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

Population Pharmacokinetic-B Cell Modeling for Ofatumumab in Patients with Relapsing Multiple Sclerosis

BACKGROUND

Ofatumumab, a fully human anti-CD20 monoclonal antibody indicated for the treatment of relapsing forms of multiple sclerosis (RMS), binds to a unique conformational epitope, thereby depleting B cells very efficiently and allowing subcutaneous administration at lower doses.

OBJECTIVES

The aims were to characterize the relationship between ofatumumab concentration and B cell levels, including the effect of covariates such as body weight, age, or baseline B cell count, and use simulations to confirm the chosen therapeutic dose.

METHODS

Graphical and regression analyses previously performed based on data from a dose-range finding study provided the B cell depletion target used in the present work. All available adult phase 2/3 data for ofatumumab in RMS patients were pooled to develop a population pharmacokinetics (PK)-B cell count model, using nonlinear mixed-effects modeling. The population PK-B cell model was used to simulate B cell depletion and repletion times and the effect of covariates on PK and B cell metrics, as well as the dose response across a range of subcutaneous ofatumumab monthly doses.

RESULTS

The final PK-B cell model was developed using data from 1486 patients. The predetermined B cell target was best achieved and sustained with the 20-mg dose regimen, with median B cell count reaching 8 cells/µL in 11 days and negligible repletion between doses. Only weight had a significant effect on PK, which did not translate into any clinically relevant effect on B cell levels.

CONCLUSION

The PK-B cell modeling confirms the dose chosen for the licensed ofatumumab regimen and demonstrates no requirement for dose adjustment based on adult patient characteristics.

Health Economic Impact of Software-Assisted Brain MRI on Therapeutic Decision-Making and Outcomes of Relapsing-Remitting Multiple Sclerosis Patients-A Microsimulation Study

Aim: To develop a microsimulation model to assess the potential health economic impact of software-assisted MRI in detecting disease activity or progression in relapsing-remitting multiple sclerosis (RRMS) patients. Methods: We develop a simulated decision analytical model based on a hypothetical cohort of RRMS patients to compare a baseline decision-making strategy in which only clinical evolution (relapses and disability progression) factors are used for therapy decisions in MS follow-up, with decision-making strategies involving MRI. In this context, we include comparisons with a visual radiologic assessment of lesion evolution, software-assisted lesion detection, and software-assisted brain volume loss estimation. The model simulates clinical (EDSS transitions, number of relapses) and subclinical (new lesions and brain volume loss) disease progression and activity, modulated by the efficacy profiles of different disease-modifying therapies (DMTs). The simulated decision-making process includes the possibility to escalate from a low efficacy DMT to a high efficacy DMT or to switch between high efficacy DMTs when disease activity is detected. We also consider potential error factors that may occur during decision making, such as incomplete detection of new lesions, or inexact computation of brain volume loss. Finally, differences between strategies in terms of the time spent on treatment while having undetected disease progression/activity, the impact on the patient’s quality of life, and costs associated with health status from a US perspective, are reported. Results: The average time with undetected disease progression while on low efficacy treatment is shortened significantly when using MRI, from around 3 years based on clinical criteria alone, to 2 when adding visual examination of MRI, and down to only 1 year with assistive software. Hence, faster escalation to a high efficacy DMT can be performed when MRI software is added to the radiological reading, which has positive effects in terms of health outcomes. The incremental utility shows average gains of 0.23 to 0.37 QALYs over 10 and 15 years, respectively, when using software-assisted MRI compared to clinical parameters only. Due to long-term health benefits, the average annual costs associated with health status are lower by $1500–$2200 per patient when employing MRI and assistive software. Conclusions: The health economic burden of MS is high. Using assistive MRI software to detect and quantify lesions and/or brain atrophy has a significant impact on the detection of disease activity, treatment decisions, health outcomes, utilities, and costs in patients with MS.

Estimation of sample size in randomized controlled trials in multiple sclerosis studying annualized relapse rates: A systematic review

BACKGROUND

In multiple sclerosis (MS) studies, the most appropriate model for the distribution of the number of relapses was shown to be the negative binomial (NB) distribution.

OBJECTIVE

To determine whether the sample-size estimation (SSE) and the analysis of annualized relapse rates (ARRs) in randomized controlled trials (RCTs) were aligned and compare the SSE between normal and NB distributions.

METHODS

Systematic review of phase 3 and 4 RCTs for which the primary endpoint was ARR in relapsing remitting MS published since 2008 in pre-selected major medical journals. A PubMed search was performed on 30 November 2020. We checked whether the SSE and ARR analyses were congruent. We also performed standardized (fixed α/β, number of arms and overdispersion) SSEs using data collected from the studies.

