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Background Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators. Results We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report. Conclusion Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: ( https://sites.google.com/view/sr-ln/ssc ).

Background Private-equity-owned nursing homes (PENH) represent the strongest form of profit orientation in the nursing care market. Private equity firms aim to increase the profitability of nursing care facilities, which often leads to cost-cutting measures and the use of less qualified staff. Our study aims to fill the existing knowledge gap by examining the association between private equity ownership and COVID-19 related infections and deaths among residents and staff during the COVID-19 pandemic. Methods We analyzed outbreak and mortality data for the period from 20/03/2020 to 05/01/2022 from 32 long-term care facilities in the Federal State of Hesse, Germany, which included 16 PENH that were propensity score matched on regional population density and number of beds with 16 non-PENH. We used logistic regression to determine the odds ratios (OR) for above-median values for the independent variables of PENH-status, number of beds, proportion of single rooms, registered nurses' ratio, and copayments. Results PENH had substantially fewer outbreaks in number, but longer and larger outbreaks among nursing home residents, as well as a markedly increased proportion of deceased residents. The odds of the outcome \"infections & deaths\" were 5.38 ( p  <. 05) times higher among PENH compared to non-PENH. Conclusions The study indicates a need for further research into the quality of care in PENH to inform evidence-based policy decisions, given the higher infection and death rates. Improved documentation and public visibility of PENH is also recommended, in line with existing practices for for-profit and non-profit nursing homes in Germany. Given our findings, regulatory bodies should closely observe PENH operational practices.

This paper provides an overview of control function (CF) methods for solving the problem of endogenous explanatory variables (EEVs) in linear and nonlinear models. CF methods often can be justified in situations where \"plug-in\" approaches are known to produce inconsistent estimators of parameters and partial effects. Usually, CF approaches require fewer assumptions than maximum likelihood, and CF methods are computationally simpler. The recent focus on estimating average partial effects, along with theoretical results on nonparametric identification, suggests some simple, flexible parametric CF strategies. The CF approach for handling discrete EEVs in nonlinear models is more controversial but approximate solutions are available.

For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least-square estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this article, we propose fast subsampling algorithms to efficiently approximate the maximum likelihood estimate in logistic regression. We first establish consistency and asymptotic normality of the estimator from a general subsampling algorithm, and then derive optimal subsampling probabilities that minimize the asymptotic mean squared error of the resultant estimator. An alternative minimization criterion is also proposed to further reduce the computational cost. The optimal subsampling probabilities depend on the full data estimate, so we develop a two-step algorithm to approximate the optimal subsampling procedure. This algorithm is computationally efficient and has a significant reduction in computing time compared to the full data approach. Consistency and asymptotic normality of the estimator from a two-step algorithm are also established. Synthetic and real datasets are used to evaluate the practical performance of the proposed method. Supplementary materials for this article are available online.

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effect models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that (1) circumvent the need for analytic approximations, numerical integration, or Metropolis–Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Pólya–Gamma distribution, are implemented in the R package BayesLogit . Supplementary materials for this article are available online.

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two-step grouped fixed-effects (GFE) estimators, where individuals are first classified into groups using kmeans clustering, and the model is then estimated allowing for group-specific heterogeneity. Our framework relies on two key properties: heterogeneity is a function—possibly nonlinear and time-varying—of a low-dimensional continuous latent type, and informative moments are available for classification. We illustrate the method in a model of wages and labor market participation, and in a probit model with time-varying heterogeneity. We derive asymptotic expansions of two-step GFE estimators as the number of groups grows with the two dimensions of the panel. We propose a data-driven rule for the number of groups, and discuss bias reduction and inference.

Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provides both a method for simulation and an efficient estimator to hitherto intractable Gaussian integrals. We prove that the estimator has a rare vanishing relative error asymptotic property. Numerical experiments suggest that the scheme proposed is accurate in a wide range of set-ups for which competing estimation schemes fail. We give an application to exact independently and identically distributed data simulation from the Bayesian posterior of the probit regression model.

Background: Dietary diversity is a proxy indicator of nutrient adequacy. However, little is documented on dietary diversity among pregnant women in Ethiopia in general and specifically in the study area. This study assessed dietary diversity and associated factors among pregnant women attending antenatal care in public health facilities in Bale Zone, Southeast Ethiopia. Methods: An institution-based cross-sectional study was conducted in Bale Zone from January to March 2017. The sample size was determined using a single population proportion formula. Data were collected by pretested structured interviewer-administered questionnaires from a total of 413 pregnant women who were identified through systematic random sampling. The sample was drawn proportionally from selected public health facilities based on the client load. Dietary diversity was computed from information about the nine food groups obtained using a 24-hour dietary recall method. Statistical analysis was done using bivariate and multivariate logistic regression with the P-value <0.05 at 95% confidence interval considered as statistically significant. Results: The mean age of the pregnant women was 26.93 with standard deviation [+ or -]6.12 years. About 55.2% of the pregnant women had inadequate dietary diversity. Getting information from a health professional [AOR =5.26, 95% CI (1.60, 17.36)], being an urban dweller [AOR =8.95, 95% CI (4.42, 18.16)], having a protected water source [AOR =11.16, 95% CI (4.74, 26.27)], having a latrine [AOR =8.21, 95% CI (4.01, 16.80)], having a home garden [AOR =4.26, 95% CI (2.08, 8.70)], having a bank account [AOR =12.25, 95% CI (6.01, 24.97)] and having use of a mobile phone [AOR =3.82, 95% CI (1.92, 7.62)] were significantly associated with dietary diversity. Conclusion: In this community, the prevalence of inadequate dietary diversity is high. Variables which indicate a better living condition such as having a protected source of water, having a latrine, having a home garden, being an urban dweller, having a bank account and having use of a mobile phone were independent predictors of dietary diversity. Therefore, attention should be paid to improve to better living conditions of pregnant women by addressing determinate variables through community awareness. Keywords: dietary diversity, pregnant women, antenatal care, Ethiopia

I introduce a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent-level heterogeneity in link surplus (degree heterogeneity). Like in fixed effects panel data analyses, the joint distribution of observed and unobserved agent-level characteristics is left unrestricted. Two estimators for the (common) homophily parameter, β₀, are developed and their properties studied under an asymptotic sequence involving a single network growing large. The first, tetrad logit (TL), estimator conditions on a sufficient statistic for the degree heterogeneity. The second, joint maximum likelihood (JML), estimator treats the degree heterogeneity $\\left\\{ {{A_{i0}}} \\right\\}_{i = 1}^N$ as additional (incidental) parameters to be estimated. The TL estimate is consistent under both sparse and dense graph sequences, whereas consistency of the JML estimate is shown only under dense graph sequences.

Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form, the model is not identifiable without specifying a parametric outcome distribution. Although it is fundamental for valid statistical inference, identifiability under nonignorable missing mechanisms is not established for many commonly used models. In this article, we first demonstrate identifiability of the normal distribution under monotone missing mechanisms. We then extend it to the normal mixture and t mixture models with nonmonotone missing mechanisms. We discover that models under the Logistic missing mechanism are less identifiable than those under the Probit missing mechanism. We give necessary and sufficient conditions for identifiability of models under the Logistic missing mechanism, which sometimes can be checked in real data analysis. We illustrate our methods using a series of simulations, and apply them to a real-life dataset. Supplementary materials for this article are available online.