Literature DB >> 20530124

Optimal design and analysis of phase I/II clinical trials in multiple sclerosis with gadolinium-enhanced lesions as the endpoint.

Brian C Healy1, David Ikle, Eric A Macklin, Gary Cutter.   

Abstract

Many phase I/II clinical trials in multiple sclerosis use gadolinium-enhanced lesions as the outcome measure. The best scanning interval and analysis for this outcome has not been determined. The objective of this study was to compare timing schemes and analysis techniques in terms of power for phase I/II clinical trials. Data were simulated under four scenarios assuming a negative binomial distribution for the number of new lesions and an exponential distribution for the duration of enhancement. The first scenario assumed an immediate treatment effect on the number of new lesions, while the second scenario assumed a delayed treatment effect. The third scenario assumed a higher proportion of patients had no new lesions, and the final scenario assumed an immediate treatment effect on the duration of enhancement. For each scenario, power for a six-month trial with 100 patients per arm was calculated using 10 analysis strategies. The scanning intervals tested were monthly scans, bimonthly scans and a single end-of-study scan. In addition, cost-effectiveness of each trial design and analysis was compared. Negative binomial regression models for the total number of new lesions were the most powerful analyses under an immediate treatment effect, and repeated measures models with a categorical time effect were the most powerful analyses under a delayed treatment effect. Although monthly scans generally provided most power, this design was also most costly. Designs with fewer scans per patient provide similar power and are more cost-effective. Negative binomial regression models are more powerful than non-parametric approaches.

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Year:  2010        PMID: 20530124     DOI: 10.1177/1352458510371409

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  1 in total

1.  Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

Authors:  Nieves Velez de Mendizabal; Matthew M Hutmacher; Iñaki F Troconiz; Joaquín Goñi; Pablo Villoslada; Francesca Bagnato; Robert R Bies
Journal:  PLoS One       Date:  2013-09-05       Impact factor: 3.240

  1 in total

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