Journal of Data Science

Abstract: The application of linear mixed models or generalized linear mixed models to large databases in which the level 2 units (hospitals) have a wide variety of characteristics is a problem frequently encountered in studies of medical quality. Accurate estimation of model parameters and standard errors requires accounting for the grouping of outcomes within hospitals. Including the hospitals as random effect in the model is a common method of doing so. However in a large, diverse population, the required assump tions are not satisfied, which can lead to inconsistent and biased parameter estimates. One solution is to use cluster analysis with clustering variables distinct from the model covariates to group the hospitals into smaller, more homogeneous groups. The analysis can then be carried out within these groups. We illustrate this analysis using an example of a study of hemoglobin A1c control among diabetic patients in a national database of United States Department of Veterans’ Affairs (VA) hospitals.

COVID-19 is quickly spreading around the world and carries along with it a significant threat to public health. This study sought to apply meta-analysis to more accurately estimate the basic reproduction number (R0) because prior estimates of R0 have a broad range from 1.95 to 6.47 in the existing literature. Utilizing meta-analysis techniques, we can determine a more robust estimation of R0, which is substantially larger than that provided by the World Health Organization (WHO). A susceptible-Infectious-removed (SIR) model for the new infection cases based on R0 from meta analysis is proposed to estimate the effective reproduction number Rt. The curves of estimated Rt values over time can illustrate that the isolation measures enforced in China and South Korea were substantially more effective in controlling COVID-19 compared to the measures enacted early in both Italy and the United States. Finally, we present the daily standardized infection cases per million population over time across countries, which is a good index to indicate the effectiveness of isolation measures on the prevention of COVID-19. This standardized infection case determines whether the current infection severity status is out of range of the national health capacity to care for patients.

Abstract: Count data often have excess zeros in many clinical studies. These zeros usually represent “disease-free state”. Although disease (event) free at the time, some of them might be at a high risk of having the putative outcome while others may be at low or no such risk. We postulate these zeros as a one of the two types, either as ‘low risk’ or as ‘high risk’ zeros for the disease process in question. Low risk zeros can arise due to the absence of risk factors for disease initiation/progression and/or due to very early stage of the disease. High risk zeros can arise due to the presence of significant risk factors for disease initiation/ progression or could be, in rare situations, due to misclassification, more specific diagnostic tests, or below the level of detection. We use zero inflated models which allows us to assume that zeros arise from one of the two separate latent processes-one giving low-risk zeros and the other high-risk zeros and subsequently propose a strategy to identify and classify them as such. To illustrate, we use data on the number of involved nodes in breast cancer patients. Of the 1152 patients studied, 38.8% were node- negative (zeros). The model predicted that about a third (11.4%) of negative nodes are “high risk” and the remaining (27.4%) are at “low risk” of nodal positivity. Posterior probability based classification was more appropriate compared to other methods. Our approach indicates that some node negative patients may be re-assessed for their diagnosis about nodal positivity and/or for future clinical management of their disease. The approach developed here is applicable to any scenario where the disease or outcome can be characterized by count-data.

Abstract: Information regarding small area prevalence of chronic disease is important for public health strategy and resourcing equity. This paper develops a prevalence model taking account of survey and census data to derive small area prevalence estimates for diabetes. The application involves 32000 small area subdivisions (zip code census tracts) of the US, with the prevalence estimates taking account of information from the US-wide Behavioral Risk Factor Surveillance System (BRFSS) survey on population prevalence differentials by age, gender, ethnic group and education. The effects of such aspects of population composition on prevalence are widely recognized. However, the model also incorporates spatial or contextual influences via spatially structured effects for each US state; such contextual effects are allowed to differ between ethnic groups and other demographic categories using a multivariate spatial prior. A Bayesian estimation approach is used and analysis demonstrates the considerably improved fit of a fully specified compositional-contextual model as compared to simpler ‘standard’ approaches which are typically limited to age and area effects.

Abstract: We propose two simple, easy-to-implement methods for obtaining simultaneous credible bands in hierarchical models from standard Markov chain Monte Carlo output. The methods generalize Scheff´e’s (1953) approach to this problem, but in a Bayesian context. A small simulation study is followed by an application of the methods to a seasonal model for Ache honey gathering.