RESULTS

Twenty articles (22 studies) were selected. NB distribution (or quasi-Poisson) was used for SSE in only 7/22 studies, whereas 21/22 used it for ARR analyses. SSE relying on NB regression necessitated a smaller sample size in 21/22 of our calculations.

CONCLUSION

SSE was rarely performed using the most appropriate model. However, the use of an NB model is recommended to optimize the number of included patients and to be congruent with the final analysis.

An Exposure-Response Analysis of the Clinical Efficacy of Ponesimod in a Randomized Phase II Study in Patients with Multiple Sclerosis

BACKGROUND AND OBJECTIVE

Ponesimod is a sphingosphine-1-phosphate receptor modulator being developed for the treatment of multiple sclerosis. The effects of disease-modifying treatments on magnetic resonance imaging (MRI) lesions in relapsing multiple sclerosis accurately predict effects on clinical relapses, therefore MRI lesion counts are generally accepted efficacy endpoints in phase II clinical studies of multiple sclerosis disease-modifying treatments. Here, we characterize the effect of ponesimod systemic exposure on the cumulative number of T1 gadolinium-enhancing (Gd+) lesions and the annualized relapse rate in a phase IIb study.

METHODS

This study assessed the cumulative number of new Gd+ lesions on T1-weighted MRI scans (primary endpoint) at weeks 12, 16, 20, and 24 and the annualized relapse rate (secondary endpoint). The effect of the demographic and prognostic covariates of sex, age, weight, T1 Gd+ lesions at baseline, and Expanded Disability Status Scale score at baseline were explored. Analyses were performed using NONMEM, Version 7.3.0 (ICON plc).

RESULTS

An increase in ponesimod exposure led to a statistically significant decrease in the cumulative T1 Gd+ lesions on MRI from week 12 to 24 of treatment. Increasing the ponesimod daily dose beyond 20 mg did not provide significant additional  benefits. Sex, age, T1 Gd+ lesions at baseline, and Expanded Disability Status Scale score at baseline were associated with a higher number of new cumulative T1 Gd+ from week 12 to 24 of treatment.

CONCLUSIONS

This analysis shows a relationship between ponesimod exposure and the cumulative number of new T1 Gd+ lesions. Sex, age, T1 Gd+ lesions at baseline, and Expanded Disability Status Score at baseline were not found to be importantly associated with the magnitude of ponesimod effect, and consequently, there is no indication from these analyses that dosage adjustments based on the explored covariates are warranted.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01006265, registration date 1 November, 2009.

Semi-parametric analysis of overdispersed count and metric data with varying follow-up times: Asymptotic theory and small sample approximations

Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum-likelihood estimators. Although this approach can account for heterogeneity it postulates a common overdispersion parameter across groups. Such parametric assumptions are usually difficult to verify, especially in small trials. Therefore, novel procedures that are based on asymptotic results for newly developed rate and variance estimators are proposed in a general framework. Moreover, in case of small samples the procedures are carried out using permutation techniques. Here, the usual assumption of exchangeability under the null hypothesis is not met due to varying follow-up times and unequal overdispersion parameters. This problem is solved by the use of studentized permutations leading to valid inference methods for situations with (i) varying follow-up times, (ii) different overdispersion parameters, and (iii) small sample sizes.

Group sequential designs for negative binomial outcomes

Count data and recurrent events in clinical trials, such as the number of lesions in magnetic resonance imaging in multiple sclerosis, the number of relapses in multiple sclerosis, the number of hospitalizations in heart failure, and the number of exacerbations in asthma or in chronic obstructive pulmonary disease (COPD) are often modeled by negative binomial distributions. In this manuscript, we study planning and analyzing clinical trials with group sequential designs for negative binomial outcomes. We propose a group sequential testing procedure for negative binomial outcomes based on Wald statistics using maximum likelihood estimators. The asymptotic distribution of the proposed group sequential test statistics is derived. The finite sample size properties of the proposed group sequential test for negative binomial outcomes and the methods for planning the respective clinical trials are assessed in a simulation study. The simulation scenarios are motivated by clinical trials in chronic heart failure and relapsing multiple sclerosis, which cover a wide range of practically relevant settings. Our research assures that the asymptotic normal theory of group sequential designs can be applied to negative binomial outcomes when the hypotheses are tested using Wald statistics and maximum likelihood estimators. We also propose two methods, one based on Student's t-distribution and one based on resampling, to improve type I error rate control in small samples. The statistical methods studied in this manuscript are implemented in the R package gscounts, which is available for download on the Comprehensive R Archive Network (CRAN).

