Related Experiment Video
Updated: May 8, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
The median informs the message: accuracy of individualized scenarios for survival time based on oncologists'
Belinda E Kiely1, Andrew J Martin, Martin H N Tattersall
1Belinda E. Kiely, Andrew J. Martin, and Martin R. Stockler, National Health and Medical Research Council Clinical Trials Centre, University of Sydney; Belinda E. Kiely, Martin H.N. Tattersall, Nicholas R.C. Wilcken, Philip J. Beale, and Martin R. Stockler, Sydney Medical School, University of Sydney; Belinda E. Kiely, Martin H.N. Tattersall, Philip J. Beale, and Martin R. Stockler, Sydney Cancer Centre-Royal Prince Alfred and Concord Hospitals, Sydney; David Goldstein, Prince of Wales Hospital Clinical School, University of New South Wales, Kensington; Nicholas R.C. Wilcken, Westmead Hospital, Westmead; Ehtesham A. Abdi, Tweed Hospital, Tweed Heads; Amanda Glasgow, Wollongong Hospital, Wollongong, New South Wales; Anna K. Nowak, School of Medicine and Pharmacology, University of Western Australia, Crawley; Anna K. Nowak, Sir Charles Gardner Hospital, Nedlands, Western Australia; David K. Wyld, Royal Brisbane and Women's Hospital, Brisbane, Queensland; Michael Jefford, Peter MacCallum Cancer Centre; Michael Jefford, University of Melbourne, Melbourne, Victoria, Australia; and Paul A. Glare, Memorial Sloan-Kettering Cancer Center, New York, NY.
Purpose:
To determine the accuracy and usefulness of oncologists' estimates of survival time in individual patients with advanced cancer.
Patients And Methods:
Twenty-one oncologists estimated the "median survival of a group of identical patients" for each of 114 patients with advanced cancer. Accuracy was defined by the proportions of patients with an observed survival time bounded by prespecified multiples of their estimated survival time. We expected 50% to live longer (or shorter) than their oncologist's estimate (calibration), 50% to live from half to double their estimate (typical scenario), 5% to 10% to live ≤ one quarter of their estimate (worst-case scenario), and 5% to 10% to live three or more times their estimate (best-case scenario). Estimates within 0.67 to 1.33 times observed survival were deemed precise. Discriminative value was assessed with Harrell's C-statistic and prognostic significance with proportional hazards regression.
Results:
Median survival time was 11 months. Oncologists' estimates were relatively well-calibrated (61% shorter than observed), imprecise (29% from 0.67 to 1.33 times observed), and moderately discriminative (Harrell C-statistic 0.63; P = .001). The proportion of patients with an observed survival half to double their oncologist's estimate was 63%, ≤ one quarter of their oncologist's estimate was 6%, and three or more times their oncologist's estimate was 14%. Independent predictors of observed survival were oncologist's estimate (hazard ratio [HR] = 0.92; P = .004), dry mouth (HR = 5.1; P < .0001), alkaline phosphatase more than 101 U/L (HR = 2.8; P = .0002), Karnofsky performance status ≤ 70 (HR = 2.3; P = .007), prostate primary (HR = 0.23; P = .002), and steroid use (HR = 2.4; P = .02).
Conclusion:
Oncologists' estimates of survival time were relatively well-calibrated, moderately discriminative, independently associated with observed survival, and a reasonable basis for estimating worst-case, typical, and best-case scenarios for survival.
Related Concept Videos
01:21Cancer Survival Analysis
01:24Kaplan-Meier Approach
01:20Comparing the Survival Analysis of Two or More Groups
01:18Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
01:20Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:15Assumptions of Survival Analysis