Modeling tumor measurement data to predict overall survival (OS) in cancer clinical trials
| Autor: | Ming Wen An, Sumithra J. Mandrekar, Jun Tang, Fang-Shu Ou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Pharmacology
Oncology medicine.medical_specialty Medicine (General) Cancer clinical trial business.industry Colorectal cancer General Medicine Patient response medicine.disease Article Clinical trial Tumor measurement data Cancer trial R5-920 RECIST Response Evaluation Criteria in Solid Tumors Internal medicine Covariate medicine Overall survival business Lung cancer Prediction |
| Zdroj: | Contemporary Clinical Trials Communications Contemporary Clinical Trials Communications, Vol 23, Iss, Pp 100827-(2021) |
| ISSN: | 2451-8654 |
| Popis: | Introduction Longitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST. Material and methods Joint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices. Results Data from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p Highlights • Two-stage modeling incorporating time-varying coefficients achieves better predictive accuracy than RECIST-alone. • Two–stage modeling offers the possibility of alternative endpoint definition. • Serial tumor measurements can be incorporated in OS prediction using joint modeling. • Joint modeling can potentially guide individualized medicine. |
| Databáze: | OpenAIRE |
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