Imaging outcome measures for progressive multiple sclerosis trials

Imaging markers that are reliable, reproducible and sensitive to neurodegenerative changes in progressive multiple sclerosis (MS) can enhance the development of new medications with a neuroprotective mode-of-action. Accordingly, in recent years, a considerable number of imaging biomarkers have been included in phase 2 and 3 clinical trials in primary and secondary progressive MS. Brain lesion count and volume are markers of inflammation and demyelination and are important outcomes even in progressive MS trials. Brain and, more recently, spinal cord atrophy are gaining relevance, considering their strong association with disability accrual; ongoing improvements in analysis methods will enhance their applicability in clinical trials, especially for cord atrophy. Advanced magnetic resonance imaging (MRI) techniques (e.g. magnetization transfer ratio (MTR), diffusion tensor imaging (DTI), spectroscopy) have been included in few trials so far and hold promise for the future, as they can reflect specific pathological changes targeted by neuroprotective treatments. Positron emission tomography (PET) and optical coherence tomography have yet to be included. Applications, limitations and future perspectives of these techniques in clinical trials in progressive MS are discussed, with emphasis on measurement sensitivity, reliability and sample size calculation.

Modelling and sample size reestimation for longitudinal count data with incomplete follow up

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.

Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients

Identification of treatment responders is a challenge in comparative studies where treatment efficacy is measured by multiple longitudinally collected continuous and count outcomes. Existing procedures often identify responders on the basis of only a single outcome. We propose a novel multiple longitudinal outcome mixture model that assumes that, conditionally on a cluster label, each longitudinal outcome is from a generalized linear mixed effect model. We utilize a Monte Carlo expectation-maximization algorithm to obtain the maximum likelihood estimates of our high-dimensional model and classify patients according to their estimated posterior probability of being a responder. We demonstrate the flexibility of our novel procedure on two multiple sclerosis clinical trial datasets with distinct data structures. Our simulation study shows that incorporating multiple outcomes improves the responder identification performance; this can occur even if some of the outcomes are ineffective. Our general procedure facilitates the identification of responders who are comprehensively defined by multiple outcomes from various distributions. Copyright © 2017 John Wiley & Sons, Ltd.

Generalized optimal design for two-arm, randomized phase II clinical trials with endpoints from the exponential dispersion family

For two-arm randomized phase II clinical trials, previous literature proposed an optimal design that minimizes the total sample sizes subject to multiple constraints on the standard errors of the estimated event rates and their difference. The original design is limited to trials with dichotomous endpoints. This paper extends the original approach to be applicable to phase II clinical trials with endpoints from the exponential dispersion family distributions. The proposed optimal design minimizes the total sample sizes needed to provide estimates of population means of both arms and their difference with pre-specified precision. Its applications on data from specific distribution families are discussed under multiple design considerations. Copyright © 2016 John Wiley & Sons, Ltd.

Perception of risk and shared decision making process in multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) treatment has changed considerably in recent years thanks to the introduction of ever-more-powerful drugs. Unfortunately, the higher efficacies of these therapies are associated with increased risks of severe adverse events. In this scenario, neurologists and persons with MS (pwMSs) must now balance benefits and risks when making decisions regarding MS management. Areas covered: This review highlights the importance of the risk perception of pwMSs and their neurologists in the shared decision-making process in MS management, taking into account different ways improve the empowerment and engagement of pwMSs. Expert commentary: The shared decision-making process in MS is strongly influenced by an individual's risk perception, which is dynamic and influenced by the personal, emotional, social, and experiential factors of both the pwMSs and neurologists.

The distribution of the magnetic resonance imaging response to glatiramer acetate in multiple sclerosis

We investigated the distribution of the magnetic resonance imaging (MRI)-measured response to glatiramer acetate (GA) treatment in multiple sclerosis (MS) using data from a clinical trial of relapsing=remitting (RR) MS. A fixed and a random effects model were used to quantify the between-patient heterogeneity in treatment response, expressed as new enhancing lesion percentage reduction. In 95% of the patients, lesion reduction due to treatment was estimated to range between -20% and -54%, indicating a rather homogeneous effect of GA on MRI-measured disease activity in RRMS.

