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OpenLearning

Commonly Used Biostatistical Methods for Analysing Longitudinal Data in Health Research

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Overview

Dr Haider Mannan is a Senior Lecturer in Biostatistics at Translational Health Research Institute and the School of Medicine, Western Sydney University.

His biostatistical expertise is wide including regression models for clustered and longitudinal data (e.g. multilevel mixed effects, GEE models), continuous, binary (e.g. logistic regression), survival (e.g. Cox regression, Weibull regression), categorical and ordinal outcomes, nonparametric methods, analysis of small studies and state transition models (e.g. Markov simulation/model, latent Markov model), to note a few, all in the context of epidemiological/health studies. He has published several software in peer reviewed journals for disease risk modelling using SAS macros.

He has published two monographs, 80 peer reviewed articles in epidemiology/biostatistics with 32 as first authored and 42 in Q1 journals including International Journal of Obesity, Obesity, Appetite, International Journal of Eating Disorders, American Journal of Epidemiology, BMJ Open, Preventive Medicine, European Journal of Preventive Cardiology, Annals of Epidemiology, European Journal of Nutrition, Accident Analysis and Prevention, PLos One and Statistical Methods in Medical Research. He teaches and coordinates a biostatistics unit for Master of Epidemiology, MPH and MHSc courses during spring semester. This unit focuses on application of commonly used multivariate statistical methods in epidemiology and public health. He has excelled in teaching this unit having obtained a perfect score of 5 in spring 2019-2023. He also coordinates Epidemiology of Climate Change and teaches introductory biostatistics to MBBS and MD students, conducts internal workshops on multilevel and multivariate regression models.

He earned a Ph.D. in biostatistics and epidemiology from University of Western Australia with distinction in 2008, and then worked for 6 years at the Monash University Department of Epidemiology and Preventive Medicine and James Cook University as Senior Research Fellow.

Syllabus

  • Identify advantages of longitudinal study design; multilevel data structures for longitudinal studies; assumptions, pros and cons of multilevel & GEE models; their common correlation structures; sample size & number of waves for fitting them.
  • Fit and interpret multilevel linear models under simple and complex longitudinal study designs such as nested, crossed effects, and repeated measures studies with time-varying covariates.
  • Fit and interpret multilevel logistic, Poisson and its variants regressions under longitudinal study designs; segmented linear model under interrupted time series and the location-scale model under ecological momentary analysis.
  • Fit and interpret GEE linear, logistic, Poisson and its variants regressions under various longitudinal study designs.
  • Issues with sample size & number of waves for fitting MLMs & GEE models.
  • Common correlation structures for MLMs & GEE models.
  • Both 2- and 3-level random intercept only, random intercept & random coefficient models, estimating their model fit & intraclass correlation at each level.
  • Both large sample normal theory-based & small sample-based methods for fitting MLMs.
  • Fitting MLMs under complex study designs, e.g., nested, crossed effects, interruptive time series, ecological momentary analysis, repeated measures studies etc.
  • MLMs with heteroscedastic as well as independent & correlated residuals
  • 2-level logistic growth models including modelling non-linear effects of time, covariates & time-covariate interactions & predicting marginal probabilities.
  • 2-level Poisson growth (linear) model & estimation of posterior mean using Bayesian analysis.
  • Pros and cons of MLM & GEE models.
  • Fitting MLMs in presence of missing data.

Taught by

Western Sydney University

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