Modelling MRI enhancing lesion counts in multiple sclerosis using a negative binomial model: implications for clinical trials

Sormani, MP; Bruzzi, Paolo; Miller, D. H.; Gasperini, Claudio; Barkhof, Frederik; Filippi, Massimo

doi:10.1016/s0022-510x(99)00015-5

Cited by 70 publications

(90 citation statements)

References 15 publications

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“…Recently, the mixed Poisson family of distributions was considered to model lesion counts. In Sormani et al (1999aSormani et al ( , 1999b, they showed that negative binomial model gave a relatively good fit to this type of data. With an assumed parametric model, one may be able to develop more powerful tests and construct confidence intervals for treatment effect.…”

Section: Introductionmentioning

confidence: 99%

Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data

Aban

Cutter

Mavinga

2009

Computational Statistics & Data Analysis

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In comparing the mean count of two independent samples, some practitioners would use the t-test or the Wilcoxon rank sum test while others may use methods based on a Poisson model. It is not uncommon to encounter count data that exhibit overdispersion where the Poisson model is no longer appropriate. This paper deals with methods for overdispersed data using the negative binomial distribution resulting from a Poisson-Gamma mixture. We investigate the small sample properties of the likelihood-based tests and compare their performances to those of the t-test and of the Wilcoxon test. We also illustrate how these procedures may be used to compute power and sample sizes to design studies with response variables that are overdispersed count data. Although methods are based on inferences about two independent samples, sample size calculations may also be applied to problems comparing more than two independent samples. It will be shown that there is gain in efficiency when using the likelihood-based methods compared to the t-test and the ilcoxon test. In studies where each observation is very costly, the ability to derive smaller sample size estimates with the appropriate tests is not only statistically, but also financially, appealing.

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Section: Introductionmentioning

confidence: 99%

Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data

Aban

Cutter

Mavinga

2009

Computational Statistics & Data Analysis

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“…10 With the NB-parameter estimates based on dataset A ( ϭ 7.4, dispersion ϭ 0.45), the calculations show approximately 129 patients required for detecting a 50% reduction in the mean number of enhancing lesions, fitting in with previous estimated numbers. 9 …”

Section: Sample Size Calculationsmentioning

confidence: 59%

“…(Significance was determined by a Wilcoxon rank sum test at a 2-sided test level of .05). For comparison, the number of required patients for trials by using the number of enhancing lesions as a primary outcome measure was calculated with the methodology developed by Sormani et al, 10 with the parameters of the required and validated NB distributions derived from dataset A.…”

Section: Methodsmentioning

confidence: 99%

“…Table 4 displays the results of the sample size calculations based on the cumulative enhancing-lesion volume as a primary outcome measure. A treatment effect on the Weibull distribution was defined according to alteration of the scale parameter, and the shape parameter was kept constant, in line with the methodology of Sormani et al, 10 in which the shape parameter of the NB distribution was kept constant. To strengthen this choice, comparison of the estimated shape parameter for the Weibull distribution of the treatment arm of dataset B (shape ϭ 0.76, data not shown) with the shape pa- rameter of datasets A and B, 0.76 and 0.83, respectively, showed minor-to-no difference in the shape of the distribution as a result of treatment.…”

Section: Literature Searchmentioning

confidence: 99%

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Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials

Elskamp¹,

Knol²,

Uitdehaag

et al. 2011

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.

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“…Using the lesion counts from MR data, the analysis thereof has been performed on negative binomial model. 66.6 % of achievement has been gained as a result of the modeling [5].…”

Section: Introductionmentioning

confidence: 99%

The Significance of Artificial Neural Networks Algorithms Classification in the Multiple Sclerosis and Its Subgroups

Karaca¹,

Hayta²

2015

International Advanced Research Journal in Science, Engineering

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Abstract:The study includes three subgroups of Multiple Sclerosis, namely RRMS, SPMS and PPMS as well as healthy individuals. The purpose of this study is to diagnose Multiple Sclerosis subgroups through Magnetic Resonance Imaging and Expanded Status Disability Scale. MRI and EDSS data belong to 139 volunteers, 120 of whom are MS patients (76 RRMS, 38 SPMS, 6 PPMS patients) and the remaining are healthy people. All subjects are between the ages of 20 and 55. Disability levels of MS symptoms are determined using Expanded Disability Status Scale. We have focused on three regions in the brain: brain stem, periventricular corpus callosum, and upper cervical regions. EDSS scores and number of lesions in these three regions are considered as the parameters of the ANN algorithms to determine the subgroups of the disease. The empirical results are examined taking two aspects into consideration. One of them is MRI and the other is EDSS data. These two elements are applied onto the input of Artificial Neural Networks Algorithms that are Feed Forward Back Propagation, Learning Vector Quantization and Radial Basis Function. The significance of these variables for the diagnosis of MS subgroups has been revealed as a result of this study in which algorithm has been utilized.

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Modelling MRI enhancing lesion counts in multiple sclerosis using a negative binomial model: implications for clinical trials

Cited by 70 publications

References 15 publications

Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data

Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data

Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials

The Significance of Artificial Neural Networks Algorithms Classification in the Multiple Sclerosis and Its Subgroups

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