A Population Approach to Characterize Interferon Beta-1b Effect on Contrast Enhancing Lesions in Patients With Relapsing Remitting Multiple Sclerosis

In patients with relapsing-remitting multiple sclerosis (RRMS), interferon beta-1b (IFNβ-1b) reduces the occurrence of contrast enhancing lesions (CELs) on magnetic resonance imaging (MRI). Questions remain on the stability of IFNβ-1b effect over time and its action beyond the reduction of CELs. In this study, we described the IFNβ-1b effect by a mixed effects model, quantifying the interpatient variability associated with its parameters. Using a negative binomial distribution model as a natural history model, the effect of IFNβ-1b was evaluated using different mathematical functions of time. IFNβ-1b produced a decrease in the expected CEL numbers, inhibiting the formation of new CELs but did not promote the resolution of the already-formed ones. Based on the final selected model, simulations were carried out to optimize the combined IFNβ-1b-corticosteroid therapy as a proof-of-concept. In summary, we provide evidence on the dynamics of CELs under IFNβ-1b treatment that can be used to monitor the effects of therapies in MS.

Fingolimod effect on brain volume loss independently contributes to its effect on disability

Background:

Brain volume loss occurs in patients with relapsing–remitting MS. Fingolimod reduced brain volume loss in three phase 3 studies.

Objective:

To evaluate whether the effect of fingolimod on disability progression was mediated by its effects on MRI lesions, relapses or brain volume loss, and the extent of this effect.

Methods:

Patients (992/1272; 78%) from the FTY720 Research Evaluating Effects of Daily Oral Therapy in Multiple Sclerosis (FREEDOMS) study were analyzed. Month-24 percentage brain volume change, month-12 MRI-active lesions and relapse were assessed. The Prentice criteria were used to test surrogate marker validity. The proportion of treatment effect on disability progression explained by each marker was calculated.

Results:

Two-year disability progression was associated with active T2 lesions (OR = 1.24; p = 0.001) and more relapses during year 1 (OR = 2.90; p < 0.001) and lower percentage brain volume change over two years (OR = 0.78; p < 0.001). Treatment effect on active T2 lesions, relapses and percentage brain volume change explained 46%, 60% and 23% of the fingolimod effect on disability. Multivariate analysis showed the number of relapses during year 1 (OR = 2.62; p < 0.001) and yearly percentage brain volume change over two years (OR = 0.85; p = 0.009) were independent predictors of disability progression, together explaining 73% of fingolimod effect on disability.

Conclusions:

The treatment effect on relapses and, to a lesser extent, brain volume loss were both predictors of treatment effect on disability; combining these predictors better explained the effect on disability than either factor alone.

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

BACKGROUND

Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.

METHODS AND FINDINGS

We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.

CONCLUSIONS

The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

Clinical and MRI activity as determinants of sample size for pediatric multiple sclerosis trials

OBJECTIVE

To estimate sample sizes for pediatric multiple sclerosis (MS) trials using new T2 lesion count, annualized relapse rate (ARR), and time to first relapse (TTFR) endpoints.

METHODS

Poisson and negative binomial models were fit to new T2 lesion and relapse count data, and negative binomial time-to-event and exponential models were fit to TTFR data of 42 children with MS enrolled in a national prospective cohort study. Simulations were performed by resampling from the best-fitting model of new T2 lesion count, number of relapses, or TTFR, under various assumptions of the effect size, trial duration, and model parameters.

RESULTS

Assuming a 50% reduction in new T2 lesions over 6 months, 90 patients/arm are required, whereas 165 patients/arm are required for a 40% treatment effect. Sample sizes for 2-year trials using relapse-related endpoints are lower than that for 1-year trials. For 2-year trials and a conservative assumption of overdispersion (ϑ), sample sizes range from 70 patients/arm (using ARR) to 105 patients/arm (TTFR) for a 50% reduction in relapses, and 230 patients/arm (ARR) to 365 patients/arm (TTFR) for a 30% relapse reduction. Assuming a less conservative ϑ, 2-year trials using ARR require 45 patients/arm (60 patients/arm for TTFR) for a 50% reduction in relapses and 145 patients/arm (200 patients/arm for TTFR) for a 30% reduction.

CONCLUSION

Six-month phase II trials using new T2 lesion count as an endpoint are feasible in the pediatric MS population; however, trials powered on ARR or TTFR will need to be 2 years in duration and will require multicentered collaboration.

Dealing with excess of zeros in the statistical analysis of magnetic resonance imaging lesion count in multiple sclerosis

Lesion count observed on brain magnetic resonance imaging scan is a common end point in phase 2 clinical trials evaluating therapeutic treatment in relapsing remitting multiple sclerosis (MS). This paper compares the performances of Poisson, zero-inflated poisson (ZIP), negative binomial (NB), and zero-inflated NB (ZINB) mixed-effects regression models in fitting lesion count data in a clinical trial evaluating the efficacy and safety of fingolimod in comparison with placebo, in MS. The NB and ZINB models prove to be superior to the Poisson and ZIP models. We discuss the advantages and limitations of zero-inflated models in the context of MS treatment.

