ReviewCancers2021
Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Comparative effects of remimazolam and propofol on hemodynamic stability during sedation for painless gastroscopy: a randomized clinical trial.BMC anesthesiology · 2025Trial
- Article
- An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.Medical & biological engineering & computing · 2026Article
- Reproducibility of statistically significant phase III oncology trials: An In Silico meta-epidemiological analysis.European journal of cancer (Oxford, England : 1990) · 2025Article
- Survival-inferred fragility of statistical significance in phase III oncology trials.NPJ precision oncology · 2025Article
- Incomplete Toxicity Reporting and Use of Toxicity-Minimizing Language in Phase III Oncology Trials.JCO oncology practice · 2025Article
- Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.JCO precision oncology · 2024Article
- Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.JCO clinical cancer informatics · 2024Article
- Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials.medRxiv : the preprint server for health sciences · 2024Article
- Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.Clinical trials (London, England) · 2024Article
- Treatment Outcomes in Patients With Metastatic Renal Cell Carcinoma With Sarcomatoid and/or Rhabdoid Dedifferentiation After Progression on Immune Checkpoint Therapy.The oncologist · 2024Article
- Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Review
- Interpreting Randomized Controlled Trials.Cancers · 2023Review
- Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity.Biometrics · 2023Article
- Less is More? First Impressions From COSMIC-313.Cancer investigation · 2023Article
- Association Between Sites of Metastasis and Outcomes With Immune Checkpoint Inhibitors in Advanced Urothelial Carcinoma.Clinical genitourinary cancer · 2022Article
- Review
- The Big Data Paradox in Clinical Practice.Cancer investigation · 2022Article
- Adjuvant Systemic Therapies for Patients with Renal Cell Carcinoma: Choosing Treatment Based on Patient-level Characteristics.European urology oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.