Machine learning-based models for prediction of innovative medicine reimbursement decisions in Scotland.

Objective: This study aimed to investigate the critical factors for reimbursement decisions of innovative medicines in Scotland and to explore the feasibility of machine learning models for predicting decisions.

Method: All appraisals for innovative medicines issued by the Scottish Medicines Consortium (SMC) from 2016 to 2020 were screened to extract decision outcomes and 24 explanatory factors. SelectKBest with chi-square test was used for factor selection.

Exit interviews from two randomised placebo-controlled phase 3 studies with caregivers of young children with autism spectrum disorder.

Introduction: Autism spectrum disorder (ASD) is characterised by difficulty with social communication and restricted, repetitive patterns of behaviour. This study aimed to improve understanding of the ASD patient experience with the treatment (bumetanide) regarding the changes in core symptoms and to assess changes considered as meaningful. To achieve this, qualitative interviews were conducted with caregivers of patients in two phase 3 clinical trials (NCT03715153; NCT03715166) of a novel ASD treatment.

Cost-effectiveness of ten commonly used antipsychotics in first-episode schizophrenia in the UK: economic evaluation based on a discrete event simulation model.

Background: Previous economic evidence about interventions for schizophrenia is outdated, non-transparent and/or limited to a specific clinical context.

Aims: We developed a discrete event simulation (DES) model for estimating the cost-effectiveness of interventions in schizophrenia in the UK.

Method: The DES model was developed based on the structure of previous models, populated with demographic, clinical and cost data from the UK, and antipsychotics' effects from recent network meta-analyses.