MRI-based clinical trials in relapsing-remitting MS: new sample size calculations based on a longitudinal model

BACKGROUND

Sample sizes for magnetic resonance imaging (MRI)-based clinical trials in multiple sclerosis (MS) generally assume that lesion counts are reasonably described by the negative binomial (NB) model.

OBJECTIVE

This study aimed to assess the appropriateness of the NB model for lesion count data and to provide sample sizes for placebo-controlled, MRI-based clinical trials in relapsing-remitting MS using a more realistic model.

METHODS

The fit of the NB model in each arm of five MS clinical trials was assessed using Pearson's chi-squared statistic. Required sample sizes associated with various tests of treatment effect were estimated by simulating data from a new, longitudinal model for repeated lesion count data on individual patients.

RESULTS

Evidence (p < 0.05) against the NB model was found in at least one arm of four of the five trials. If a trial is designed using this model but the resulting clinical data do not follow its assumptions then this trial can be seriously under-powered for assessing differences in mean lesion counts.

CONCLUSION

Sample sizes based on the longitudinal model are more realistic and often smaller than those previously reported using the NB model.

A parametric model fitting time to first event for overdispersed data: application to time to relapse in multiple sclerosis

In this article, we propose a parametric model for the distribution of time to first event when events are overdispersed and can be properly fitted by a Negative Binomial distribution. This is a very common situation in medical statistics, when the occurrence of events is summarized as a count for each patient and the simple Poisson model is not adequate to account for overdispersion of data. In this situation, studying the time of occurrence of the first event can be of interest. From the Negative Binomial distribution of counts, we derive a new parametric model for time to first event and apply it to fit the distribution of time to first relapse in multiple sclerosis (MS). We develop the regression model with methods for covariate estimation. We show that, as the Negative Binomial model properly fits relapse counts data, this new model matches quite perfectly the distribution of time to first relapse, as tested in two large datasets of MS patients. Finally we compare its performance, when fitting time to first relapse in MS, with other models widely used in survival analysis (the semiparametric Cox model and the parametric exponential, Weibull, log-logistic and log-normal models).

Modeling MR imaging enhancing-lesion volumes in multiple sclerosis: application in clinical trials

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.

MATERIAL AND METHODS

First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.

RESULTS

A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.

CONCLUSIONS

The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.

Modeling the distribution of new MRI cortical lesions in multiple sclerosis longitudinal studies

OBJECTIVE

Recent studies have shown the relevance of the cerebral grey matter involvement in multiple sclerosis (MS). The number of new cortical lesions (CLs), detected by specific MRI sequences, has the potential to become a new research outcome in longitudinal MS studies. Aim of this study is to define the statistical model better describing the distribution of new CLs developed over 12 and 24 months in patients with relapsing-remitting (RR) MS.

METHODS

Four different models were tested (the Poisson, the Negative Binomial, the zero-inflated Poisson and the zero-inflated Negative Binomial) on a group of 191 RRMS patients untreated or treated with 3 different disease modifying therapies. Sample size for clinical trials based on this new outcome measure were estimated by a bootstrap resampling technique.

RESULTS

The zero-inflated Poisson model gave the best fit, according to the Akaike criterion to the observed distribution of new CLs developed over 12 and 24 months both in each treatment group and in the whole RRMS patients group adjusting for treatment effect.

CONCLUSIONS

The sample size calculations based on the zero-inflated Poisson model indicate that randomized clinical trials using this new MRI marker as an outcome are feasible.

Modelling the distribution of cortical lesions in multiple sclerosis

Recent studies have shown the relevance of the cerebral grey matter involvement in multiple sclerosis (MS). Cortical lesions (CLs), detected by specific MRI sequences, are likely to become a new research outcome in MS studies. The aim of this study was to infer the optimal statistical model describing the distribution of CLs in patients with relapsing-remitting (RR)MS. The negative binomial model gave the best fit to the observed distribution of CLs in a group of 44 RRMS patients with one MRI scan of the brain. This observation has important implications for the statistical analysis of CLs in MS studies.

