GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world

Abstract

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real‐world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi‐state models, discrete event simulation models, physiology‐based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real‐world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

The use of mathematical modeling studies for evidence synthesis and guideline development: A glossary

“…In a discrete‐time model, cycle length represents the interval from one time point to the next, for example a specific number of days, weeks, months, or years …”

“…This type of dynamic model treats time as a discrete variable (in contrast to a continuous‐time model ) and other variables (or health outcomes) can only change at specific time points …”

“…Mathematical modeling studies are frequently used to synthesize evidence from multiple data sources to address a clinical or public health question not directly addressed by a primary study. For example, a mathematical model was used to synthesize evidence obtained from virological, clinical, epidemiological, and behavioral data to help determine optimal target populations for influenza vaccination programs . Other examples are mathematical modeling studies that aim to predict the real‐world drug effectiveness from randomized controlled trial (RCT) efficacy data (reviewed in Panayidou et al).…”

“…Systematic reviewers and guideline developers struggle with questions about whether and how to include the results of mathematical modeling studies into a body of evidence. The review of mathematical modeling studies predicting drug‐effectiveness from RCT data identified 12 studies using four different modeling approaches . Because of the varying use of key terminology between studies, and because certain terms can have different meanings in the literature, it was necessary to describe in the review each modeling approach in detail to illustrate the differences between them.…”

“…The review of mathematical modeling studies predicting drug-effectiveness from RCT data identified 12 studies using four different modeling approaches. 7 Because of the varying use of key terminology between studies, and because certain terms can have different meanings in the literature, it was necessary to describe in the review each modeling approach in detail to illustrate the differences between them. This effort highlights an important reason for the challenges in summarizing the results of mathematical modeling studies.…”