ArticlePharmacoEconomics - open2026
Prediction Error in Quality-Adjusted Life Years in Economic Evaluations of Immune Checkpoint Inhibitors: A Comparison Based on Projected and Observed Updated Survival.
Article in PharmacoEconomics - open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurvival data from pivotal clinical trials are critical for estimating quality-adjusted life years (QALYs). However, immature survival data require extrapolation beyond observed follow-up to project outcomes, introducing potential prediction error into QALY estimates used in economic evaluations. Immune checkpoint inhibitors (ICIs) present unique extrapolation challenges due to delayed responses and extended survival benefits. We therefore quantify the QALY prediction error of early extrapolations by benchmarking them against updated follow-up at a common time horizon.
methodsUsing reconstructed individual patient data derived from published Kaplan-Meier curves of pivotal trials, this study assessed the accuracy of early survival extrapolations for ICIs approved in China by comparing early projections with QALYs obtained from updated data at the same horizon. A partitioned-survival framework using overall survival (OS) and progression-free survival (PFS) informed state occupancy, and QALYs were obtained via restricted mean survival time (RMST) integration of health-state utilities at the target horizon. Statistical analyses evaluated bias, precision, and agreement between extrapolated and updated QALY estimates. Linear regression and sensitivity analyses assessed the impact of target extrapolation horizon (T) on prediction error.
resultsIn total, 14 randomized controlled trials (4839 patients) were included for analysis. The mean deviation between extrapolated and observed QALYs was - 0.01 (95% CI - 0.03 to 0.01), with a mean absolute error (MAE) of 0.03 (95% CI 0.01 to 0.04). Strong agreement existed between extrapolated and updated QALYs (Spearman's ρ = 0.98, 95% CI 0.94 to 0.99, P < 0.001). Consistently, OS extrapolations showed minimal deviation (MAE: 0.56 months), while PFS tended to be underestimated (MAE: 1.91 months). Moreover, predictive error increased significantly with longer extrapolation periods for QALY (MAE increase: 0.011 QALY/year, P = 0.004) and OS (MAE increase: 0.342 months/year, P = 0.010).
conclusionsNear-horizon QALY prediction error was modest on average but increased with longer target extrapolation horizons. These findings support transparent reporting of extrapolation uncertainty and suggest that structured evidence reassessment may be particularly valuable as longer follow-up becomes available.
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.