This study provides Class II evidence that, in absence of clinical features that suggest a hereditary demyelinating neuropathy, sonographic enlargement of proximal median nerve segments and brachial plexus accurately identifies patients with chronic inflammatory neuropathies.

ObjectiveTo assess the effect of eligibility criteria on exclusion rates, generalizability, and outcome heterogeneity in amyotrophic lateral sclerosis (ALS) clinical trials and to assess the value of a risk-based inclusion criterion.MethodsA literature search was performed to summarize the eligibility criteria of clinical trials. The extracted criteria were applied to an incidence cohort of 2,904 consecutive patients with ALS to quantify their effects on generalizability and outcome heterogeneity. We evaluated the effect of a risk-based selection approach on trial design using a personalized survival prediction model.ResultsWe identified 38 trials. A large variability exists between trials in all patient characteristics for enrolled patients (p < 0.001), except for the proportion of men (p = 0.21). Exclusion rates varied widely (from 14% to 95%; mean 59.8%; 95% confidence interval 52.6%–66.7%). Stratification of the eligible populations into prognostic subgroups showed that eligibility criteria lead to exclusion of patients in all prognostic groups. Eligibility criteria neither reduce heterogeneity in survival time (from 22.0 to 20.5 months, p = 0.09) nor affect between-patient variability in functional decline (from 0.62 to 0.65, p = 0.25). In none of the 38 trials were the eligibility criteria found to be more efficient than the prediction model in optimizing sample size and eligibility rate.ConclusionsThe majority of patients with ALS are excluded from trial participation, which questions the generalizability of trial results. Eligibility criteria only minimally improve homogeneity in trial endpoints. An individualized risk-based criterion could be used to balance the gains in trial design and loss in generalizability.

Simulation studies to evaluate performance of statistical methods require a well-specified data-generating model. Details of these models are essential to interpret the results and arrive at proper conclusions. A case in point is random-effects meta-analysis of dichotomous outcomes. We reviewed a number of simulation studies that evaluated approximate normal models for meta-analysis of dichotomous outcomes, and we assessed the data-generating models that were used to generate events for a series of (heterogeneous) trials. We demonstrate that the performance of the statistical methods, as assessed by simulation, differs between these 3 alternative data-generating models, with larger differences apparent in the small population setting. Our findings are relevant to multilevel binomial models in general.