Conceptualizing Experimental Controls Using the Potential Outcomes Framework [0.03%]
用潜在结果框架理解实验控制变量
Kristen B Hunter,Kristen Koenig,Marie-Abèle Bind
Kristen B Hunter
The goal of a controlled experiment is to remove unwanted variation when estimating the causal effect of the intervention. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negati...
Kenneth Lange,Xun-Jian Li,Hua Zhou
Kenneth Lange
Classroom expositions of maximum likelihood estimation (MLE) rely on traditional calculus methods to construct analytic solutions. This creates in students a false sense of the ease with which MLE problems can be attacked. In a nod to reali...
A Multiple Imputation Approach for the Cumulative Incidence, with Implications for Variance Estimation [0.03%]
多重填充方法在累积发病率估计中的应用及其对方差估计的影响
Elizabeth C Chase,Philip S Boonstra,Jeremy M G Taylor
Elizabeth C Chase
We present an alternative approach to estimating the cumulative incidence function that uses non-parametric multiple imputation to reduce the problem to that of estimating a binomial proportion. In the standard competing risks setting, we s...
Linda J Harrison,Sean S Brummel
Linda J Harrison
Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; ...
Eugene Demidenko
Eugene Demidenko
The classic formula for estimating the binomial probability as the proportion of successes contradicts common sense for extreme probabilities when the event never occurs or occurs every time. Laplace's law of succession estimator, one of th...
M-A C Bind,D B Rubin
M-A C Bind
Consider a study whose primary results are "not statistically significant". How often does it lead to the following published conclusion that "there is no effect of the treatment/exposure on the outcome"? We believe too often and that the r...
Theo Economou,Daphne Parliari,Aurelio Tobias et al.
Theo Economou et al.
In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn a...
Eric Yanchenko,Howard D Bondell,Brian J Reich
Eric Yanchenko
In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model...
Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency [0.03%]
使用第二代P值并通过监控频率控制第一类错误进行顺序监测
Jonathan J Chipman,Robert A Greevy Jr,Lindsay Mayberry et al.
Jonathan J Chipman et al.
The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGPV) to connect study design intentions with ...
Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation [0.03%]
贝叶斯视角下的带约束和惩罚的估计中的proximal MCMC算法研究
Xinkai Zhou,Qiang Heng,Eric C Chi et al.
Xinkai Zhou et al.
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs t...