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Causal Treatment Effects (causal + treatment_effects)
Selected AbstractsUtilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time-Lagged DataBIOMETRICS, Issue 4 2001Kevin J. Anstrom Summary. Observational studies frequently are conducted to compare long-term effects of treatments. Without randomization, patients receiving one treatment are not guaranteed to be prognostically comparable to those receiving another treatment. Furthermore, the response of interest may be right-censored because of incomplete follow-up. Statistical methods that do not account for censoring and confounding may lead to biased estimates. This article presents a method for estimating treatment effects in nonrandomized studies with right-censored responses. We review the assumptions required to estimate average causal effects and derive an estimator for comparing two treatments by applying inverse weights to the complete cases. The weights are determined according to the estimated probability of receiving treatment conditional on covariates and the estimated treatment-specific censoring distribution. By utilizing martingale representations, the estimator is shown to be asymptotically normal and an estimator for the asymptotic variance is derived. Simulation results are presented to evaluate the properties of the estimator. These methods are applied to an observational data set of acute coronary syndrome patients from Duke University Medical Center to estimate the effect of a treatment strategy on the mean 5-year medical cost. [source] SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHINGJOURNAL OF ECONOMIC SURVEYS, Issue 1 2008Marco Caliendo Abstract Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ,what to do if there is choice-based sampling?' or ,when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes. [source] Can Nutritional Label Use Influence Body Weight Outcomes?KYKLOS INTERNATIONAL REVIEW OF SOCIAL SCIENCES, Issue 4 2009Andreas C. Drichoutis SUMMARY Many countries around the world have already mandated, or plan to mandate, the presence of nutrition related information on most pre-packaged food products. Health advocates and lobbyists would like to see similar laws mandating nutrition information in the restaurant and fast-food market as well. In fact, New York City has already taken a step forward and now requires all chain restaurants with 15 or more establishments anywhere in US to show calorie information on their menus and menu board. The benefits were estimated to be as much as 150,000 fewer obese New Yorkers over the next five years. The implied benefits of the presence of nutrition information are that consumers will be able to observe such information and then make informed (and hopefully healthier) food choices. In this study, we use the latest available dataset from the US National Health and Nutrition Examination Survey (2005,2006) to explore whether reading such nutrition information really has an effect on body weight outcomes. In order to deal with the inherent problem of cross-sectional datasets, namely self-selection, and the possible occurrence of reverse causality we use a propensity score matching approach to estimate causal treatment effects. We conducted a series of tests related to variable choice of the propensity score specification, quality of matching indicators, robustness checks, and sensitivity to unobserved heterogeneity, using Rosenbaum bounds to validate our propensity score exercise. Our results generally suggest that reading nutrition information does not affect body mass index. The implications of our findings are also discussed. [source] Some Methods of Propensity-Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulationsBIOMETRICAL JOURNAL, Issue 1 2009Peter C. Austin Abstract Propensity-score matching is increasingly being used to reduce the impact of treatment-selection bias when estimating causal treatment effects using observational data. Several propensity-score matching methods are currently employed in the medical literature: matching on the logit of the propensity score using calipers of width either 0.2 or 0.6 of the standard deviation of the logit of the propensity score; matching on the propensity score using calipers of 0.005, 0.01, 0.02, 0.03, and 0.1; and 5 , 1 digit matching on the propensity score. We conducted empirical investigations and Monte Carlo simulations to investigate the relative performance of these competing methods. Using a large sample of patients hospitalized with a heart attack and with exposure being receipt of a statin prescription at hospital discharge, we found that the 8 different methods produced propensity-score matched samples in which qualitatively equivalent balance in measured baseline variables was achieved between treated and untreated subjects. Seven of the 8 propensity-score matched samples resulted in qualitatively similar estimates of the reduction in mortality due to statin exposure. 5 , 1 digit matching resulted in a qualitatively different estimate of relative risk reduction compared to the other 7 methods. Using Monte Carlo simulations, we found that matching using calipers of width of 0.2 of the standard deviation of the logit of the propensity score and the use of calipers of width 0.02 and 0.03 tended to have superior performance for estimating treatment effects (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical TrialsBIOMETRICS, Issue 1 2009L. Taylor Summary Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine. [source] | |||||||||||||