ArticleMedical decision making : an international journal of the Society for Medical Decision Making2017
Extrapolation of Survival Curves from Cancer Trials Using External Information.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 5 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Structured expert elicitation for long-term survival outcomes in health technology assessment: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Pooled it
- Pooled it
- Cost-utility analysis of atezolizumab with bevacizumab in untreated unresectable or advanced hepatocellular carcinoma in France.PloS one · 2023Guideline
- The Use of Expert Elicitation among Computational Modeling Studies in Health Research: A Systematic Review.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Pooled it
- Methodology and results of real-world cost-effectiveness of carfilzomib in combination with lenalidomide and dexamethasone in relapsed multiple myeloma using registry data.The European journal of health economics : HEPAC : health economics in prevention and care · 2020Trial
- Flexible Survival Extrapolation with Blended Hazards: Accounting for Treatment Effect Waning in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Lung cancer screening in Lithuania: a cost-effectiveness analysis.Journal of comparative effectiveness research · 2026Article
- Article
- Monitoring overall survival in pivotal trials in indolent cancers.Statistics in biopharmaceutical research · 2025Article
- MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial.PharmacoEconomics · 2024Article
- An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?PharmacoEconomics · 2024Article
- Cost-effectiveness of lung cancer screening with volume computed tomography in Portugal.Journal of comparative effectiveness research · 2024Article
- General Population Mortality Adjustment in Survival Extrapolation of Cancer Trials: Exploring Plausibility and Implications for Cost-Effectiveness Analyses in HER2-Positive Breast Cancer in Sweden.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Shall We Screen Lung Cancer with Volume Computed Tomography in Austria? A Cost-Effectiveness Modelling Study.Cancers · 2024Article
- SurvInt: a simple tool to obtain precise parametric survival extrapolations.BMC medical informatics and decision making · 2024Article
- Multiple Real-World Data Sources in a Bayesian Framework to Inform Long-Term Survival Estimates of Mosunetuzumab in Patients with Follicular Lymphoma.Oncology and therapy · 2023Article
- survextrap: a package for flexible and transparent survival extrapolation.BMC medical research methodology · 2023Article
- Alternative cascade-testing protocols for identifying and managing patients with familial hypercholesterolaemia: systematic reviews, qualitative study and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2023Article
- Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundEstimates of life expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to the limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period. However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy. Here, we investigate the use of information external to the RCT data to inform model choice and estimation of life expectancy.
methodsWe used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion. We illustrate with a 5-year follow-up RCT of cetuximab plus radiotherapy v. radiotherapy alone for head and neck cancer.
resultsStandard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models, we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources achieved an adequate fit and predicted a 4.7-month (95% CrL: 0.4; 9.1) gain in life expectancy due to cetuximab.
conclusionsLong-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.