Clinical relevance of improved microbleed detection by susceptibility-weighted magnetic resonance imaging

BACKGROUND AND PURPOSE

Susceptibility-weighted imaging (SWI) has been shown to be more sensitive in detecting cerebral microbleeds (MBs) than is conventional T2*-weighted gradient-recalled echo imaging (GRE). However, the clinical relevance of this improved detection in terms of associations with clinical measures and risk factors is unclear. We sought to determine whether associations of MBs with clinical characteristics, risk factors, white-matter hyperintensities, and lacunes were different on GRE versus SWI in memory clinic patients.

METHODS

One hundred forty-one patients presenting at our memory clinic were included and underwent clinical evaluation and a magnetic resonance imaging protocol including both GRE and SWI. Images were analyzed for numbers and locations of MBs and white-matter hyperintensities. In a subset of patients, apolipoprotein E status was determined. Negative binomial regression was used to assess clinical and radiologic associations with MB number.

RESULTS

MB prevalence was 23% on GRE and 40% on SWI. A total of 219 and 284 MBs were detected on GRE and SWI, respectively. Within groups with MBs, the median MB count was 1 (range, 1 to 144) on GRE and 2 (range, 1 to 129) on SWI (P<0.001). The increase in the number of MBs on SWI was equally distributed among brain regions. Strengths of the associations with age, sex, white-matter hyperintensities, and presence of lacunes with higher MB numbers were comparable for GRE and SWI (all P<0.05); no differential independent associations were detected.

CONCLUSIONS

SWI detected more MBs in more patients, irrespective of MB location. However, this enhanced detection had no improved clinical relevance in terms of associations with vascular risk factors or radiologic markers of small-vessel disease.

Magnetic resonance active lesions as individual-level surrogate for relapses in multiple sclerosis

Background: Use of quantitative magnetic resonance imaging (MRI) metrics as surrogates for clinical outcomes in multiple sclerosis (MS) trials is controversial.

Objectives: We sought to validate, at the individual-patient level, the number of MRI active lesions, as a surrogate marker for relapses in MS.

Methods: Individual-patient data from two large, placebo-controlled clinical trials of subcutaneous interferon β-1a in patients with relapsing–remitting or secondary progressive (SP) MS were analysed separately and as pooled data. The four Prentice criteria were applied to assess surrogacy for the number of new T2 MRI lesions. The predictive value of short-term treatment effects on this MRI marker for longer-term clinical relapses was also assessed.

Results: All Prentice criteria were satisfied. The number of new T2 MRI lesions correlated with the number of relapses over the follow-up period. The proportion of treatment effect on relapses accounted for by the effect of treatment on new T2 MRI lesions over 2 years was 53% in patients with relapsing–remitting MS, 67% in patients with secondary progressive MS, and 62% in pooled data. In the pooled data, treatment effects on new lesions over 1 year mediated a good proportion (70%) of effects on relapses over the subsequent year.

Conclusions: This study provides evidence that new T2 MRI lesion count is a surrogate for relapses in patients with MS treated with interferon or drugs with a similar mechanism of action. Short-term treatment effects on this MRI measure can predict longer-term effects on relapses.

Seasonal prevalence of MS disease activity

OBJECTIVE

This observational cohort study investigated the seasonal prevalence of multiple sclerosis (MS) disease activity (likelihood and intensity), as reflected by new lesions from serial T2-weighted MRI, a sensitive marker of subclinical disease activity.

METHODS

Disease activity was assessed from the appearance of new T2 lesions on 939 separate brain MRI examinations in 44 untreated patients with MS. Likelihood functions for MS disease activity were derived, accounting for the temporal uncertainty of new lesion occurrence, individual levels of disease activity, and uneven examination intervals. Both likelihood and intensity of disease activity were compared with the time of year (season) and regional climate data (temperature, solar radiation, precipitation) and among relapsing and progressive disease phenotypes. Contrast-enhancing lesions and attack counts were also compared for seasonal effects.

RESULTS

Unlike contrast enhancement or attacks, new T2 activity revealed a likelihood 2-3 times higher in March-August than during the rest of the year, and correlated strongly with regional climate data, in particular solar radiation. In addition to the likelihood or prevalence, disease intensity was also elevated during the summer season. The elevated risk season appears to lessen for progressive MS and occur about 2 months earlier.

CONCLUSION

This study documents evidence of a strong seasonal pattern in subclinical MS activity based on noncontrast brain MRI. The observed seasonality in MS disease activity has implications for trial design and therapy assessment. The observed activity pattern is suggestive of a modulating role of seasonally changing environmental factors or season-dependent metabolic activity.

