An Alternative Perspective on the Robust Poisson Method for Estimating Risk or Prevalence Ratios
Denis Talbot, Miceline Mésidor, Yohann Chiu, Marc Simard, Caroline Sirois
备注：
29 pages, 1 figure. To be published in Epidemiology. Modifications since previous version: Expanded comparison with alternative methods for estimating risk or prevalence ratios; new Appendix reporting on a simulation study
The robust Poisson method is becoming increasingly popular when estimating the association of exposures with a binary outcome. Unlike the logistic regression model, the robust Poisson method yields results that can be interpreted as risk or prevalence ratios. In addition, it does not suffer from frequent non-convergence problems like the most common implementations of maximum likelihood estimators of the log-binomial model. However, using a Poisson distribution to model a binary outcome may seem counterintuitive. Methodological papers have often presented this as a good approximation to the more natural binomial distribution. In this paper, we provide an alternative perspective to the robust Poisson method based on the semiparametric theory. This perspective highlights that the robust Poisson method does not require assuming a Poisson distribution for the outcome. In fact, the method only assumes a log-linear relationship between the risk/prevalence of the outcome and the explanatory variables. This assumption and consequences of its violation are discussed. Suggestions to reduce the risk of violating the modeling assumption are also provided. Additionally, we discuss and contrast the robust Poisson method with other approaches for estimating exposure risk or prevalence ratios.
This paper focuses on drawing statistical inference based on a novel variant of maxima or minima nomination sampling (NS) designs. These sampling designs are useful for obtaining more representative sample units from the tails of the population distribution using the available auxiliary ranking information. However, one common difficulty in performing NS in practice is that the researcher cannot obtain a nominated sample unless he/she uniquely determines the sample unit with the highest or the lowest rank in each set. To overcome this problem, a variant of NS which is called partial nomination…
We present methodology for estimating the stochastic intensity of a doubly stochastic Poisson process. Statistical and theoretical analyses of traffic traces show that these processes are appropriate models of high intensity traffic arriving at an array of service systems. The statistical estimation of the underlying latent stochastic intensity process driving the traffic model involves a rather complicated nonlinear filtering problem. We develop a novel simulation methodology, using deep neural networks to approximate the path measures induced by the stochastic intensity process, for solving …
To make informative public policy decisions in battling the ongoing COVID-19 pandemic, it is important to know the disease prevalence in a population. There are two intertwined difficulties in estimating this prevalence based on testing results from a group of subjects. First, the test is prone to measurement error with unknown sensitivity and specificity. Second, the prevalence tends to be low at the initial stage of the pandemic and we may not be able to determine if a positive test result is a false positive due to the imperfect specificity of the test. The statistical inference based on la…
It is highly important for governments and health organizations to monitor the prevalence of breast cancer as a leading source of cancer-related death among women. However, the accurate diagnosis of this disease is expensive, especially in developing countries. This article concerns a cost-efficient method for estimating prevalence of breast cancer, when diagnosis is based on a comprehensive biopsy procedure. Multistage ranked set sampling (MSRSS) is utilized to develop a proportion estimator. This design employs some visually assessed cytological covariates, which are pertinent to determinati…
A Bayesian Multiscale Integrated Abundance Model for Estimating Latent Opioid Misuse Prevalence from Spatially Misaligned Data
Wake Forest University（维克森林大学）；Wake Forest University School of Medicine（维克森林大学医学院）；Yale School of Public Health（耶鲁公共卫生学院）；Gillings School of Public Health, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校吉林斯公共卫生学院）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）
A Counterfactual Framework for Estimating Infectious Disease Prevalence under Repeated Testing with Symptomatic and Contact-Tracing Components
Division of Biostatistics, College of Public Health, The Ohio State University（俄亥俄州立大学公共卫生学院生物统计系）；Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（卡罗林斯卡医学院医学流行病学与生物统计系）
Multi-Parameter Estimation of Prevalence (MPEP): A Bayesian modelling approach to estimate the prevalence of opioid dependence
University of Bristol（布里斯托大学）；Public Health Scotland（苏格兰公共卫生局）；Glasgow Caledonian University（格拉斯哥喀里多尼亚大学）；National Drug and Alcohol Research Centre, University of New South Wales（新南威尔士大学国家药物与酒精研究中心）；Department of Mathematics, University of Athens（雅典大学数学系）
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Incorporating External Controls for Estimating the Average Treatment Effect on the Treated with High-Dimensional Data: Retaining Double Robustness and Ensuring Double Safety
