Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.

Objectives: Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide.

Methods: Based on a retrospective observational registry covering 3090 patients with glioblastoma treated with temozolomide, we proposed the use of a two-step iterative exploratory learning process consisting of an initialization phase and a machine learning phase.

Nearest Neighbour Propensity Score Matching and Bootstrapping for Estimating Binary Patient Response in Oncology: A Monte Carlo Simulation.

Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in pharmacoepidemiology to estimate treatment response using observational data. Unfortunately, there is limited evidence on the optimal approach for accurately estimating binary treatment response and, more so, to estimate its variance. Bootstrapping, although commonly used to accurately estimate variance, is rarely used together with PS matching.

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network.

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE).

Comparative and combined effectiveness of innovative therapies in cancer: a literature review.

To achieve therapeutic innovation in oncology, already expensive novel medicines are often concomitantly combined to potentially enhance effectiveness. While this aggravates the pricing problem, comparing effectiveness of novel yet expensive (concomitant) treatments is much needed for healthcare decision-making to deliver effective but affordable treatments. This study reviewed published clinical trials and real-world studies of targeted and immune therapies.