Modeling lesion counts in multiple sclerosis when patients have been selected for baseline activity

The number of new gadolinium-enhancing lesions discovered via magnetic resonance imaging is a well-established outcome for multiple sclerosis studies, especially Phase II Studies. Due to the high cost of magnetic resonance imaging scans, many investigators select participants for the presence of lesions. While this selection procedure is thought to improve the power of inferences, the effect of screening for baseline activity on parameter estimation and interval coverage has not yet been examined. The objective of this study was to investigate the performance of the negative binomial distribution for modeling lesion count data in multiple sclerosis when patients have been selected for activity on a baseline scan. We performed computer simulations to investigate the influence of the screening process on inferences made using a negative binomial model about treatment effects in two independent samples. We also demonstrate how the statistical properties of screening can be incorporated into trial design. We demonstrate that when the negative binomial distribution is used to model lesion counts, while screening for baseline activity improves point estimation, this practice also has the potential to decrease interval coverage and inflate the Type I error rate. For data that is to be modeled using a negative binomial distribution, screening for baseline activity can create a trade-off between cost effectiveness and a higher than desired false positive rate that must be carefully considered in planning Phase II trials.

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

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.

Improved detection of active multiple sclerosis lesions: 3D subtraction imaging

PURPOSE

To examine the benefits of using near-isotropic single-slab three-dimensional (3D) magnetic resonance (MR) imaging for the creation of subtraction images and to evaluate their performance in the detection of active multiple sclerosis (MS) brain lesions in comparison with two-dimensional (2D) subtraction images.

MATERIALS AND METHODS

The study protocol was approved by the local ethics review board and all subjects gave written informed consent before investigation. Three-dimensional MR sequences, including double inversion-recovery, fluid-attenuated inversion recovery, T2-weighted, and T1-weighted magnetization-prepared rapid acquisition gradient-echo (MP-RAGE), and corresponding 2D sequences were performed twice in 14 patients (eight women, six men; mean age, 37.6 years) with MS and nine age-matched healthy control subjects (three women, six men; mean age, 31.7 years). Active lesions were scored by two independent raters, followed by a consensus reading. Lesion counts were evaluated by using negative binomial regression; interrater agreement was evaluated by using intraclass correlation coefficient.

RESULTS

Three-dimensional subtraction images had less residual misregistration and flow artifacts and depicted higher numbers of active lesions with greater interobserver agreement compared with 2D subtraction images. Among the 3D sequences, MP-RAGE subtraction imaging enabled detection of a significantly higher mean number of positive active MS lesions compared with 2D subtraction imaging (2.8 versus 1.7, P = .012), particularly infratentorial lesions (0.6 vs 0.1, P < .05), and a substantially higher (nonsignificant) mean number of small (<3 mm) lesions (0.6 vs 0.1, P > .05).

CONCLUSION

Three-dimensional subtraction imaging, after image registration, produced better image quality, leading to increased detection of active MS lesions with greater interobserver agreement in comparison with 2D subtraction imaging; 3D MP-RAGE subtraction imaging represents a promising technique to increase sensitivity in ascertaining lesion dissemination in time and increase the power of MR imaging metrics for the evaluation of treatment effects in clinical trials.

Rapid benefits of a new formulation of subcutaneous interferon beta-1a in relapsing-remitting multiple sclerosis

This study evaluated the efficacy of a new formulation of subcutaneous (sc) interferon (IFN)-beta1a in relapsing-remitting multiple sclerosis (RRMS). Patients (n = 180) were randomized (2 : 1) to IFN-beta1a or placebo for 16 weeks; all patients then received IFN-beta1a for 24 weeks. Monthly brain MRI was performed. At week 16, the mean number of combined unique active (CUA) lesions was lower with IFN-beta1a than with placebo (p < 0.001; 69% fewer lesions). The mean cumulative number of CUA lesions was already lower with IFN-beta1a by week 4 (post hoc analysis; p = 0.015). The new formulation of sc IFN-beta1a has rapid beneficial effects on MRI outcomes in RRMS.

Gadolinium-enhancing or active T2 magnetic resonance imaging lesions in multiple sclerosis clinical trials?

BACKGROUND

The treatment effects in multiple sclerosis (MS) clinical trials are often estimated by monitoring disease activity by the count of "active" plaques on T2-weighted or gadolinium (Gd)-enhanced T1-weighted magnetic resonance imaging (MRI).

OBJECTIVE

To evaluate the relationship between the treatment effects estimated on T2-weighted or Gd-enhanced T1-weighted MRI.

METHODS

Data were extracted from published randomized clinical trials in relapsing-remitting MS with frequent MRI, reporting both active T2 and Gd-enhancing lesions. A regression analysis was performed between the treatment effects estimated on the two different MRI endpoints.

