Statistical methods for the analysis of relapse data in MS clinical trials.
Y. Wang, L. Meyerson, Y. Tang, and N. Qian. Journal of the neurological sciences, 285 (1-2):
206-11(October 2009)5489<m:linebreak></m:linebreak>JID: 0375403; 0 (Antibodies, Monoclonal); 0 (Immunologic Factors); 0 (natalizumab); 2009/03/05 received; 2009/07/15 revised; 2009/07/22 accepted; 2009/08/25 aheadofprint; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Count data.
DOI: 10.1016/j.jns.2009.07.017
Abstract
Patients with multiple sclerosis (MS) often experience unpredictable recurrent relapses with periods of remission. The modeling of MS relapse data is complicated because both within-subject serial dependence between relapses and between-patient heterogeneity may exist. We compare six statistical methods for assessing the treatment efficacy in reducing the frequency of relapses in MS clinical trials. All methods can be implemented in SAS, and are grouped into two classes, one based on Poisson-type regressions for count data and the other on Cox proportional hazards models for time to relapse. We apply these models to the data of a Tysabri (Natalizumab) MS trial and interpret the differences in results based on the underlying assumptions. Negative binomial regression is recommended for evaluating the overall treatment effect because of its simplicity and efficiency.
%0 Journal Article
%1 Wang2009
%A Wang, Y C
%A Meyerson, L
%A Tang, Y Q
%A Qian, N
%D 2009
%J Journal of the neurological sciences
%K Adult Antibodies BinomialDistribution BiomedicalResearch BiomedicalResearch:methods ClinicalTrialsasTopic Computers Female Humanized Humans ImmunologicFactors ImmunologicFactors:therapeuticuse Male Models Monoclonal Monoclonal:therapeuticuse MultipleSclerosis MultipleSclerosis:drugtherapy MultipleSclerosis:therapy PoissonDistribution ProportionalHazardsModels Recurrence RegressionAnalysis Software Statistical TimeFactors TreatmentOutcome
%N 1-2
%P 206-11
%R 10.1016/j.jns.2009.07.017
%T Statistical methods for the analysis of relapse data in MS clinical trials.
%U http://www.ncbi.nlm.nih.gov/pubmed/19709676
%V 285
%X Patients with multiple sclerosis (MS) often experience unpredictable recurrent relapses with periods of remission. The modeling of MS relapse data is complicated because both within-subject serial dependence between relapses and between-patient heterogeneity may exist. We compare six statistical methods for assessing the treatment efficacy in reducing the frequency of relapses in MS clinical trials. All methods can be implemented in SAS, and are grouped into two classes, one based on Poisson-type regressions for count data and the other on Cox proportional hazards models for time to relapse. We apply these models to the data of a Tysabri (Natalizumab) MS trial and interpret the differences in results based on the underlying assumptions. Negative binomial regression is recommended for evaluating the overall treatment effect because of its simplicity and efficiency.
%@ 1878-5883; 1878-5883
@article{Wang2009,
abstract = {Patients with multiple sclerosis (MS) often experience unpredictable recurrent relapses with periods of remission. The modeling of MS relapse data is complicated because both within-subject serial dependence between relapses and between-patient heterogeneity may exist. We compare six statistical methods for assessing the treatment efficacy in reducing the frequency of relapses in MS clinical trials. All methods can be implemented in SAS, and are grouped into two classes, one based on Poisson-type regressions for count data and the other on Cox proportional hazards models for time to relapse. We apply these models to the data of a Tysabri (Natalizumab) MS trial and interpret the differences in results based on the underlying assumptions. Negative binomial regression is recommended for evaluating the overall treatment effect because of its simplicity and efficiency.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Wang, Y C and Meyerson, L and Tang, Y Q and Qian, N},
biburl = {https://www.bibsonomy.org/bibtex/2f5c1bbc1fc7804ecc2e8d755427e8cd5/jepcastel},
city = {Department of Biometrics, Biogen Idec, 14 Cambridge Center, Cambridge, MA 02142, USA. yong-cheng.wang@biogenidec.com},
doi = {10.1016/j.jns.2009.07.017},
interhash = {5b0bc45cda17fbe855d4d66a87d6e9d6},
intrahash = {f5c1bbc1fc7804ecc2e8d755427e8cd5},
isbn = {1878-5883; 1878-5883},
issn = {1878-5883},
journal = {Journal of the neurological sciences},
keywords = {Adult Antibodies BinomialDistribution BiomedicalResearch BiomedicalResearch:methods ClinicalTrialsasTopic Computers Female Humanized Humans ImmunologicFactors ImmunologicFactors:therapeuticuse Male Models Monoclonal Monoclonal:therapeuticuse MultipleSclerosis MultipleSclerosis:drugtherapy MultipleSclerosis:therapy PoissonDistribution ProportionalHazardsModels Recurrence RegressionAnalysis Software Statistical TimeFactors TreatmentOutcome},
month = {10},
note = {5489<m:linebreak></m:linebreak>JID: 0375403; 0 (Antibodies, Monoclonal); 0 (Immunologic Factors); 0 (natalizumab); 2009/03/05 [received]; 2009/07/15 [revised]; 2009/07/22 [accepted]; 2009/08/25 [aheadofprint]; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; Count data},
number = {1-2},
pages = {206-11},
pmid = {19709676},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Statistical methods for the analysis of relapse data in MS clinical trials.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19709676},
volume = 285,
year = 2009
}