This blog makes public the hallway conversations about social science statistical methods and analysis from the and related research groups. Expect to see posts on trends in methodological thought, questions and comments, paper and conference announcements, applied problems needing methodological solutions, and methodological techniques seeking applied problems. Also included arelar weekly research workshop held here and billed as a tour of and applications with weekly stops in different disciplines.I study predictive effects of teachers and schools on test scores in fourth through eighth grade and outcomes later in life such as college attendance and earnings. The predictive effects have the following form: predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher, and given the test score for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model which, under certain assumptions, makes this feasible. Administrative school district data n combination with tax data were used to calculate estimates and do iSome scientists still think that good data visualization is only necessary when presenting work to "the public." In truth, thinking hard about how to learn the most from any data set should always involve some form of graph, map, chart, or other visual statistical display. This talk will demonstrate how visualization techniques that include so-called "linked views" offer new insights to researchers visualizing large and/or diverse data sets. In particular, the talk will highlight a few high-dimensional visualization examples where ideas about linked views first put forth by John Tukey are extended beyond two-dimensional displays and point clouds. Examples will be principally drawn from astronomy and medical imaging, and software highlighted will include the Universe Information System known as "WorldWide Telescope" and a new python-based linked-view system called "Glue".nference.Nearly every nation in the world is undergoing rapid epidemiologic transition toward noncommunicable chronic diseases including cardiovascular disease obesity, diabetes, and cancers. Numerous organizations including the United Nations, World Health Organization, US Centers for Disease Control and Prevention, and other national and international organizations have emphasized the importance of dietary habits as a key risk Yet, the burdens of suboptimal dietary habits not established. Quantification of these burdens has been limited by inadequate or absent data on dietary habits in many nations, not only for each country as a whole, but also for age- and sex-specific strata. As part of our work in the 2013 Global Burden of Diseases Nutrition and Chronic Diseases Group, we systematically identified and obtained data on national and subnational individual-level surveys of dietary consumption worldwide; and used a Bayesian hierarchical model to evaluate and account for differences in comparability, assessment methods, representativeness, and missingness. We also quantified effects of dietary habits ons, including differences by age, in new meta-analyses. We compiled additional data to quantify the alternative optimal distribution of key dietary risk factors, and the numbers of cause-specific deaths by country, age, and sex. Using this compilation of global data, we used comparative risk assessment to quantify the impacts of current dietary habits on in each nation around the world. The case of sugar-sweetened beverage and , adiposity-related cancers, and diabetes will be presented as an example of our newest findings.Nearly every nation in the world is undergoing rapid epidemiologic transition toward noncommunicable chronic diseases including cardiovascular disease obesity, diabetes, and cancers. Numerous organizations including the United Nations, World Health Organizatio Centers for Disease Control and Prevention, and other national and international organizations have emphasized the importance of dietary habits as a key Yet, the burdens of suboptimal dietary habits on as well as heterogeneity in these burdens by region, country, age, and sex, are not established. Quantification of these burdens has beey inadequate or absent data on dietary habits in many nations, not only for each country as a whole, but also for age- and sex-specific strata. As part of our work in the 2013 Global Burden of Diseases Nutrition and Chronic Diseases Group, we systematically identified aand obtained data on national and subnational individual-level surveysn worldwide; and used a Bayesian hierarchical model to evaluate and account for differences in comparability, assessment methods, representativeness, and missingness. We also quantified effects of dietary habits on, including differences by age, in new meta-analyses. We compiled additional data to quantify the alternative optimal distribution of key dietary risk factors, and the numbers of cause-specific deaths by country, age, and sex. Using this compilation of global data, we used comparative risk assessment to quantify the impacts of current dietary habits on s in each nation around the world. 
