How Long Should We Wait? The Impact of Immature Data on Colorectal Cancer Decision Modeling.

Authors

Jaemin Sim, Gyeongseon Shin, Donghwan Lee, Gyeyoung Choi, SeungJin Bae

Published in

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

To identify a practical data maturity threshold at which model-based long-term survival projections become sufficiently reliable to inform health technology assessment (HTA).
This retrospective modeling study used real-world, patient-level data from the Korea Clinical Data Utilization Network for Research Excellence registry. Patients with KRAS wild-type advanced colorectal cancer receiving first-line cetuximab- or bevacizumab-based chemotherapy between 2013 and 2021 (N = 1,208) were included. Hypothetical immature datasets were created by right-censoring at 30%, 50%, and 70% maturity (30%, 50%, and 70% of deaths observed). Partitioned survival analysis (PartSA) and state-transition model (STM) were developed for each maturity level and validated against observed 6-year overall survival and life expectancy using absolute prediction error and coverage within observed 95% confidence intervals.
With highly immature data at 30% maturity, 6-year survival prediction errors ranged from 2.4%-11.1% (PartSA) and 4.5%-16.3% (STM). Prediction stability improved substantially at 50% maturity (PartSA 0.4%-5.8%, STM 0.04%-7.7%), with only modest gains at 70% maturity (0.2%-3.9% and 0.3%-6.1%). Life-expectancy deviations showed a similar pattern, narrowing from up to 0.4 years (PartSA) and 0.8 years (STM) at 30% maturity to within ±0.3 years at 50% and ±0.2 years at 70% maturity, regardless of modeling framework.
In this first-line advanced colorectal cancer case study, prediction stability improved substantially once 50% maturity was reached. This threshold may provide a practical benchmark for planning reassessment or evidence-updating strategies in HTA; however, its applicability to other cancers, treatment settings, and data structures requires further evaluation.

PMID:
42641950
Bibliographic data and abstract were imported from PubMed on 26 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.