Subject(s)
Capacity Building/organization & administration , Coronavirus Infections/epidemiology , Health Information Systems/organization & administration , Pneumonia, Viral/epidemiology , Public Health Administration , Betacoronavirus , COVID-19 , Capacity Building/standards , Data Accuracy , Data Management/organization & administration , Health Information Systems/standards , Humans , Local Government , Pandemics , SARS-CoV-2 , United StatesABSTRACT
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
Subject(s)
Linear Models , Logistic Models , Computer Simulation , HumansABSTRACT
Yuan and Chan (Psychometrika, 76, 670-690, 2011) recently showed how to compute the covariance matrix of standardized regression coefficients from covariances. In this paper, we describe a method for computing this covariance matrix from correlations. Next, we describe an asymptotic distribution-free (ADF; Browne in British Journal of Mathematical and Statistical Psychology, 37, 62-83, 1984) method for computing the covariance matrix of standardized regression coefficients. We show that the ADF method works well with nonnormal data in moderate-to-large samples using both simulated and real-data examples. R code (R Development Core Team, 2012) is available from the authors or through the Psychometrika online repository for supplementary materials.
Subject(s)
Data Interpretation, Statistical , Models, Statistical , Regression Analysis , Psychometrics , Statistics as TopicABSTRACT
With fixed predictors, the standard method (Cohen, Cohen, West, & Aiken, 2003, p. 86; Harris, 2001, p. 80; Hays, 1994, p. 709) for computing confidence intervals (CIs) for standardized regression coefficients fails to account for the sampling variability of the criterion standard deviation. With random predictors, this method also fails to account for the sampling variability of the predictor standard deviations. Nevertheless, under some conditions the standard method will produce CIs with accurate coverage rates. To delineate these conditions, we used a Monte Carlo simulation to compute empirical CI coverage rates in samples drawn from 36 populations with a wide range of data characteristics. We also computed the empirical CI coverage rates for 4 alternative methods that have been discussed in the literature: noncentrality interval estimation, the delta method, the percentile bootstrap, and the bias-corrected and accelerated bootstrap. Our results showed that for many data-parameter configurations--for example, sample size, predictor correlations, coefficient of determination (R²), orientation of ß with respect to the eigenvectors of the predictor correlation matrix, RX--the standard method produced coverage rates that were close to their expected values. However, when population R² was large and when ß approached the last eigenvector of RX, then the standard method coverage rates were frequently below the nominal rate (sometimes by a considerable amount). In these conditions, the delta method and the 2 bootstrap procedures were consistently accurate. Results using noncentrality interval estimation were inconsistent. In light of these findings, we recommend that researchers use the delta method to evaluate the sampling variability of standardized regression coefficients.
Subject(s)
Confidence Intervals , Data Interpretation, Statistical , Models, StatisticalABSTRACT
OBJECTIVE: To describe the composition and duties of local boards of health (LBOHs). DESIGN: An online and written survey was utilized for data collection. The survey included demographics, roles and responsibilities, orientation and training, and concerns and needs of LBOHs. SETTING: This article seeks to expand what limited information we have on the composition and duties of LBOHs as an important foundational step in analyzing the role of LBOHs in leveraging improved public health outcomes. PARTICIPANTS: In 2011, the mixed methods survey was sent to a random sample of 2420 LBOHs in the 41 states, which meet the definition of having LBOHs. MAIN OUTCOME MEASURE: The data represent responses from 353 LBOHs in 35 states. RESULTS: Elected officials appoint members of 68% of LBOHs. The average board consists of a 7-member, county-based LBOH made up primarily of males (60%) and whites (96%). Hispanics make up 9% of boards. The majority of LBOH chairs have a graduate degree but no formal education or experience in public health. Local boards of health report reviewing public health regulations as their most common power but list recommending the approval of the budget for the local health department as boards' most frequent activity in the past 3 years. CONCLUSIONS: LBOH members and chairs are more similar in demographics to the top executives at local health departments than the general population or the public health workforce. Most LBOH chairs, however, lack experience in public health, and a quarter or more of LBOHs do not use their powers to set or recommend health priorities as a mechanism to leverage better community health outcomes.
