Comparison of multistate Markov modeling with contemporary outcomes in a reanalysis of the NINDS tissue plasminogen activator for acute ischemic stroke treatment trial

Abstract

Historically, ordinal measures of functional outcome have been dichotomized for the primary analysis in acute stroke therapy trials. A number of alternative methods to analyze the ordinal scales have been proposed, with an emphasis on maintaining the ordinal structure as much as possible. In addition, despite the availability of longitudinal outcome data in many trials, the primary analysis consists of a single endpoint. Inclusion of information about the course of disease progression allows for a more complete understanding of the treatment effect. Multistate Markov modeling, which allows for the full ordinal scale to be analyzed longitudinally, is compared with previously suggested analytic techniques for the ordinal modified Rankin Scale (dichotomous-logistic regression; continuous-linear regression; ordinal- shift analysis, proportional odds model, partial proportional odds model, adjacent categories logit model; sliding dichotomy; utility weights; repeated measures). In addition, a multistate Markov model utilizing an estimate of the unobservable baseline outcome derived from principal component analysis is compared Each of the methods is used to re-analyze the National Institute of Neurological Diseases and Stroke tissue plasminogen activator study which showed a consistently significant effect of tissue plasminogen activator using a global test of four dichotomized outcomes in the analysis of the primary outcome at 90 days post-stroke in the primary analysis. All methods detected a statistically significant treatment effect except the multistate Markov model without predicted baseline (p = 0.053). This provides support for the use of the estimated baseline in the multistate Markov model since the treatment effect is able to be detected with its inclusion. Multistate Markov modeling allows for a more refined examination of treatment effect and describes the movement between modified Rankin Scale states over time which may provide more clinical insight into the treatment effect. Multistate Markov models are feasible and desirable in describing treatment effect in acute stroke therapy trials.

Treatment effect on ordinal functional outcome using piecewise multistate Markov model with unobservable baseline: an application to the modified Rankin scale

“…None of the previously mentioned methods utilize the repeated measures even though outcome is collected over time. In fact, a literature search for repeated measures analysis of acute stroke trial data only returned four articles where a generalized estimating equations approach was used for repeated measures analysis (Cassarly et al, 2017a; Cassarly et al, 2017b; Feng et al, 2011; Palesch et al, 2015).…”

Cost-effectiveness of seven-days-per-week rehabilitation schedule for acute stroke patients

“…The Markov model was a simulation model in which transition states and transition probabilities, as well as patients’ moves between the transition states at a Markov cycle are defined. The Markov model has been applied to simulations of patients with various diseases [ 18 , 19 ] and with stroke patients [ 20 , 21 ].…”

Cost-effectiveness of Seven-days-per-week Rehabilitation Schedule for Acute Stroke Patients

“…The Markov model was a simulation model in which transition states and transition probabilities, as well as patients' moves between the transition states at a Markov cycle (i.e., the frequency at which patients move), are de ned. The Markov model has been applied to simulations of patients with various diseases [18,19] and with stroke patients [20,21]. In this study, three states-mRS0-2 (functionally independent), mRS3-5 (disabled), and mRS6 (death)-were de ned.…”