A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Clinical factors associated with growth and mortality of pediatric patients on HAART at Mulago PIDC, Uganda, 2003--2006

The global social and economic burden of HIV/AIDS is great, with over forty million people reported to be living with HIV/AIDS at the end of 2005; two million of these are children from birth to 15 years of age. Antiretroviral therapy has been shown to improve growth and survival of HIV-infected individuals. The purpose of this study is to describe a cohort of HIV-infected pediatric patients and assess the association between clinical factors, with growth and mortality outcomes. ^ This was a historical cohort study. Medical records of infants and children receiving HIV care at Mulago Pediatric Infectious Disease Clinic (PIDC) in Uganda between July 2003 and March 2006 were analyzed. Height and weight measurements were age and sex standardized to Centers for Disease Control and prevention (CDC) 2000 reference. Descriptive and logistic regression analyses were performed to identify covariates associated with risk of stunting or being underweight, and mortality. Longitudinal regression analysis with a mixed model using autoregressive covariance structure was used to compare change in height and weight before and after initiation of highly active antiretroviral therapy (HAART). ^ The study population was comprised of 1059 patients 0-20 years of age, the majority of whom were aged thirteen years and below (74.6%). Mean height-for-age before initiation of HAART was in the 10th percentile, mean weight-for-age was in the 8th percentile, and the mean weight-for-height was in the 23rd percentile. Initiation of HAART resulted in improvement in both the mean standardized weight-for-age Z score and weight-for-age percentiles (p <0.001). Baseline age, and weight-for-age Z score were associated with stunting (p <0.001). A negative weight-for-age Z score was associated with stunting (OR 4.60, CI 3.04-5.49). Risk of death decreased from 84% in the >2-8 years age category to 21% in the >13 years age category respectively, compared to the 0-2 years of age (p <0.05). ^ This pediatric population gained weight significantly more rapidly than height after starting HAART. A low weight-for-age Z score was associated with poor survival in children. These findings suggest that age, weight, and height measurements be monitored closely at Mulago PIDC. ^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^