Estimating disease progression using panel data.
Continuous-time Markov processes are frequently used to describe the evolution of a disease over different phases. Such modeling can provide estimates for important parameters that are defined on the paths of the process. A simple example is the mean first hitting time to a set of states. However, more interesting events are defined by several time points such as the first time the process stays in state j for at least Delta time units. These kinds of events are very important in relapsing-remitting diseases such as in multiple sclerosis (MS) where the focus is on a sustained worsening that lasts 6 months or longer. The current paper considers data on independent continuous Markov processes that are only observed intermittently. It reviews modeling and estimation, presents a new general concept of hitting times, and provides point and interval estimates for it. The methodology is applied to data from a phase III clinical trial of Avonex--a drug given to MS patients.
Mandel
Micha
M
Department of Statistics, The Hebrew University, Jerusalem, Israel. msmic@huji.ac.il
D000465:Algorithms; D017326:Clinical Trials, Phase III as Topic; D003198:Computer Simulation; D016001:Confidence Intervals; D004185:Disability Evaluation; D018450:Disease Progression; D006801:Humans; D007155:Immunologic Factors / Q000627:therapeutic use; D000068556:Interferon beta-1a; D016899:Interferon-beta / Q000627:therapeutic use; D008390:Markov Chains; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000175:diagnosis / Q000188:drug therapy; D011336:Probability; D012720:Severity of Illness Index
D007155:Immunologic Factors; D016899:Interferon-beta; D000068556:Interferon beta-1a