Multistate Markov Models for Ordinal Functional Outcomes of Acute Onset Disease: Application in Acute Stroke Therapy Trials

The modified Rankin Scale (mRS), a seven-point ordinal scale ranging from no symptoms to death, is the most commonly used outcome measures in acute stroke therapy trials. Often, one visit is chosen for the primary analysis, and the scale is dichotomized leading to loss of information. Recently, alternative methods for analyzing the mRS have been explored. In addition, acute onset conditions require immediate attention and treatment, posing a challenge to assess baseline outcome measures for clinical trials. Thus, the mRS is not obtainable at baseline. Much of the progression or recovery experienced by a patient suffering from an acute onset disease is expected to occur early on. Moreover, typically, the goal of a treatment or therapeutic action is improvement in patient health compared to their baseline measure. To accurately quantify improvement, a measure of the outcome at baseline is ideal. This dissertation first explores the feasibility of multistate Markov models for the analysis of the mRS which allow for the full ordinal scale as well as the repeated measures data to be incorporated. The operating characteristics (type I error and power) of the multistate Markov model are compared with those from repeated logistic regression. Next, a framework is developed to predict and incorporate the latent baseline mRS score in a piecewise-constant multistate model. The last part of this work applies the piecewise-constant latent baseline model to real acute stroke trial data and compares the results with alternative methods for analysis of the mRS

Individualized Response to Semantic Versus Phonological Aphasia Therapies in Stroke

Attempts to personalize aphasia treatment to the extent where it is possible to reliably predict individual response to a particular treatment have yielded inconclusive results. The current study aimed to (i) compare the effects of phonologically versus semantically focussed naming treatment and (ii) examine biographical and neuropsychological baseline factors predictive of response to each treatment. One hundred and four individuals with chronic post-stroke aphasia underwent 3 weeks of phonologically focussed treatment and 3 weeks of semantically focussed treatment in an unblinded cross-over design. A linear mixed-effects model was used to compare the effects of treatment type on proportional change in correct naming across groups. Correlational analysis and stepwise regression models were used to examine biographical and neuropsychological predictors of response to phonological and semantic treatment across all participants. Last, chi-square tests were used to explore the association between treatment response and phonological and semantic deficit profiles. Semantically focussed treatment was found to be more effective at the group-level, independently of treatment order (P = 0.041). Overall, milder speech and language impairment predicted good response to semantic treatment (r range: 0.256–0.373) across neuropsychological tasks. The Western Aphasia Battery-Revised Spontaneous Speech score emerged as the strongest predictor of semantic treatment response (R2 = 0.188). Severity of stroke symptoms emerged as the strongest predictor of phonological treatment response (R2 = 0.103). Participants who showed a good response to semantic treatment were more likely to present with fluent speech compared to poor responders (P = 0.005), whereas participants who showed a good response to phonological treatment were more likely to present with apraxia of speech (P = 0.020). These results suggest that semantic treatment may be more beneficial to the improvement of naming performance in aphasia than phonological treatment, at the group-level. In terms of personalized predictors, participants with relatively mild impairments and fluent speech responded better to semantic treatment, while phonological treatment benefitted participants with more severe impairments and apraxia of speech

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

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

Sankey plot of NINDS t-PA treatment group mRS scores over time (months).

<p>Sankey plot of NINDS t-PA treatment group mRS scores over time (months).</p

General MSMM for panel observed mRS data.

<p>General MSMM for panel observed mRS data.</p

Sankey plot of NINDS t-PA control group mRS scores over time (months).

<p>Sankey plot of NINDS t-PA control group mRS scores over time (months).</p

Results from previously used methods for analysis of the mRS.

<p>Results from previously used methods for analysis of the mRS.</p

NINDS t-PA 90 day mRS counts (%).

<p>NINDS t-PA 90 day mRS counts (%).</p

Summary of statistics obtained from each type of analysis of the mRS.

<p>Summary of statistics obtained from each type of analysis of the mRS.</p