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Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

1
Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
2
Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
3
Department of Statistics, University of California Santa Cruz, Santa Cruz, CA 95064, USA
4
Department of Biostatistics, Division of Basic Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 70030, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios H. Roukos
Cancers 2021, 13(11), 2741; https://doi.org/10.3390/cancers13112741
Received: 2 May 2021 / Revised: 18 May 2021 / Accepted: 30 May 2021 / Published: 1 June 2021
Clinicians often erroneously discount prognostic information as unlikely to change patient management. This is fueled by the mistaken belief that only “predictive” subgroups or biomarkers can modify the differences in clinical benefit between treatment choices. We use the treatment of metastatic clear cell carcinoma as an example to illustrate how clinical decisions can be informed by prognostic variables. Diametrically opposite decisions can be made depending on individual patient prognosis and on the clinical outcome of interest that clinicians choose to focus on. We also demonstrate why such patient-specific treatment decisions inevitably should be guided by each patient’s goals and values, which can be explicitly represented by utility functions.
We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk–benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making. View Full-Text
Keywords: individualized inferences; patient-specific decision-making; precision medicine; prognostic biomarkers; utilities individualized inferences; patient-specific decision-making; precision medicine; prognostic biomarkers; utilities
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MDPI and ACS Style

Msaouel, P.; Lee, J.; Thall, P.F. Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes. Cancers 2021, 13, 2741. https://doi.org/10.3390/cancers13112741

AMA Style

Msaouel P, Lee J, Thall PF. Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes. Cancers. 2021; 13(11):2741. https://doi.org/10.3390/cancers13112741

Chicago/Turabian Style

Msaouel, Pavlos, Juhee Lee, and Peter F. Thall 2021. "Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes" Cancers 13, no. 11: 2741. https://doi.org/10.3390/cancers13112741

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