Cure-fraction models
For therapies where a subgroup of patients appears to achieve long-term remission, extrapolating a single parametric curve to a lifetime horizon usually understates survival. EasyHTA’s PSM-cure template addresses this by building a composite curve in three phases: the observed KM data, a parametric extrapolation, and general-population mortality adjusted by a standardised mortality ratio.
What this is, and what it is not
The three phases
The curve follows the trial data over the observed period, a parametric extrapolation after that, and general-population mortality once the cohort is treated as cured. You control the two points where it changes over:
- The switch point, where the curve stops following the observed data and starts following your selected parametric distribution.
- The cure point, where it stops following that distribution and follows population mortality instead, adjusted by the standardised mortality ratio (SMR).
The switch point cannot fall after the cure point, and neither can fall after the time horizon.
Population mortality and the SMR
After the cure point, survival follows age-specific mortality from a national life table, multiplied by the SMR. An SMR of 1.0 asserts that surviving patients face exactly general-population mortality. Values above 1 carry a persistent excess risk, which is usually the more defensible assumption for oncology populations and is often expected by HTA bodies.
Set the cohort’s mean age at model entry so the life table is read from the right age, and choose the life table for your reference country. The table used is recorded with the analysis, so a re-run reproduces the same result. The cohort can optionally be split by sex.
An optional mortality floor prevents the extrapolated phase from declining more slowly than the general population, which can otherwise happen when a well-fitting distribution has a very flat tail. It is off by default, and turning it on is worth recording in your assumptions log.
Testing the cure assumption
When to use it
A cure assumption needs external support. Before using this template, consider whether there is a plateau in the observed KM curve that persists well beyond the point where events cluster, whether long-term registry or follow-up evidence supports sustained remission in this indication, and whether the cure point you have chosen falls where that evidence is strongest rather than where it flatters the ICER. NICE DSU TSD21 discusses cure and other survival models that TSD14 does not cover, and is the appropriate reference when a standard parametric model is not adequate.
If none of that support exists, the base PSM template, which has no cure point and no SMR, is the more defensible starting point. See Parametric distributions for the fitting step that precedes this one, and Quickstart for the end-to-end workflow.
References
Latimer NR (2011, last updated 2013). NICE DSU Technical Support Document 14: Undertaking survival analysis for economic evaluations alongside clinical trials, extrapolation with patient-level data. Sheffield: NICE DSU. sheffield.ac.uk/nice-dsu
Rutherford MJ, Lambert PC, Sweeting MJ, Pennington B, Crowther MJ, Abrams KR, Latimer NR (2020). NICE DSU Technical Support Document 21: Flexible methods for survival analysis. Sheffield: NICE DSU. sheffield.ac.uk/nice-dsu
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