@article {riddell_adaptive_2013,
	title = {An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context},
	journal = {Statistical Methods in Medical Research},
	volume = {25},
	year = {2016},
	pages = {1330-1345},
	abstract = {<p>The biomarker-adaptive threshold design (BATD) allows researchers to simultaneously study the efficacy of treatment in the overall group and to investigate the relationship between a hypothesized predictive biomarker and the treatment effect on the primary outcome. It was originally developed for survival outcomes for Phase III clinical trials where the biomarker of interest is measured on a continuous scale. In this paper, generalizations of the BATD to accommodate count biomarkers and outcomes are developed and then studied in the multiple sclerosis (MS) context where the number of relapses is a commonly used outcome. Through simulation studies, we find that the BATD has increased power compared with a traditional fixed procedure under varying scenarios for which there exists a sensitive patient subgroup. As an illustration, we apply the procedure for two hypothesized markers, baseline enhancing lesion count and disease duration at baseline, using data from a previously completed trial. MS duration appears to be a predictive marker relationship for this dataset, and the procedure indicates that the treatment effect is strongest for patients who have had MS for less than 7.8 years. The procedure holds promise of enhanced statistical power when the treatment effect is greatest in a sensitive patient subgroup.</p>
},
	keywords = {adaptive designs, Biomarkers, clinical trials, negative binomial regression},
	doi = {10.1177/0962280213480576},
	url = {http://smm.sagepub.com/content/early/2013/04/01/0962280213480576},
	author = {Riddell, CA and Zhao, Y and Petkau, J}
}
@article {mackay_altman_longitudinal_2012,
	title = {A longitudinal model for magnetic resonance imaging lesion count data in multiple sclerosis patients},
	journal = {Statistics in Medicine},
	volume = {31},
	number = {5},
	year = {2012},
	pages = {449{\textendash}469},
	abstract = {<p>Magnetic resonance imaging (MRI) data are routinely collected at multiple time points during phase 2 clinical trials in multiple sclerosis. However, these data are typically summarized into a single response for each patient before analysis. Models based on these summary statistics do not allow the exploration of the trade-off between numbers of patients and numbers of scans per patient or the development of optimal schedules for MRI scanning. To address these limitations, in this paper, we develop a longitudinal model to describe one MRI outcome: the number of lesions observed on an individual MRI scan. We motivate our choice of a mixed hidden Markov model based both on novel graphical diagnostic methods applied to five real data sets and on conceptual considerations. Using this model, we compare the performance of a number of different tests of treatment effect. These include standard parametric and nonparametric tests, as well as tests based on the new model. We conduct an extensive simulation study using data generated from the longitudinal model to investigate the parameters that affect test performance and to assess size and power. We determine that the parameters of the hidden Markov chain do not substantially affect the performance of the tests. Furthermore, we describe conditions under which likelihood ratio tests based on the longitudinal model appreciably outperform the standard tests based on summary statistics. These results establish that the new model is a valuable practical tool for designing and analyzing multiple sclerosis clinical trials. Copyright {\textcopyright} 2011 John Wiley \&amp; Sons, Ltd.</p>
},
	keywords = {clinical trials, hypothesis testing, latent variables, longitudinal count data, mixed hidden Markov model, model diagnostics},
	doi = {10.1002/sim.4394},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.4394/abstract},
	author = {Altman, RM and Petkau, A.John and Vrecko, D and Smith, A}
}
