Modality Value Is a Paired Comparison: Censoring- and Acquisition-Aware Estimation of Omics Modality Gain

Multimodal survival research typically measures an omics modality's value by the performance gain from adding it, a metric that shapes model design, modality selection, and testing resource allocation. In practice, however, omics testing covers only a subset of patients, its availability can depend on patient characteristics, disease status, and clinical workflow, and survival outcomes are further subject to right censoring—making the modality's true gain in the target population hard to obtain from the observed subset. Existing weighting methods typically estimate performance with and without the modality separately, turning a paired target into a difference of separately normalized estimates, which can inflate variance and turn “not estimable” into “no value.” We define modality value as the paired performance difference of a fixed predictor under controlled-input ablation, derive identification and non-identifiability conditions, and propose a model-agnostic framework: joint weighting corrects for selective acquisition and censoring, paired increments preserve score correspondence, support diagnostics flag unreliable cases, and rank discordance guides adaptive selection between estimators—without retraining or imputation. Across 10 TCGA cohorts, 3 model classes, 5 acquisition mechanisms, SurvBoard's natural missingness, and 10 cross-cohort transfers, naive estimation shows substantially higher error and flips the sign of the estimated gain in 26.0% of signal-present cases. A selection rule learned on semi-synthetic cohorts and frozen before external transfer improves over either fixed estimator.