Activities per year
OBJECTIVE(S): Motivated by two recent reviews of applications, we aimed to assess the performance of these methods in a range of realistic scenarios under various failures of assumptions.
METHOD(S): We undertook an extensive simulation study, investigating the impact of varying sample size, missing EMs, strength of effect modification and validity of the shared EM assumption, validity of extrapolation and varying between-study overlap, and different covariate distributions and correlations. We assessed bias, standard error, and coverage for MAIC, STC, and ML-NMR, alongside standard indirect comparisons.
RESULTS: ML-NMR and STC performed similarly throughout, eliminating bias and estimating standard errors well when the requisite assumptions were met. MAIC performed poorly in almost all simulation scenarios, in some cases increasing bias compared with a standard indirect comparison. MAIC required full overlap between populations otherwise estimates were biased and unstable, especially when sample size was small. All methods incurred bias when EMs were missing from the model.
CONCLUSIONS: Serious questions are raised about the suitability of MAIC, currently the most popular approach, which is only valid in scenarios where there may be little benefit over a standard indirect comparison. ML-NMR and STC are robust methods for population adjustment, but careful selection of potential EMs prior to analysis is essential to avoid bias. ML-NMR offers additional advantages over MAIC and STC, including synthesising larger treatment networks and producing estimates in any target population, making this an attractive choice in many scenarios.
|Number of pages||9|
|Publication status||Unpublished - 26 Aug 2020|
|Event||41st Annual Conference of the International Society for Clinical Biostatistics - Online, Krakow, Poland|
Duration: 23 Aug 2020 → 27 Aug 2020
|Conference||41st Annual Conference of the International Society for Clinical Biostatistics|
|Period||23/08/20 → 27/08/20|
Phillippo, D. M., 28 Nov 2019
Supervisor: Welton, N. (Supervisor) & Dias, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)File
David M Phillippo (Participant)24 Aug 2020 → 26 Aug 2020
Activity: Participating in or organising an event types › Participation in conference