I work primarily on statistical methods with medical applications. I have particular interest in models for longitudinal data (especially counts and other non-normal responses), survival time models, parameter-driven models, random effects, and latent variables. I have worked extensively in the field of hidden Markov models. Most recently, I have been working on random survival forests and assessing prediction error in survival times. I have applied my research to problems in fields such as multiple sclerosis and ovarian cancer.
Altman, R.M., Altman, A., and Ghatage, P. A Cautionary Note on the Use of the C-Index as a Measure of Prediction Error in Random Survival Forests. Submitted.
Altman, R.M., Petkau, A.J., Vrecko, D., and Smith, A. (2012). MRI-based clinical trials in relapsing-remitting MS: New sample size calculations based on a longitudinal model. Multiple Sclerosis Journal, 18, 1455 - 1463.
Altman, R.M., Petkau, A.J., Vrecko, D., and Smith, A. (2012). A longitudinal model for magnetic resonance imaging lesion count data in multiple sclerosis patients. Statistics in Medicine, 31, 449–469. DOI: 10.1002/sim.4394.
Altman, R.M. (2007). Mixed hidden Markov models: An extension of the hidden Markov model to the longitudinal data setting. JASA, 102, 201-210.
Altman, R.M. and Petkau, A.J. (2005). Application of hidden Markov models to multiple sclerosis lesion count data. Statistics in Medicine, 24, 2335–2344.
Altman, R.M. (2004). Assessing the goodness-of-fit of hidden Markov models. Biometrics, 60, 444–450.
MacKay, R.J. (2002). Estimating the Order of a Hidden Markov Model. The Canadian Journal of Statistics, 30, 573-589.