Some case studies were more historic in nature – to test analytical methods on medicines that have already passed through regulatory and health technology assessment (HTA) that might have been useful earlier on in the process or stakeholder views of these methods (WP1, WP2, WP4). Other case studies included medicines in development that were thought to have a particularly large efficacy-effectiveness gap (see Clarifying the Issues for a definition) or a difference in expectations between regulators and payers (WP2, WP3).
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- Exploring potential drivers of effectiveness
- Detecting channeling bias
- Alternative study designs
- Evidence synthesis and network meta-analysis
- Predicting relative effectiveness in the real-world
- Simulation of methods to adjust for bias
Drivers of effectiveness in
Hodgkin’s lymphoma
Drivers of effectiveness in diabetes
Patient level drivers of effectiveness in schizophrenia
Healthcare setting-level drivers of effectiveness in schizophrenia
Detecting channeling bias after launch in anticoagulant medicines
Detecting channeling bias after launch in antihypertensive medicines
Detecting channeling bias after launch in diabetes
Early pragmatic trials in chronic obstructive pulmonary disease
Adjusting for drop out from cohort multiple randomised controlled trial in cardiovascular disease
Modelling and simulation of a population enrichment RCT in schizophrenia
Methods for network meta-analysis using individual participant
data in depression
Incorporating non-randomised studies in NMA of RCTs in schizophrenia
Using RWE to connect ‘disconnected’ networks of evidence and inform second-line treatment effects in rheumatoid arthritis
Using RWE to estimate relative effectiveness and inform trial design in multiple sclerosis
Predicting effectiveness based on RCT efficacy data and RWE prior to launch in rheumatoid arthritis
Using RWE to inform relative effectiveness estimates and trial design in metastatic melanoma
Propensity weighting and extrapolation in non-small-cell
lung cancer
Adjusting for confounding bias in a heterogeneous control arm in a pragmatic trial
Comparison of methods for competing risks in pragmatic trials
Controlling for confounding in a pragmatic study with time-to-event outcomes
Adjusting for confounding in early postlaunch settings: going beyond logistic regression models