RESULTS

A strong association was found between the treatment effect on Gd-enhancing lesions and on active T2 lesions (R(2) = 0.93), and the treatment effect estimates were almost the same (slope = 0.96).

CONCLUSION

Defining either active T2 or Gd-enhancing lesions as MRI endpoint seems to be not crucial for monitoring MRI activity in MS clinical trials. The choice of the best MRI endpoint should be based on different considerations (e.g., sensitivity, reproducibility, time for assessment, safety, and patients' comfort). Further monitoring active T2 lesions could allow less expensive trials, without requiring injection of Gd-based contrast agents.

Inferences and Power Analysis Concerning Two Negative Binomial Distributions with An Application to MRI Lesion Counts Data

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.

The distribution of new enhancing lesion counts in multiple sclerosis: further explorations

Background

A statistical distribution describing the number of new enhancing lesions seen on MRI in patients with MS is of great importance for improving the statistical methodology of clinical trials using new enhancing lesions as outcome measure. We examined whether there are superior alternatives for the currently proposed negative binomial (NB) distribution.

Objective

To determine the optimal statistical distribution describing new enhancing lesion counts from a selection of six conceivable models, and to assess the effect on the distribution of a treatment effect, varying follow-up duration and selection for activity at baseline.

Methods

The statistical NB, Poisson-Inverse Gaussian (P-IG), Poisson- Lognormal (P-LN), Neyman type A (NtA), Pólya-Aeppli (PA) and Zero Inflated Poisson (ZIP) distribution were fitted on new enhancing lesion data derived from one treated and two untreated cohorts of RRMS and relapsing SPMS patients and on subgroups of varying follow-up duration and selection for baseline activity. Measure of comparison was Akaike's information criterion (AIC).

Results

Both the subgroup analyses as well as a treatment had a noticeable effect on the distributional characteristics of new enhancing lesion counts. The NB distribution generally provided the most optimal fit, closely followed by the P-IG distribution and the P-LN distribution. Fits of the PA and NtA distribution were suboptimal, while the ZIP distribution was the least adequate for modelling new enhancing lesion counts.

Conclusion

The NB distribution is the optimal distribution for modelling new enhancing lesion counts, irrespective of the effect of treatment, follow-up duration or a baseline activity selection criterion.

Effect of laquinimod on MRI-monitored disease activity in patients with relapsing-remitting multiple sclerosis: a multicentre, randomised, double-blind, placebo-controlled phase IIb study

BACKGROUND

A 24-week phase II trial has shown that 0.3 mg of laquinimod given daily to patients with relapsing-remitting multiple sclerosis was well tolerated and reduced the formation of active lesions. We assessed the effect of oral daily 0.3 and 0.6 mg laquinimod on MRI-monitored disease activity in a 36-week double-blind, placebo-controlled phase IIb study.

METHODS

The study was done in 51 centres in nine countries. Inclusion criteria were one or more relapses in the year before entry and at least one gadolinium enhancing (GdE) lesion on screening MRI. Of 720 patients screened, 306 eligible patients were enrolled. Patients, aged 18-50 years, were randomly assigned to placebo (n=102), laquinimod 0.3 mg a day (n=98), or 0.6 mg a day (n=106). Brain MRI scans and clinical assessments were done at week -4, baseline, and monthly from week 12 to week 36. The primary outcome was the cumulative number of GdE lesions at weeks 24, 28, 32, and 36. The principal analysis of the primary endpoint was done on the intention-to-treat cohort. This study is registered with ClinicalTrials.gov, number NCT00349193.

FINDINGS

Compared with placebo, treatment with laquinimod 0.6 mg per day showed a 40.4% reduction of the baseline adjusted mean cumulative number of GdE lesions per scan on the last four scans (simple means 4.2 [SD 9.2] vs 2.6 [5.3], p=0.0048); treatment with 0.3 mg per day showed no significant effects (3.9 [5.5] vs placebo, p=0.6740). Both doses of laquinimod were well tolerated, with some transient and dose-dependent increases in liver enzymes. A case of Budd-Chiari syndrome-ie, a thrombotic venous outflow obstruction of the liver-occurred after 1 month of exposure in a patient with underlying hypercoagulability who received 0.6 mg laquinimod. Anticoagulant treatment resulted in a decline of liver enzymes to normal without any clinical signs of hepatic decompensation.

INTERPRETATION

In patients with relapsing-remitting multiple sclerosis, 0.6 mg per day laquinimod significantly reduced MRI-measured disease activity and was well tolerated.