%0 Journal Article %1 Friede2010 %A Friede, Tim %A Schmidli, Heinz %D 2010 %J Statistics in medicine %K BinomialDistribution ClinicalTrialsasTopic ClinicalTrialsasTopic:methods DataInterpretation Double-BlindMethod EndpointDetermination EndpointDetermination:methods EpidemiologicResearchDesign Humans MultipleSclerosis PilotProjects PoissonDistribution Relapsing-Remitting Relapsing-Remitting:drugtherapy Relapsing-Remitting:pathology SampleSize Single-BlindMethod Statistical TimeFactors %N 10 %P 1145-56 %R 10.1002/sim.3861 %T Blinded sample size reestimation with count data: methods and applications in multiple sclerosis. %U http://www.ncbi.nlm.nih.gov/pubmed/20146203 %V 29 %X Sample size estimation in clinical trials depends critically on nuisance parameters, such as variances or overall event rates, which have to be guessed or estimated from previous studies in the planning phase of a trial. Blinded sample size reestimation estimates these nuisance parameters based on blinded data from the ongoing trial, and allows to adjust the sample size based on the acquired information. In the present paper, this methodology is developed for clinical trials with count data as the primary endpoint. In multiple sclerosis such endpoints are commonly used in phase 2 trials (lesion counts in magnetic resonance imaging (MRI)) and phase 3 trials (relapse counts). Sample size adjustment formulas are presented for both Poisson-distributed data and for overdispersed Poisson-distributed data. The latter arise from sometimes considerable between-patient heterogeneity, which can be observed in particular in MRI lesion counts. The operation characteristics of the procedure are evaluated by simulations and recommendations on how to choose the size of the internal pilot study are given. The results suggest that blinded sample size reestimation for count data maintains the required power without an increase in the type I error. %@ 1097-0258; 0277-6715

@article{Friede2010, abstract = {Sample size estimation in clinical trials depends critically on nuisance parameters, such as variances or overall event rates, which have to be guessed or estimated from previous studies in the planning phase of a trial. Blinded sample size reestimation estimates these nuisance parameters based on blinded data from the ongoing trial, and allows to adjust the sample size based on the acquired information. In the present paper, this methodology is developed for clinical trials with count data as the primary endpoint. In multiple sclerosis such endpoints are commonly used in phase 2 trials (lesion counts in magnetic resonance imaging (MRI)) and phase 3 trials (relapse counts). Sample size adjustment formulas are presented for both Poisson-distributed data and for overdispersed Poisson-distributed data. The latter arise from sometimes considerable between-patient heterogeneity, which can be observed in particular in MRI lesion counts. The operation characteristics of the procedure are evaluated by simulations and recommendations on how to choose the size of the internal pilot study are given. The results suggest that blinded sample size reestimation for count data maintains the required power without an increase in the type I error.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Friede, Tim and Schmidli, Heinz}, biburl = {https://www.bibsonomy.org/bibtex/2fb445b65a04b60d11d2e5da40e305b0a/jepcastel}, city = {Universitatsmedizin Gottingen, Abteilung Medizinische Statistik, Gottingen, Germany.}, doi = {10.1002/sim.3861}, interhash = {cf4fe90b37e86490f456f0ff5a57d5e7}, intrahash = {fb445b65a04b60d11d2e5da40e305b0a}, isbn = {1097-0258; 0277-6715}, issn = {1097-0258}, journal = {Statistics in medicine}, keywords = {BinomialDistribution ClinicalTrialsasTopic ClinicalTrialsasTopic:methods DataInterpretation Double-BlindMethod EndpointDetermination EndpointDetermination:methods EpidemiologicResearchDesign Humans MultipleSclerosis PilotProjects PoissonDistribution Relapsing-Remitting Relapsing-Remitting:drugtherapy Relapsing-Remitting:pathology SampleSize Single-BlindMethod Statistical TimeFactors}, month = {5}, note = {5506<m:linebreak></m:linebreak>JID: 8215016; aheadofprint;<m:linebreak></m:linebreak>Sample size; Count data}, number = 10, pages = {1145-56}, pmid = {20146203}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Blinded sample size reestimation with count data: methods and applications in multiple sclerosis.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20146203}, volume = 29, year = 2010 }