October 13-15, 2005
Julie Horrocks, University of Guelph
We compare methods for predicting a binary response from longitudinal data with missing values. A simple approach is to use summary measures of the non-missing longitudinal data (such as the mean, slope, or maximum value) as the predictor variable in a logistic regression model. Another approach is to fit a mixed linear model with random slopes and/or intercepts to the longitudinal data, and use the random coefficients as predictors in the logistic regression model. A Bayesian model is also examined. The methods are applied to a data set on adhesion of certain blood lymphocytes (CD56bright cells) in infertile women. It is thought that the shape of the longitudinal profiles of adhesion measurements over time can be used to predict the success of infertility treatments. This research is funded by CHRP and NSERC.
Tulay Koru-Sengul, University of Saskatchewan
He (Daniel) Li and Liqun Wang, University of Manitoba
The main approach for the estimation of nonlinear mixed effects models focuses on the maximum likelihood method. Given the current computing capacity, intensive numerical integration often makes exact maximum likelihood estimation impractical. We propose two estimators for nonlinear mixed effects models where the distributions of the regression random errors are nonparametric and those of random effects are parametric but not necessarily normal. These estimators are based on the first two conditional moments of the response variable given the observed predictor variables. We present numerical examples demonstrating that these estimators are computationally feasible and practical, and they perform quite satisfactorily even for relatively small sample sizes.
Zhenguo Qiu, University of Northern British Columbia
Previous studies have reported variation in Canadian neonatal intensive care units (NICU) length of stay (LOS) but little is known about the reason for this variation. We examine predictors of NICU LOS using Bayesian hierarchical modeling methods. Variations in NICU LOS were examined and quantified, accounting for patient risks at admission, NICU characteristics and patient-NICU interaction.
Forty-five percent of the variation in NICU LOS was attributable to patient risks and 13% to NICU characteristics. Neonatologist-medical staff ratio was partially responsible for longer NICU LOS among neonates with lethal congenital anomalies and neonates with complete maternal antenatal corticosteroid treatment. Availability of specialized services was associated with longer NICU LOS among neonates with complete maternal antenatal corticosteroid treatment. Also, neonates admitted to NICUs with high patient intake volume and high neonatologist-medical staff ratios tended to be longer in NICU LOS.
Annie Qu, Oregon State University
We develop a consistent and highly efficient marginal model for missing
at random data using estimating function approaches. Our approach differs
from Robins et al.'s (1995) weighted estimating equations and Paik's (1997)
imputation method in that our approach does not require knowing the missing
mechanism, and does not require estimating the probability of missing
based on an assumed model. Under the missing at random assumption we are
able to formulate unbiased estimating functions which will guarantee an
unbiased estimator, and further we also show that the unbiased estimating
function is efficient using the idea of projection and semiparametric
efficient bounds (Bickel et al.,
Peng Zhang, University of Waterloo
This paper presents a new class of non-normal linear mixed models that offers an efficient estimation of the disease progression in the analysis of the longitudinal data from the MDRD (Modification of Diet in Renal Disease) trial. This new analysis utilizes the finding that the distribution of random effects is negatively skewed from both a preliminary data analysis and two previous analyses. We assume a log-gamma distribution for the random effects and provide the maximum likelihood inference in the resulting non-normal linear mixed model. To validate the adequacy of the log-gamma assumption versus the usual normality assumption for the random effects, we propose a lack-of-fit test that clearly indicates a better fit of the log-gamma modeling in analysis of MDRD data. This full maximum likelihood inference is advantageous to deal with the MAR type of dropouts encountered in the MDRD data.
Back to Workshop index