Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Cassarly, Christy; Martin, Renée; Chimowitz, Marc I.; Peña, Edsel A.; Ramakrishnan, Viswanathan; Palesch, Yuko Y.

doi:10.1080/03610918.2016.1222425

Cited by 8 publications

(13 citation statements)

References 53 publications

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“…However, our study was rigorously conducted in terms of frequency of fecal sample collection, duration of follow-up, and number of participants in a nonoutbreak setting. The use of multistate models has been shown to preserve power with modest sample size given more frequent follow-ups ( 9 ).…”

Section: Discussionmentioning

confidence: 99%

Duration of Carbapenemase-Producing Enterobacteriaceae Carriage in Hospital Patients

Yin¹,

Hernández-Koutoucheva²,

Musicha³

et al. 2020

Emerg. Infect. Dis.

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R apid global dissemination of carbapenemaseproducing Enterobacteriaceae (CPE) poses a public health threat (1). To prevent the spread of CPE in healthcare settings, international guidelines advocate for early identification, isolation, and contact precautions (2,3). To provide information helpful for the design of rational infection control policies, we estimated CPE carriage duration in a hospital cohort and identified risk factors for prolonged carriage.

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Section: Discussionmentioning

confidence: 99%

Duration of Carbapenemase-Producing Enterobacteriaceae Carriage in Hospital Patients

Yin¹,

Hernández-Koutoucheva²,

Musicha³

et al. 2020

Emerg. Infect. Dis.

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“…A number of alternative methods for ordinal outcome data have received attention in recent years (Cassarly et al, 2017a). Linear regression and analysis of variance have been suggested where the ordinal outcome is treated as a continuous variable.…”

Section: Summary and Discussionmentioning

confidence: 99%

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

Cassarly

Martin

Chimowitz

et al. 2018

Journal of Biopharmaceutical Statistics

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In clinical trials, longitudinally assessed ordinal outcomes are commonly dichotomized and only the final measure is used for primary analysis, partly for ease of clinical interpretation. Dichotomization of the ordinal scale and failure to utilize the repeated measures can reduce statistical power. Additionally, in certain emergent settings, the same measure cannot be assessed at baseline prior to treatment. For such a data set, a piecewise-constant multistate Markov model that incorporates a latent model for the unobserved baseline measure is proposed. These models can be useful in analyzing disease history data and are advantageous in clinical applications where a disease process naturally moves through increasing stages of severity. Two examples are provided using acute stroke clinical trials data. Conclusions drawn in this article are consistent with those from the primary analysis for treatment effect in both of the motivating examples. Use of these models allows for a more refined examination of treatment effect and describes the movement between health states from baseline to follow-up visits which may provide more clinical insight into the treatment effect.

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“…Most recently, the Multistate Markov model (MSMM) was proposed for analysis of the mRS [ 14 ]. The MSMM analyzes repeated measures data with ordinal outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…These types of models describe how a subject moves between a series of disease states over time, which is desirable in the description of disease processes that naturally move through increasing stages of severity [ 15 ]. Results suggest that the MSMM can be a more efficient approach than dichotomized methods to analyze the mRS data in some scenarios [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Another benefit of MSMMs is the potential for decreased sample size. The power of MSMMs applied to acute onset clinical trial data was shown to increase significantly when the number of follow-up visits was increased [ 14 ]. Future trials could collect the mRS more frequently, increasing the power to detect differences using this modeling technique.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Cited by 8 publications

References 53 publications

Duration of Carbapenemase-Producing Enterobacteriaceae Carriage in Hospital Patients

Duration of Carbapenemase-Producing Enterobacteriaceae Carriage in Hospital Patients

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

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

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