Statistical Methods for Pantumor Analysis- Models to Account for Tumor-Level Heterogeneity

Author(s)

Swaminathan A1, Snider J2, Sondhi A2, Samant M2, Humblet O2
1Flatiron Health, Wood Ridge, NJ, USA, 2Flatiron Health, New York, NY, USA

OBJECTIVES

:
Recent tumor-agnostic regulatory approvals indicate growing interest in estimating effects of drugs and biomarkers on outcomes in specific tumor types (TTs) and across all TTs (pantumor effects), which can be challenging due to tumor-level differences in prognosis and data availability. We compared the performance of six models for estimating simulated tumor-specific and pantumor effects of a biomarker on overall survival (OS).

METHODS

:
Patient survival times were simulated based on patients’ biomarker status, tumor type, and the pantumor and tumor-specific effects of the biomarker on OS. We varied the number of TTs in each dataset (5-50), and the number of patients per TT (5-2000). Each permutation was run 100 times. To each dataset, we fit separate Cox models for each TT, a fixed-effect Cox (FE) model, a random-effects (RE) Cox model, a stratified Cox model, and the sample-size (SS) and minimum-risk (MR) alternatives to the stratified Cox (Merhotra et al., 2012). We report the absolute error (AE) comparing the estimated and true effects (log hazard).

RESULTS

:
In estimating tumor-specific effects, the RE model had the lowest AE overall (median: 0.11, IQR: 0.04-0.20) compared to the FE (median: 0.19, IQR: 0.07-0.47) and separate models (median: 0.2, IQR: 0.08-0.51). In TTs with 5 patients, the RE model had the lowest AE in 86.2% of simulations. In TTs with 2000 patients, RE and separate models performed similarly. In low variability scenarios (tumor-specific effect within 0.04 of pantumor effect), the RE model had the lowest AE in 79.8% of simulations. In high variability scenarios (tumor-specific effect >0.2 from pantumor effect), all models performed similarly. In estimating pantumor effects, all models had similar AE.

CONCLUSIONS

:
RE models performed favorably for estimating tumor-specific effects, and all models performed similarly for estimating pantumor effects.

Conference/Value in Health Info

2021-05, ISPOR 2021, Montreal, Canada

Value in Health, Volume 24, Issue 5, S1 (May 2021)

Code

PCN198

Topic

Clinical Outcomes, Methodological & Statistical Research

Topic Subcategory

Clinical Outcomes Assessment, Confounding, Selection Bias Correction, Causal Inference, Modeling, Simulation, Optimization

Disease

Oncology

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