Long-term survival outcomes of patients with Niemann-Pick disease type C receiving miglustat treatment: A large retrospective observational study.

Miglustat has been indicated for the treatment of Niemann-Pick disease type C (NP-C) since 2009. The aim of this observational study was to assess the effect of miglustat on long-term survival of patients with NP-C. Data for 789 patients from five large national cohorts and from the NPC Registry were collected and combined. Miglustat-treated and untreated patients overall and within sub-groups according to age-at-neurological-onset, that is, early infantile-onset (<2 years), late infantile-onset (2 to <6 years), juvenile-onset (6 to <15 years), and adolescent/adult-onset (≥15 years) were analysed and compared. Survival was analysed from the time of first neurological manifestation (Neurological onset group, comprising 669 patients) and from diagnosis (Diagnosis group, comprising 590 patients) using a Cox proportional hazard model adjusted for various covariates. Overall, 384 (57.4%) patients in the Neurological onset group and 329 (55.8%) in the Diagnosis group were treated with miglustat. Miglustat treatment was associated with a significant reduction in risk of mortality in both groups (entire Neurological onset group, Hazard ratio [HR] = 0.51; entire Diagnosis group, HR = 0.44; both P < .001). The effect was observed consistently in all age-at-neurological-onset sub-groups (HRs = 0.3 to 0.7) and was statistically significant for late infantile-onset patients in both groups (Neurological onset group, HR = 0.36, P < .05; Diagnosis group, HR = 0.32, P < .01), and juvenile-onset patients in the Diagnosis group only (HR = 0.30, P < .05). Despite the limitations of the data that urge cautious interpretation, the findings are consistent with a beneficial effect of miglustat on survival in patients with NP-C.

Statistical approaches for conducting network meta-analysis in drug development.

We introduce health technology assessment and evidence synthesis briefly, and then concentrate on the statistical approaches used for conducting network meta-analysis (NMA) in the development and approval of new health technologies. NMA is an extension of standard meta-analysis where indirect as well as direct information is combined and can be seen as similar to the analysis of incomplete-block designs. We illustrate it with an example involving three treatments, using fixed-effects and random-effects models, and using frequentist and Bayesian approaches. As most statisticians in the pharmaceutical industry are familiar with SAS® software for analyzing clinical trials, we provide example code for each of the methods we illustrate. One issue that has been overlooked in the literature is the choice of constraints applied to random effects, and we show how this affects the estimates and standard errors and propose a symmetric set of constraints that is equivalent to most current practice. Finally, we discuss the role of statisticians in planning and carrying out NMAs and the strategy for dealing with important issues such as heterogeneity.

Treatment outcomes following continuous miglustat therapy in patients with Niemann-Pick disease Type C: a final report of the NPC Registry.

BACKGROUND: Niemann-Pick disease Type C (NP-C) is a rare, progressive neurodegenerative disorder characterized by progressive neurodegeneration and premature death. We report data at closure of the NPC Registry that describes the natural history, disease course and treatment experience of NP-C patients in a real-world setting. METHODS: The NPC Registry was a prospective observational cohort study that ran between September 2009 and October 2017. Patients with a confirmed diagnosis of NP-C were enrolled regardless of treatment status. All patients underwent clinical assessments and medical care as determined by their physicians; data were collected through a secure internet-based portal. RESULTS: At closure on October 19, 2017, 472 patients from 22 countries were enrolled in the NPC Registry. Mean (standard deviation) age at enrollment was 21.2 (15.0) years, and 51.9% of patients were male. First neurological symptom onset occurred during the early-infantile (< 2 years), late-infantile (2 to < 6 years), juvenile (6 to < 15 years), or adolescent/adult (≥ 15 years) periods in 13.5, 25.6, 31.8, and 29.1% of cases, respectively. The most frequent neurological manifestations prior to enrollment included ataxia (67.9%), vertical supranuclear gaze palsy (67.4%), dysarthria (64.7%), cognitive impairment (62.7%), dysphagia (49.1%), and dystonia (40.2%). During infancy, splenomegaly and hepatomegaly were frequent (n = 199/398 [50%] and n = 147/397 [37.0%], respectively) and persisted in most affected patients. Of the 472 enrolled patients, 241 were continuously treated with miglustat during the NPC Registry observation period, of whom 172 of these 241 patients were treated continuously for ≥12 months. A composite disability score that assesses impairment of ambulation, manipulation, language, and swallowing was highest in the early-infantile population and lowest in the adolescent/adult population. Among the continuous miglustat therapy population, 70.5% of patients had improved or had stable disease (at least 3 of the 4 domains having a decreased or unchanged score between enrollment and last follow-up). The NPC Registry did not identify any new safety signals associated with miglustat therapy. CONCLUSIONS: The profiles of clinical manifestations in the final NPC Registry dataset agreed with previous clinical descriptions. Miglustat therapy was associated with a stabilization of neurological manifestations in most patients. The safety and tolerability of miglustat therapy was consistent with previous reports.

