Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS.
Jordi W J van UnnikStavros NikolakopoulosMarinus J C EijkemansJésus Gonzalez-BermejoGaelle BruneteauCapucine Morelot-PanziniLeonard H van den BergMerit E CudkowiczChristopher J McDermottThomas SimilowskiRuben P A van EijkPublished in: Neurology (2023)
Our algorithm uses prior knowledge to determine the uncertainty of the expected treatment effect in ALS clinical trials and optimizes the planning of interim analyses. Improving futility monitoring in ALS could minimize the exposure of patients to ineffective or harmful treatments, and result in significant ethical and efficiency gains.
Keyphrases
- clinical trial
- amyotrophic lateral sclerosis
- end stage renal disease
- machine learning
- deep learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- randomized controlled trial
- double blind
- phase ii
- patient reported
- combination therapy
- patient reported outcomes
- neural network
- study protocol