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Michael Munsell,1 Molly Frean,1 Joseph Menzin,1 Amy L Phillips2 1Boston Health Economics, Inc., Waltham, MA, USA; 2Health Economics & Outcomes Research, EMD Serono Inc., Rockland, MA, USA Objective: As the multiple sclerosis (MS) disease-modifying drug (DMD) treatment options have expanded to include oral therapies, it is important to understand whether route of administration is associated with DMD adherence. The objective of this study was to compare adherence to DMDs in patients with MS newly initiating treatment with a self-injectable versus an oral DMD. Methods: This retrospective database study used IMS Health Real World Data Adjudicated Claims – US data between July 1, 2010 and June 30, 2014. Adherence was measured by medication possession ratio (MPR), calculated as the total number of treated days divided by the total number of days from the first treated day until the end of 12-month follow-up. A binary measure representing adherence (MPR ≥0.8) versus nonadherence (MPR

We presents a PHD filtering approach to estimate the state of an unknown number of persons in a video sequence. Persons are represented by moving blobs, which are tracked across different frames using a first-order moment approximation to the posterior density. The PHD filter is a good alternative to standard multi-target tracking algorithms, since overrides making explicit associations between measurements and persons locations. The recursive method has linear complexity in the number of targets, so it also has the potential benefit of scaling well with a large number of persons being tracked. The PHD filter achieves interesting results for the multiple persons tracking problem, albeit discarding useful information from higher order interactions. Nevertheless, a backward state-space representation using PHD smoothing can be used to refine the filtered estimates. In this paper, we present two smoothing strategies for improving PHD filter estimates in multiple persons tracking. Results from using PHD smoothing techniques in a video sequence shows a slight gain in the cardinality estimates (meaning the number of persons in a particular video frame), but good performance in the individual location estimates. [ABSTRACT FROM AUTHOR]

Clinical trials of genotype-targeted therapies in DMD typically compare treated patients to genotype-matched controls. While this avoids confounding by genotype effects, the pool of eligible controls is further limited in an already rare disease. We hypothesized that using genotype-unmatched controls can be feasible when 1) DMD genotype class effects are small and precisely known and 2) stronger baseline prognostic factors are accounted for. We evaluated this prospect in a hypothetical clinical trial for effects on 1-year change in the North Star Ambulatory Assessment total score (ΔNSAA). The hypothetical 'treatment' group (n=58) was exon 51 skip-amenable patients from the DEMAND III trial placebo arm. The genotype-unmatched 'control' group (n=58) included patients amenable to skipping exons other than 44, 45, 51 or 53 drawn from five natural history studies using similar age, function, and steroid inclusion criteria. Mean difference in ΔNSAA for the treatment vs. control group indicated a bias of -1.6 NSAA units. After statistically accounting for genotype differences and baseline prognostic factors (e.g., motor function and steroid type), the bias was reduced to -0.3 units. As a statistical cost of genotype adjustment, the standard error of the treatment effect increased by 35%. Power analysis indicated that detecting a 5 (or 4) unit treatment effect using unmatched vs. matched controls would require 1.2 (or 1.4) times more enrolled patients. When genotype adjustment was integrated into a hypothetical multi-genotype, randomized, double-blind platform trial, overall sample size requirements were reduced vs. conducting separate trials with matched controls for each genotype class. We conclude that comparisons with genotypically-unmatched controls, with careful consideration of baseline prognostic factors, are feasible in DMD drug development and could help limit placebo exposure among patients eligible for genotype-targeted treatments. [ABSTRACT FROM AUTHOR]

Loss of ambulation (LoA) is a critical milestone in the progression of Duchenne muscular dystrophy (DMD) that usually occurs by the early teens, with significant variation across individuals. Accurate individualized predictions of time to LoA are valuable for DMD drug development (e.g., enrichment of trial populations and benchmarking of long-term treatment outcomes), as well as for counseling of patients and caregivers (e.g., advance ordering of a power wheelchair, preparing home modifications for accessibility). A composite prognostic score for LoA was previously developed based data from 608 boys and 1,240 years of follow-up across multiple DMD natural history and placebo-arm data sources. Timed function tests, steroid type, height and weight were identified as the most important predictors of LoA. We independently validated this score using data from 226 patients with 819 years of follow-up in the CINRG Duchenne natural history study, which was not used for score development. The previously-defined prognostic groups showed excellent risk stratification in the validation data, with median times to LoA of 1.8, 3.8, 7.8 and >9.0 years across groups, compared with medians of 1.1, 2.1, 4.1 and >9.0 years in the training data. Risk groups were well-differentiated despite the use of different LoA definitions in the training data (inability to walk 10 meters or complete the 6-minute walk test) and validation data (full-time wheelchair dependence), but shifted to later median ages in the validation data. Simplified prognostic scores were also explored using classification and regression trees to identify functional thresholds that best predicted time to LoA. Individualized prediction of time to LoA is possible in DMD, and is most accurate when based on a composite profile of motor function. Calibration of prognostic scores to specific definitions of LoA is important for quantitative accuracy, and for applications in clinical management and drug development. [ABSTRACT FROM AUTHOR]