A Causal Framework for Making Individualized Treatment Decisions in Oncology
We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/16/3923 |
| _version_ | 1797432349465509888 |
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| author | Pavlos Msaouel Juhee Lee Jose A. Karam Peter F. Thall |
| author_facet | Pavlos Msaouel Juhee Lee Jose A. Karam Peter F. Thall |
| author_sort | Pavlos Msaouel |
| collection | DOAJ |
| description | We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions. |
| first_indexed | 2024-03-09T09:59:06Z |
| format | Article |
| id | doaj.art-2a1dcbf2655e48b79db0b78b4b417c6f |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T09:59:06Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-2a1dcbf2655e48b79db0b78b4b417c6f2023-12-01T23:32:14ZengMDPI AGCancers2072-66942022-08-011416392310.3390/cancers14163923A Causal Framework for Making Individualized Treatment Decisions in OncologyPavlos Msaouel0Juhee Lee1Jose A. Karam2Peter F. Thall3Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Statistics, University of California, Santa Cruz, CA 95064, USADepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USAWe discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.https://www.mdpi.com/2072-6694/14/16/3923adjuvant therapycausal diagramsindividualized inferencespatient-specific decision-makingprecision medicineprognostic biomarkers |
| spellingShingle | Pavlos Msaouel Juhee Lee Jose A. Karam Peter F. Thall A Causal Framework for Making Individualized Treatment Decisions in Oncology Cancers adjuvant therapy causal diagrams individualized inferences patient-specific decision-making precision medicine prognostic biomarkers |
| title | A Causal Framework for Making Individualized Treatment Decisions in Oncology |
| title_full | A Causal Framework for Making Individualized Treatment Decisions in Oncology |
| title_fullStr | A Causal Framework for Making Individualized Treatment Decisions in Oncology |
| title_full_unstemmed | A Causal Framework for Making Individualized Treatment Decisions in Oncology |
| title_short | A Causal Framework for Making Individualized Treatment Decisions in Oncology |
| title_sort | causal framework for making individualized treatment decisions in oncology |
| topic | adjuvant therapy causal diagrams individualized inferences patient-specific decision-making precision medicine prognostic biomarkers |
| url | https://www.mdpi.com/2072-6694/14/16/3923 |
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