Related Experiment Video
Updated: Feb 7, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Value of External Data in the Extrapolation of Survival Data: A Study Using the NJR Data Set
Mark Pennington1, Richard Grieve2, Jan Van der Meulen2
1King's Health Economics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Background:
Extrapolation of time-to-event data can be a critical component of cost-effectiveness analysis.
Objectives:
To contrast the value of external data on treatment effects as a selection aid in model fitting to the clinical data or for the direct extrapolation of survival.
Methods:
We assume the existence of external summary data on both treatment and control and consider two scenarios: availability of external individual patient data (IPD) on the control only and an absence of external IPD. We describe how the summary data can be used to extrapolate survival or to assess the plausibility of extrapolations of the clinical data. We assess the merit of either approach using a comparison of cemented and cementless total hip replacement as a case study. Merit is judged by comparing incremental net benefit (INB) obtained in scenarios with incomplete IPD with that derived from modeling external IPD on both treatment and control.
Results:
Measures of fit with the external summary data did not identify survival model specifications that best estimated INB. Addition of external IPD for the control only did not improve estimates of INB. Extrapolation of survival using the external summary data comparing treatment and control improved estimates of INB.
Conclusions:
Our case study indicates that summary data comparing treatment and control are more valuable than IPD limited to the control when extrapolating event rates for cost-effectiveness analysis. These data are best exploited in direct extrapolation of event rates rather than as an aid to select extrapolations on the basis of the clinical data.
Related Concept Videos
Censoring Survival Data
Design Example: Setting a Curve Using Design Data
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Data Reporting and Recording
Data Validation
Key parameters for method validation include:

