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Supplemental material, sj-docx-1-mdm-10.1177_0272989X221107649 for Dynamic and Flexible Survival Models for Extrapolation of Relative Survival: A Case Study and Simulation Study by Benjamin Kearns, Matt D. Stevenson, Kostas Triantafyllopoulos and Andrea Manca in Medical Decision Making

Additional file 4:Table S4-1. Summary of clinical outcomes used in Scenario 1 (based on PARTNER S3i PSM). Table S4-2. Utility values and decrements applied in scenario analyses. Table S4-3. Summary of clinical outcomes used in Scenario 2 (based on SURTAVI trial). Table S4-4. Results of scenario analyses.

Additional file 2: Table S2-1. Cost parameters and corresponding codes

Additional file 5. Comparison of current model with local published 2020 study [9].

Additional file 3: Table S3-1. Parameters, distributions and upper and lower values used in probabilistic sensitivity analyses

Additional file 1: Figure S1-1. Actual death proportion from PARTNER 2A trial and simulated Markov traces in the economic model. Figure S1-2. Simulated Markov traces for TAVI and SAVR arms in SURTAVI trial for intermediate surgical risk.

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Colorectal cancer (CRC) is a common form of cancer and the second-leading cause of cancer-related mortality in a number of developed countries. Five-year survival in the UK is less than 50%. Within England, invitations for CRC screening are sent once every two years to men and women aged 60 to 74 by the national screening programme. Uptake of CRC screening is associated with reduced mortality, but little is known about the impact of having a long-term condition (LTC) on uptake. Our objective was to examine the association between having LTC and uptake of population-based screening for CRC.

Prostate cancer is the most common cancer amongst men in the England and currently affects ~40,000 people around 6,000 of whom suffer from metastatic disease. Overall patients with metastatic disease have a life expectancy of less than 24 months and a poor prognosis.Docetaxel was the first agent to show survival benefit in metastatic Hormone Refractory Prostate Cancer (mHRPC) and since approval by NICE in 2006 (TA101) has for many years been the mainstay of treatment.To appraise the clinical and cost effectiveness of cabazitaxel within its marketing authorisation for treating hormone-relapsed metastatic prostate cancer previously treated with a docetaxel-containing regimen.

Background: Parametric modelling of survival data is important and reimbursement decisions may depend on the selected distribution. Accurate predictions require sufficiently flexible models to describe adequately the temporal evolution of the hazard function. A rich class of models is available among the framework of generalised linear models (GLMs) and its extensions, but these models are rarely applied to survival data. This manuscript describes the theoretical properties of these more flexible models, and compares their performance to standard survival models in a reproducible case-study. Methods: We describe how survival data may be analysed with GLMs and its extensions: fractional polynomials, spline models, generalised additive models, generalised linear mixed (frailty) models and dynamic survival models. For each, we provide a comparison of the strengths and limitations of these approaches. For the case-study we compare within-sample t, the plausibility of extrapolations and extrapolation performance based on data-splitting. Results: Viewing standard survival models as GLMs shows that many impose a restrictive assumption of linearity. For the case-study, GLMs provided better within-sample t and more plausible extrapolations. However, they did not improve extrapolation performance. We also provide guidance to aid in choosing between the different approaches based on GLMs and its extensions. Conclusions: The use of GLMs for parametric survival analysis can out-perform standard parametric survival models, although the improvements were modest in our case-study. This approach is currently seldom used. We provide guidance on both implementing these models and choosing between them. The reproducible case-study will help to increase uptake of these models.