# 2016-Biostatistics: Methods and Applications 2

Biostatistics: Methods and Applications 2
Chair: Joseph Beyene (McMaster University)
[PDF]

YANG JIAN, University of Calgary
Minimum Hellinger Distance Estimation for Linear Regression Model  [PDF]

Minimum Hellinger Distance estimation (MHDE) has been shown an appealing method of estimation for discrete data when the assumed model is suspected to be true. In this presentation, we first introduce Minimum Hellinger Distance (MHD) as well as the MHDE. Then, we will derive the MHDE for linear regression model. Some properties of the estimator are discussed and a comparison with MLE is carried out through Monte Carlo simulation studies. Lastly, we will apply this method to a breast cancer data set and demonstrate its implementation and efficiency in estimation.

ERIN LUNDY, University of Western Ontario
Analyzing Heaped Counts and Longitudinal Presence/Absence Data in Joint Zero-inflated Poisson Regression Models  [PDF]

Recurrent event data where a fraction of subjects are not at-risk for an event are frequently seen in longitudinal studies. In many settings, an aggregate count of the number of self-reported events over an observation period is recorded. Self-reported counts are often subject to heaping which yields a distorted distribution for the observed counts and therefore may bias estimation. Alternatively, the presence/absence of events between shorter periodic assessments may be recorded. Motivated by a major study of criminal behaviour, we compare the analysis of aggregate heaped count data and longitudinal presence/absence data using joint zero-inflated Poisson regression models.

RACHID BENTOUMI, Université d'Ottawa
Information Gain under Length-biased Sampling [PDF]

In epidemiological studies, subjects with disease (prevalent cases) differ from newly diseased (incident) cases. Methods for regression analyses have recently been proposed to measure the potential effects of covariates on survival. We propose to extend the measure of dependence based on information gain in the context of length-biased sampling. This will require development of an estimator for the covariate distribution. We will assess the asymptotic properties of the measure of dependence and estimated information gain and illustrate the methods using both simulation and application to the Canadian Study on Health.