2020

最新文章

• Optimal alpha spending for sequential analysis with binomial data AI翻译
  作者：Silva, Ivair R.; Kulldorff, Martin; Yih, W. Katherine
  作者单位：Universidade Federal de Ouro Preto; Harvard University; Harvard Medical School; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Pilgrim Health Care
  摘要：For sequential analysis hypothesis testing, various alpha spending functions have been proposed. Given a prespecified overall alpha level and power, we derive the optimal alpha spending function that minimizes the expected time to signal for continuous as well as group sequential analysis. If there is also a restriction on the maximum sample size or on the expected sample size, we do the same. Alternatively, for fixed overall alpha, power and expected time to signal, we derive the optimal alph...
• Goodness-of-fit testing in high dimensional generalized linear models AI翻译
  作者：Jankova, Jana; Shah, Rajen D.; Buhlmann, Peter; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：We propose a family of tests to assess the goodness of fit of a high dimensional generalized linear model. Our framework is flexible and may be used to construct an omnibus test or directed against testing specific non-linearities and interaction effects, or for testing the significance of groups of variables. The methodology is based on extracting left-over signal in the residuals from an initial fit of a generalized linear model. This can be achieved by predicting this signal from the residu...
• Causal isotonic regression AI翻译
  作者：Westling, Ted; Gilbert, Peter; Carone, Marco
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：In observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose-response curve can be recovered by using the G-computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric assumptions, which carry significant risk of model misspecification. Non-parametric estimation in this context is challenging because in a non-parametr...
• Robust testing in generalized linear models by sign flipping score contributions AI翻译
  作者：Hemerik, Jesse; Goeman, Jelle J.; Finos, Livio
  作者单位：University of Oslo; Leiden University - Excl LUMC; Leiden University; Leiden University Medical Center (LUMC); University of Padua
  摘要：Generalized linear models are often misspecified because of overdispersion, heteroscedasticity and ignored nuisance variables. Existing quasi-likelihood methods for testing in misspecified models often do not provide satisfactory type I error rate control. We provide a novel semiparametric test, based on sign flipping individual score contributions. The parameter tested is allowed to be multi-dimensional and even high dimensional. Our test is often robust against the mentioned forms of misspec...
• Inference for two-stage sampling designs AI翻译
  作者：Chauvet, Guillaume; Vallee, Audrey-Anne
  作者单位：Laval University
  摘要：Two-stage sampling designs are commonly used for household and health surveys. To produce reliable estimators with associated confidence intervals, some basic statistical properties like consistency and asymptotic normality of the Horvitz-Thompson estimator are desirable, along with the consistency of associated variance estimators. These properties have been mainly studied for single-stage sampling designs. In this work, we prove the consistency of the Horvitz-Thompson estimator and of associ...
• Causal mediation analysis for stochastic interventions AI翻译
  作者：Diaz, Ivan; Hejazi, Nima S.
  作者单位：Cornell University; Weill Cornell Medicine; University of California System; University of California Berkeley
  摘要：Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for...
• Robust tests for treatment effect in survival analysis under covariate-adaptive randomization AI翻译
  作者：Ye, Ting; Shao, Jun
  作者单位：East China Normal University; University of Wisconsin System; University of Wisconsin Madison
  摘要：Covariate-adaptive randomization is popular in clinical trials with sequentially arrived patients for balancing treatment assignments across prognostic factors that may have influence on the response. However, existing theory on tests for the treatment effect under covariate-adaptive randomization is limited to tests under linear or generalized linear models, although the covariate-adaptive randomization method has been used in survival analysis for a long time. Often, practitioners will simpl...
• Sumca: simple, unified, Monte-Carlo-assisted approach to second-order unbiased mean-squared prediction error estimation AI翻译
  作者：Jiang, Jiming; Torabi, Mahmoud
  作者单位：University of California System; University of California Davis; University of Manitoba
  摘要：We propose a simple, unified, Monte-Carlo-assisted approach (called 'Sumca') to second-order unbiased estimation of the mean-squared prediction error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor, empirical best predictor and post-model-selection empirical best linear unbiased predictor and empirical best predictor as specia...
• Rerandomization and regression adjustment AI翻译
  作者：Li, Xinran; Ding, Peng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
  摘要：Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance in the analysis stage. We can embed blocking in a wider class of experimental design called rerandomization, and extend the classical analysis of covar...
• Robust inference on population indirect causal effects: the generalized front door criterion AI翻译
  作者：Fulcher, Isabel R.; Shpitser, Ilya; Marealle, Stella; Tchetgen, Eric J. Tchetgen
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Johns Hopkins University; University of Pennsylvania
  摘要：Standard methods for inference about direct and indirect effects require stringent no-unmeasured-confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of the paper is to introduce a new form of indirect effect, the population intervention indirect effect, that can be non-parametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the extent to which the effect ...