algorithm to estimate the parameters of the generalized additive selection model. For estimation of the mean outcome, we propose three types of estimating functions: simple inverse weighted, doubly robust, and orthogonal. We present the results of a data analysis and a simulation study.
A Bayesian Shrinkage Model for Incomplete Longitudinal Binary Data with Application to the Breast Cancer Prevention Trial
"... Abstract: We consider inference in randomized longitudinal studies with missing data that is generated by skipped clinic visits and loss to followup. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a nonfuture depen ..."
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Cited by 1 (1 self)
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Abstract: We consider inference in randomized longitudinal studies with missing data that is generated by skipped clinic visits and loss to followup. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a nonfuture dependence model for the dropout mechanism and partial ignorability for the intermittent missingness. We posit an exponential tilt model that links nonidentifiable distributions and distributions identified under partial ignorability. This exponential tilt model is indexed by nonidentified parameters, which are assumed to have an informative prior distribution, elicited from subjectmatter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated by, and applied to, data from the Breast Cancer Prevention Trial. KEY WORDS: Informative dropout; Prior elicitation; Intermittent missingness.
Modeling Competing Infectious Pathogens from a Bayesian Perspective: Application to Influenza Studies with Incomplete Laboratory Results
"... In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often cocirculate with influenza and cause influenzalike illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, mak ..."
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Cited by 1 (0 self)
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In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often cocirculate with influenza and cause influenzalike illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a Bayesian competingrisk model for multiple cocirculating pathogens for inference on transmissibility and intervention efficacies under the assumption that missingness in the biological confirmation of the pathogen is ignorable. Simulation studies indicate 1 a reasonable performance of the proposed model even if the number of potential pathogens is misspecified. They also show that a moderate amount of missing laboratory test results has only a small impact on inference about key parameters in the setting of close contact groups. Using the proposed model, we found that a nonpharmaceutical intervention is marginally protective against transmission of influenza A in a study conducted in elementary schools.
Printed in Great Britain Incorporating
"... prior beliefs about selection bias into the analysis of randomized trials with missing outcomes ..."
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prior beliefs about selection bias into the analysis of randomized trials with missing outcomes
(Cardiff University) and
, 2007
"... Summary: The problem of analysing longitudinal data complicated by possibly informative dropout has received considerable attention in the statistical literature. Most authors have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be giv ..."
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Summary: The problem of analysing longitudinal data complicated by possibly informative dropout has received considerable attention in the statistical literature. Most authors have concentrated on either methodology or application, but we begin this paper by arguing that more attention could be given to study objectives and to the relevant targets for inference. Next we summarise a variety of approaches that have been suggested for dealing with dropout. A longstanding concern in this subject area is that all methods require untestable assumptions. We discuss circumstances in which we are prepared to make such assumptions and we propose a new and computationally efficient modelling and analysis procedure for these situations. We assume a dynamic linear model for the expected increments of a constructed variable, under which subjectspecific random effects follow a martingale process in the absence of dropout. Informal diagnostic procedures to assess the tenability of the assumption are proposed. The paper is completed by simulations and a comparison of our method and several alternatives in the analysis of data from a trial into the treatment of schizophrenia, in which approximately 50 % of recruited subjects dropped out before the final scheduled measurement time. Key words: additive intensity model; counterfactuals; joint modelling; martingales; missing data. 1 1
Many statistical methods have been developed to deal with estimating the causal effects of...
, 2006
"... doi:10.1093/biostatistics/kxj031 ..."
Daniel Farewell
"... Proofs subject to correction. Not to be reproduced without permission. Contributions to the discussion must not exceed 400 words. Contributions longer than 400 words will be cut by the editor. ..."
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Proofs subject to correction. Not to be reproduced without permission. Contributions to the discussion must not exceed 400 words. Contributions longer than 400 words will be cut by the editor.
INTRODUCTION AND SUMMARY OF MODIFICATIONS MADE IN RESPONSE TO EARLIER REVIEW
"... The reviewers noted in the Resume ’ that the Research Methods Core was “strong with moderate weaknesses”. They also note that “…although the application makes a strong effort to address previous reviewers ' concerns and provide extensive detail, the scope of the proposed effort may be too ambit ..."
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The reviewers noted in the Resume ’ that the Research Methods Core was “strong with moderate weaknesses”. They also note that “…although the application makes a strong effort to address previous reviewers ' concerns and provide extensive detail, the scope of the proposed effort may be too ambitious.” We have eliminated the informatics (webbased assessment and data management) initiatives and reduced