103,191 research outputs found

    Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

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    Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70 (1983) 41--55] as central tools to help assess the causal effects of interventions. Since their introduction more than two decades ago, they have found wide application in a variety of areas, including medical research, economics, epidemiology and education, especially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require extensive delays before answers could be obtained. In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dramatically. Nevertheless, thus far, we believe that there have been few applications of propensity score methods to evaluate marketing interventions (e.g., advertising, promotions), where the tradition is to use generally inappropriate techniques, which focus on the prediction of an outcome from background characteristics and an indicator for the intervention using statistical tools such as least-squares regression, data mining, and so on. With these techniques, an estimated parameter in the model is used to estimate some global ``causal'' effect. This practice can generate grossly incorrect answers that can be self-perpetuating: polishing the Ferraris rather than the Jeeps ``causes'' them to continue to win more races than the Jeeps ‚áĒ\Leftrightarrow visiting the high-prescribing doctors rather than the low-prescribing doctors ``causes'' them to continue to write more prescriptions. This presentation will take ``causality'' seriously, not just as a casual concept implying some predictive association in a data set, and will illustrate why propensity score methods are generally superior in practice to the standard predictive approaches for estimating causal effects.Comment: Published at http://dx.doi.org/10.1214/088342306000000259 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Propensity score methodology for confounding control in health care utilization databases

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    Propensity score (PS) methodology is a common approach to control for confounding in nonexperimental studies of treatment effects using health care utilization databases. This methodology offers researchers many advantages compared with conventional multivariate models: it directly focuses on the determinants of treatment choice, facilitating the understanding of the clinical decision-making process by the researcher; it allows for graphical comparisons of the distribution of propensity scores and truncation of subjects without overlapping PS indicating a lack of equipoise; it allows transparent assessment of the confounder balance achieved by the PS at baseline; and it offers a straightforward approach to reduce the dimensionality of sometimes large arrays of potential confounders in utilization databases, directly addressing the ‚Äúcurse of dimensionality‚ÄĚ in the context of rare events. This article provides an overview of the use of propensity score methodology for pharmacoepidemiologic research with large health care utilization databases, covering recent discussions on covariate selection, the role of automated techniques for addressing unmeasurable confounding via proxies, strategies to maximize clinical equipoise at baseline, and the potential of machine-learning algorithms for optimized propensity score estimation. The appendix discusses the available software packages for PS methodology. Propensity scores are a frequently used and versatile tool for transparent and comprehensive adjustment of confounding in pharmacoepidemiology with large health care databases

    Evaluating the impact of public subsidies on a firm's performance: A quasi-experimental approach

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    Many regional governments in developed countries design programs to improve the competitiveness of local firms. In this paper, we evaluate the effectiveness of public programs whose aim is to enhance the performance of firms located in Catalonia (Spain). We compare the performance of publicly subsidised companies (treated) with that of similar, but unsubsidised companies (non-treated). We use the Propensity Score Matching (PSM) methodology to construct a control group which, with respect to its observable characteristics, is as similar as possible to the treated group, and that allows us to identify firms which retain the same propensity to receive public subsidies. Once a valid comparison group has been established, we compare the respective performance of each firm. As a result, we find that recipient firms, on average, change their business practices, improve their performance, and increase their value added as a direct result of public subsidy programs.Public policy, evaluation studies, firm performance, propensity Score Matching.

    Evaluating the Impact of Public Subsidies on a Firms Performance: a Quasi-experimental Approach

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    Many regional governments in developed countries design programs to improve the competitiveness of local firms. In this paper, we evaluate the effectiveness of public programs whose aim is to enhance the performance of firms located in Catalonia (Spain). We compare the performance of publicly subsidised companies (treated) with that of similar, but unsubsidised companies (non-treated). We use the Propensity Score Matching (PSM) methodology to construct a control group which, with respect to its observable characteristics, is as similar as possible to the treated group, and that allows us to identify firms which retain the same propensity to receive public subsidies. Once a valid comparison group has been established, we compare the respective performance of each firm. As a result, we find that recipient firms, on average, change their business practices, improve their performance, and increase their value added as a direct result of public subsidy programs.public policy, evaluation studies, firm performance, propensity score matching

    Augmentation of Propensity Scores for Medical Records-based Research

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    Therapeutic research based on electronic medical records suffers from the possibility of various kinds of confounding. Over the past 30 years, propensity scores have increasingly been used to try to reduce this possibility. In this article a gap is identified in the propensity score methodology, and it is proposed to augment traditional treatment-propensity scores with outcome-propensity scores, thereby removing all other aspects of common causes from the analysis of treatment effects

    The generalized propensity score methodology for estimating unbiased journal impact factors

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    The journal impact factor (JIF) proposed by Garfield in the year 1955 is one of the most commonly used and prominent citation-based indicators of the performance and significance of a scientific journal. The JIF is simple, reasonable, clearly defined, and comparable over time and, what is more, can be easily calculated from data provided by Thomson Reuters, but at the expense of serious technical and methodological flaws. The paper discusses one of the core problems: The JIF is affected by bias factors (e.g., document type) that have nothing to do with the prestige or quality of a journal. For solving this problem, we suggest using the generalized propensity score methodology based on the Rubin Causal Model. Citation data for papers of all journals in the ISI subject category "Microscopy‚ÄĚ (Journal Citation Report) are used to illustrate the proposa
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