Fractional Polynomial NMA Models For Survival Analyses: Results From A Simulation Study

Laliman, V.; Pacou, M; Gauthier, Alain P.

doi:10.1016/j.jval.2017.08.2208

Cited by 1 publication

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“…Practical experience shows that, in particular, the fractional polynomial models can be time-consuming to fit in a Bayesian framework. 39 Even for the simple example used here, some models take several minutes to converge on a standard laptop. Although the frequentist result is certainly less informative than a full posterior, fitting a large set of candidate models in less than a second provides a very useful tool to the practitioner.…”

Section: Discussionmentioning

confidence: 99%

Nonproportional Hazards in Network Meta-Analysis: Efficient Strategies for Model Building and Analysis

et al. 2020

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Objectives: To develop efficient approaches for fitting network meta-analysis (NMA) models with time-varying hazard ratios (such as fractional polynomials and piecewise constant models) to allow practitioners to investigate a broad range of models rapidly and to achieve a more robust and comprehensive model selection strategy.Methods: We reformulated the fractional polynomial and piecewise constant NMA models using analysis of variance-like parameterization. With this approach, both models are expressed as generalized linear models (GLMs) with time-varying covariates. Such models can be fitted efficiently with standard frequentist techniques. We applied our approach to the example data from the study by Jansen et al, in which fractional polynomial NMA models were introduced.Results: Fitting frequentist fixed-effect NMAs for a large initial set of candidate models took less than 1 second with standard GLM routines. This allowed for model selection from a large range of hazard ratio structures by comparing a set of criteria including Akaike information criterion/Bayesian information criterion, visual inspection of goodness-of-fit, and long-term extrapolations. The "best" models were then refitted in a Bayesian framework. Estimates agreed very closely.Conclusions: NMA models with time-varying hazard ratios can be explored efficiently with a stepwise approach. A frequentist fixed-effect framework enables rapid exploration of different models. The best model can then be assessed further in a Bayesian framework to capture and propagate uncertainty for decision-making.

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Section: Discussionmentioning

confidence: 99%