The case of sugar-sweetened beverages and adiposity-related cancers, and diabetes will be presented as an example of our newest findings.Attrition is the Achilles' Heel of the randomized experiment: it is fairlcommon,and it can unravel the benefits of randomization.This study considers when and why attrition is a problem, and how it can be diagnosed. The extant literature remains ambiguous because it relies on the language of probability, whereas problematic attrition depends on the underlying causal relations. This ambiguity arises because causation implies correlation but not vice versa. Using the structural causal language of directed acyclic graphs I show attrition is a problem when it is an active collider between the treatment and the outcome, or when the latent outcome is a mediator between the treatment and the attrition. Moreover, whether observed outcomes are representative of all outcomes, or only comparable across experimental arms, depends on two d-separation conditions. One of these is directly testable from the dataObservers of approval regulation regimes such as FDA drug review have long proposed that they cause private companies to avoid developing new products that would otherwise have been marketed. The welfare conclusions and policy recommendations vary, but the causal claim is common. Yet most such claims suffer from the problem of endogeneity and non-random assignment, such that the necessary counterfactual cannot be sustained. If a regulatory decision occurs and drug projects are discontinued or delayed, the analyst cannot usually infer whether it was a change in regulation or something else that caused the project abandonment. Using a rich dataset on the development of over 15,000 pharmaceutical investment projects from we examine responses in development projects to "bad news" regulatory announcements weighted by the asset price shocks in a general equilibrium financial market. Using a Lévy process model of asset price evolution, we demonstrate that the abrupt changes in sponsor asset prices upon the announcement of adverse regulatory news are plausibly non-anticipable for all participants but the regulator. Specifically, for the development projects of companies other than the sponsor affected, they are quasi-random, conditional on all information knownent. This assumption is supported by analysis of data, and then used to identify a model of regulatory effects upon drug development. The results suggest robust effects of induced project abandonment by regulatory decisions; a ten percent to induce a three to four percent increase in the hazard rate of drug project discontinuation for all other firms' projects in the months following the news.
While some immediate responses to adverse regulatory news are witnessed, most response takes place in a six month period following the event. Effects are larger for bad news from advisory committee decisions and requests for additional data, and are for surprise other-company abandonments where factors are implicit. The results are generally supportive of dominant theoretical models of endogenous approval regulation but policy implications are unclear and depend upon the potential health and welfare effects of the therapies foregone.For decades, market researchers have used conjoint analysis to understand how consumers make decisions when faced with multi-dimensional choices. In such analyses, respondents are asked to score or rank a set of alternatives, where each alternative is defined by multiple attributes which are varied randomly or intentionally. Political scientists are frequently interested in parallel questions about decision-making, yet to date conjoint analysis has seen little use within the field. In this manuscript, we demonstrate the potential value of conjoint analysis in political science, using examples about vote choice and immigrant admission to the United States. In doing so, we develop a set of statistical tools for drawing causal conclusions from stated preference data based on the potential outcomes framework of causal inference. We discuss the causal estimands of interest and provide a formal analysis of the assumptions required for identifying those quantities. Prior conjoint analyses have typically used designs which limit the number of unique conjoint profiles. We employ a survey experiment to compare this approach to a fully randomized approach. Both our formal analysis of the causal estimands and our empirical results highlight the potential biases of common approaches to conjoint analysis which restrict the number of profiles.In studies of public health, outcome measures such as the odds ratio, rate ratio, or efficacy are often estimated across strata to assess the overall effect of active treatment versus control treatment. Patients may be partitioned into such strata or blocks by experimental design, or, in non-randomized studies, patients may be partitioned into subclasses based on key covariates or estimated propensity scores to improve observed covariate balance across treatment groups. In finite samples, there exist tests and intervals for these estimands that can be more powerful than tests and intervals created with Cochran-Mantel-Haenszel or analogous procedures . The proposed methods multiply impute missing potential outcomes within the Rubin Causal Model so that estimands can be directly estimated. The assumptions underlying these typically more powerful methods are appropriate in many circumstances, especially when the strata are based on covariates highly predictive of treatment decisions and outcomes. When used to draw inferences about a population from which the patients in the study are considered a random sample, and the sample is large, these methods are extremely similar to the classical methods. The proposed approach is particularly relevant when assessing the safety of a new treatment relative to a standard one because, under typical conditions, the tests are more powerful and the intervals are shorter, thereby detecting smaller differences.
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