The usual meta-analysis of a sequence of randomized clinical trials only considers the difference between two treatments and produces a point estimate and a confidence interval for a parameter that measures this difference. The usual parameter is the log(odds ratio) linked to Mantel-Haenszel methodology. Inference is made either under the assumption of homogeneity or in a random effects model that takes account of heterogeneity between trials. This paper has two goals. The first is to present a likelihood based method for the estimation of the parameters in the random effects model, which avoids the use of approximating Normal distributions. The second goal is to extend this method to a bivariate random effects model, in which the effects in both groups are supposed random. In this way inference can be made about the relationship between improvement and baseline effect. The method is demonstrated by a meta-analysis dataset of Collins and Langman.
Children with a better perception of their own health status have a higher score on the CDDUX questionnaire. The whole group seems to have a lower quality of life than the healthy reference group on all domains of the DUX-25. The new disease-specific questionnaire CDDUX provides information about how children with CD think and feel about their illness. The questionnaire may enable researchers and clinicians to determine the consequences of this illness and the effects of clinical interventions on several aspects of daily living.
Summary: Purpose: To evaluate the evolution of epileptic seizures and EEG features in a large group of patients with Angelman syndrome (AS).Methods: Thirty-six patients with AS with a proven chromosome 15qll-13 deletion were retrospectively analyzed with regard to their epilepsy and EEG findings by examination of patient files and EEGs. All EEGs were reviewed by one of the authors. A logistic regression model, with a follow-up from 1 to 39 years (mean, 15 years), was used for statistical analysis.Results: Epileptic seizures had occurred in 30 (83%) patients. In 43% of them, the initial symptoms of epilepsy were febrile convulsions in infancy. In childhood, epilepsy could start with almost any type of seizure. Atypical absences and myoclonic seizures prevailed in adulthood. Epileptic seizures were present in 92% of the adult patients. The most typical EEG findings were rhythmic triphasic delta waves of high amplitude with a maximum over the frontal regions, identified in 99 (66%) of 150 EEGs, and continuously or intermittently, in 30 (83%) of 36 patients with AS. In 47% it was present even before a clinical diagnosis of AS was considered. Highamplitude rhythmic 4-6/s slow activity, seen in 44 (29%) of 150 EEGs, was not present after the age of 12 years.Conclusions: In contrast to previous reports suggesting a decreasing frequency of epileptic seizures with age, we found that 92% of the adult patients with AS continued to have epileptic seizures. The most typical EEG finding in AS, in both children and adults, was the presence of frontal triphasic delta waves. In mentally retarded patients, this EEG pattern should point the physician in the direction of AS.
Background: Endovascular treatment (EVT) is effective for stroke patients with a large vessel occlusion (LVO) of the anterior circulation. To further improve personalized stroke care, it is essential to accurately predict outcome after EVT. Machine learning might outperform classical prediction methods as it is capable of addressing complex interactions and non-linear relations between variables.Methods: We included patients from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry, an observational cohort of LVO patients treated with EVT. We applied the following machine learning algorithms: Random Forests, Support Vector Machine, Neural Network, and Super Learner and compared their predictive value with classic logistic regression models using various variable selection methodologies. Outcome variables were good reperfusion (post-mTICI ≥ 2b) and functional independence (modified Rankin Scale ≤2) at 3 months using (1) only baseline variables and (2) baseline and treatment variables. Area under the ROC-curves (AUC) and difference of mean AUC between the models were assessed.Results: We included 1,383 EVT patients, with good reperfusion in 531 (38%) and functional independence in 525 (38%) patients. Machine learning and logistic regression models all performed poorly in predicting good reperfusion (range mean AUC: 0.53–0.57), and moderately in predicting 3-months functional independence (range mean AUC: 0.77–0.79) using only baseline variables. All models performed well in predicting 3-months functional independence using both baseline and treatment variables (range mean AUC: 0.88–0.91) with a negligible difference of mean AUC (0.01; 95%CI: 0.00–0.01) between best performing machine learning algorithm (Random Forests) and best performing logistic regression model (based on prior knowledge).Conclusion: In patients with LVO machine learning algorithms did not outperform logistic regression models in predicting reperfusion and 3-months functional independence after endovascular treatment. For all models at time of admission radiological outcome was more difficult to predict than clinical outcome.
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