OBJECTIVE: A new parametric simulation procedure based on the negative binomial (NB) model was used to evaluate the sample sizes needed to achieve optimal statistical powers for parallel groups (with (PGB) and without (PG) a baseline correction scan). It was also used for baseline versus treatment (BVT) design clinical trials in relapsing-remitting (RR) and secondary progressive (SP) multiple sclerosis (MS), when using the number of new enhancing lesions seen on monthly MRI of the brain as the measure of outcome. METHODS: MRI data obtained from 120 untreated patients with RRMS selected for the presence of MRI activity at baseline, 66 untreated and unselected patients with RRMS, and 81 untreated and unselected patients with SPMS were fitted using an NB distribution. All these patients were scanned monthly for at least 6 months and were all from the placebo arms of three large scale clinical trials and one natural history study. The statistical powers were calculated for durations of follow up of 3 and 6 months. RESULTS: The frequency of new enhancing lesions in patients with SPMS was lower, but not significantly different, from that seen in unselected patients with RRMS. As expected, enhancement was more frequent in patients with RRMS selected for MRI activity at baseline than in the other two patient groups. As a consequence, the estimated sample sizes needed to detect treatment efficacy in selected patients with RRMS were smaller than those of unselected patients with RRMS and those with SPMS. Baseline correction was also seen to reduce the sample sizes of PG design trials. An increased number of scans reduced the sample sizes needed to perform BVT trials, whereas the gain in power was less evident in PG and PGB trials. CONCLUSION: This study provides reliable estimates of the sample sizes needed to perform MRI monitored clinical trials in the major MS clinical phenotypes, which should be useful for planning future studies.
OBJECTIVE: A new parametric simulation procedure based on the negative binomial (NB) model was used to evaluate the sample sizes needed to achieve optimal statistical powers for parallel groups (with (PGB) and without (PG) a baseline correction scan). It was also used for baseline versus treatment (BVT) design clinical trials in relapsing-remitting (RR) and secondary progressive (SP) multiple sclerosis (MS), when using the number of new enhancing lesions seen on monthly MRI of the brain as the measure of outcome. METHODS: MRI data obtained from 120 untreated patients with RRMS selected for the presence of MRI activity at baseline, 66 untreated and unselected patients with RRMS, and 81 untreated and unselected patients with SPMS were fitted using an NB distribution. All these patients were scanned monthly for at least 6 months and were all from the placebo arms of three large scale clinical trials and one natural history study. The statistical powers were calculated for durations of follow up of 3 and 6 months. RESULTS: The frequency of new enhancing lesions in patients with SPMS was lower, but not significantly different, from that seen in unselected patients with RRMS. As expected, enhancement was more frequent in patients with RRMS selected for MRI activity at baseline than in the other two patient groups. As a consequence, the estimated sample sizes needed to detect treatment efficacy in selected patients with RRMS were smaller than those of unselected patients with RRMS and those with SPMS. Baseline correction was also seen to reduce the sample sizes of PG design trials. An increased number of scans reduced the sample sizes needed to perform BVT trials, whereas the gain in power was less evident in PG and PGB trials. CONCLUSION: This study provides reliable estimates of the sample sizes needed to perform MRI monitored clinical trials in the major MS clinical phenotypes, which should be useful for planning future studies.
Authors: C Tortorella; B Viti; M Bozzali; M P Sormani; G Rizzo; M F Gilardi; G Comi; M Filippi Journal: Neurology Date: 2000-01-11 Impact factor: 9.910
Authors: P D Molyneux; M Filippi; F Barkhof; C Gasperini; T A Yousry; L Truyen; H M Lai; M A Rocca; I F Moseley; D H Miller Journal: Ann Neurol Date: 1998-03 Impact factor: 10.422
Authors: D H Miller; P S Albert; F Barkhof; G Francis; J A Frank; S Hodgkinson; F D Lublin; D W Paty; S C Reingold; J Simon Journal: Ann Neurol Date: 1996-01 Impact factor: 10.422
Authors: L Truyen; F Barkhof; M Tas; M A Van Walderveen; S T Frequin; O R Hommes; J J Nauta; C H Polman; J Valk Journal: Mult Scler Date: 1997-01 Impact factor: 6.312
Authors: J H Simon; L D Jacobs; M Campion; K Wende; N Simonian; D L Cookfair; R A Rudick; R M Herndon; J R Richert; A M Salazar; J J Alam; J S Fischer; D E Goodkin; C V Granger; M Lajaunie; A L Martens-Davidson; M Meyer; J Sheeder; K Choi; A L Scherzinger; D M Bartoszak; D N Bourdette; J Braiman; C M Brownscheidle; R H Whitham Journal: Ann Neurol Date: 1998-01 Impact factor: 10.422
Authors: H F McFarland; J A Frank; P S Albert; M E Smith; R Martin; J O Harris; N Patronas; H Maloni; D E McFarlin Journal: Ann Neurol Date: 1992-12 Impact factor: 10.422
Authors: J O Fleming; A Isaak; J E Lee; C C Luzzio; M D Carrithers; T D Cook; A S Field; J Boland; Z Fabry Journal: Mult Scler Date: 2011-03-03 Impact factor: 6.312
Authors: John Fleming; Gianna Hernandez; Leslie Hartman; Jane Maksimovic; Sara Nace; Benjamin Lawler; Todd Risa; Thomas Cook; Rashmi Agni; Mark Reichelderfer; Christopher Luzzio; Loren Rolak; Aaron Field; Zsuzsanna Fabry Journal: Mult Scler Date: 2017-10-24 Impact factor: 6.312
Authors: Leonard H Verhey; Alessio Signori; Douglas L Arnold; Amit Bar-Or; A Dessa Sadovnick; Ruth Ann Marrie; Brenda Banwell; Maria Pia Sormani Journal: Neurology Date: 2013-08-21 Impact factor: 9.910