Methodological quality of meta-analyses: matched-pairs comparison over time and between industry-sponsored and academic-sponsored reports.

CONTEXT: Meta-analyses are regularly used to inform healthcare decisions. Concerns have been expressed about the quality of meta-analyses and, in particular, about those supported by the pharmaceutical industry. OBJECTIVE: The objective of this study is to compare the quality of pharmaceutical-industry-supported meta-analyses with academic meta-analyses and of meta-analyses published before and after companies started to disclose their data. DATA SOURCES: We identified industry-supported meta-analyses by searching the Scopus bibliographic database, using author affiliations. We matched each industry-supported meta-analysis with an academic meta-analysis using high-level MeSH terms in PubMed. STUDY SELECTION: We included meta-analyses of randomized trials assessing the efficacy or safety of any pharmaceutical intervention in humans, published in 2002-2004 or 2008-2009. Cochrane reviews were excluded. Two individuals independently selected papers, with discrepancies resolved by two further individuals. ASSESSMENT: We developed and piloted a quality-assessment tool, consisting of 43 questions in four domains, with a key summary question covering each domain. Two individuals independently assessed each meta-analysis. RESULTS: We examined 126 meta-analysis publications in 63 matched pairs. The average quality was low, with fewer than 50% adequate in three of the four domains. Industry-supported meta-analyses less often demonstrated adequate methods for locating studies and assessing their quality (odds ratio 0.44, 95% confidence interval 0.21 to 0.92), for analysing the included studies (0.52, 0.25 to 1.06), for undertaking meta-analyses (0.82, 0.40 to 1.68) and in reaching sound conclusions (0.62, 0.30 to 1.28). Quality generally improved over time, particularly for some aspects of industry reports. CONCLUSIONS: Academic meta-analysis papers are generally of higher quality than industry-supported ones. This is largely due to less detailed reporting in industry-supported meta-analyses and a tendency for them to take the included studies at face value, probably arising from the implicit assumption that these studies already have high methodological standards to meet licensing requirements. The improved quality over time does not appear to be due to the use of data disclosed by industry. The main limitations of this study are the small sample of papers and the subjective nature of some of the assessment processes.

A tool to assess the quality of a meta-analysis.

BACKGROUND: Because meta-analyses are increasingly prevalent and cited in the medical literature, it is important that tools are available to assess their methodological quality. When performing an empirical study of the quality of published meta-analyses, we found that existing tools did not place a strong emphasis on statistical and interpretational issues. METHODS: We developed a quality-assessment tool using existing materials and expert judgment as a starting point, followed by multiple iterations of input from our working group, piloting, and discussion. After having used the tool for our empirical study, agreement for four key items in the tool was measured using weighted kappa coefficients. RESULTS: Our tool contained 43 items divided into four key areas (data sources, analysis of individual studies, meta-analysis methods, and interpretation), and each area ended with a summary question. We also produced guidance for completing the tool. Agreement between raters was fair to moderate. CONCLUSIONS: The tool should usefully inform subsequent initiatives to develop quality-assessment tools for meta-analysis. We advocate use of consensus between independent raters when assessing statistical appropriateness and adequacy of interpretation in meta-analyses.