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      A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer)

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          Abstract

          Background

          Clinically, when the diagnosis of colorectal cancer is clear, patients are more concerned about their own prognosis survival. Special population with high risk of accidental death, such as elderly patients, is more likely to die due to causes other than tumors. The main purpose of this study is to construct a prediction model of cause-specific death (CSD) in elderly patients using competing-risk approach, so as to help clinicians to predict the probability of CSD in elderly patients with colorectal cancer.

          Methods

          The data were extracted from Surveillance, Epidemiology, and End Results (SEER) database to include ≥ 65-year-old patients with colorectal cancer who had undergone surgical treatment from 2010 to 2016. Using competing-risk methodology, the cumulative incidence function (CIF) of CSD was calculated to select the predictors among 13 variables, and the selected variables were subsequently refined and used for the construction of the proportional subdistribution hazard model. The model was presented in the form of nomogram, and the performance of nomogram was bootstrap validated internally and externally using the concordance index (C-index).

          Results

          Dataset of 19,789 patients who met the inclusion criteria were eventually selected for analysis. The five-year cumulative incidence of CSD was 31.405% (95% confidence interval [CI] 31.402–31.408%). The identified clinically relevant variables in nomogram included marital status, pathological grade, AJCC TNM stage, CEA, perineural invasion, and chemotherapy. The nomogram was shown to have good discrimination after internal validation with a C-index of 0.801 (95% CI 0.795–0.807) as well as external validation with a C-index of 0.759 (95% CI 0.716–0.802). Both the internal and external validation calibration curve indicated good concordance between the predicted and actual outcomes.

          Conclusion

          Using the large sample database and competing-risk analysis, a postoperative prediction model for elderly patients with colorectal cancer was established with satisfactory accuracy. The individualized estimates of CSD outcome for the elderly patients were realized.

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          Most cited references29

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          Competing risks and the clinical community: irrelevance or ignorance?

          Life expectancy has dramatically increased in industrialized nations over the last 200 hundred years. The aging of populations carries over to clinical research and leads to an increasing representation of elderly and multimorbid individuals in study populations. Clinical research in these populations is complicated by the fact that individuals are likely to experience several potential disease endpoints that prevent some disease-specific endpoint of interest from occurrence. Large developments in competing risks methodology have been achieved over the last decades, but we assume that recognition of competing risks in the clinical community is still marginal. It is the aim of this article to address translational aspects of competing risks to the clinical community. We describe clinical populations where competing risks issues may arise. We then discuss the importance of agreement between the competing risks methodology and the study aim, in particular the distinction between etiologic and prognostic research questions. In a review of 50 clinical studies performed in individuals susceptible to competing risks published in high-impact clinical journals, we found competing risks issues in 70% of all articles. Better recognition of issues related to competing risks and of statistical methods that deal with competing risks in accordance with the aim of the study is needed. Copyright © 2011 John Wiley & Sons, Ltd.
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            Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications.

            An evaluation of the effect of a healthcare intervention (or an exposure) must consider multiple possible outcomes, including the primary outcome of interest and other outcomes such as adverse events or mortality. The determination of the likelihood of benefit from an intervention, in the presence of other competing outcomes, is a competing risks problem. Although statistical methods exist for quantifying the probability of benefit from an intervention while accounting for competing events, these methods have not been widely adopted by clinical researchers. (1) To demonstrate the importance of considering competing risks in the evaluation of treatment effectiveness, and (2) to review appropriate statistical methods, and recommend how they might be applied. We reviewed 3 statistical approaches for analyzing the competing risks problem: (a) cause-specific hazard (CSH), (b) cumulative incidence function (CIF), and (c) event-free survival (EFS). We compare these methods using a simulation study and a reanalysis of a randomized clinical trial. Simulation studies evaluating the statistical power to detect the effect of intervention under different scenarios showed that: (1) CSH approach is best for detecting the effect of an intervention if the intervention only affects either the primary outcome or the competing event; (2) EFS approach is best only when the intervention affects both primary and competing events in the same manner; and (3) CIF approach is best when the intervention affects both primary and competing events, but in opposite directions. Using data from a randomized controlled trial, we demonstrated that a comprehensive approach using all 3 approaches provided useful insights on the effect of an intervention on the relative and absolute risks of multiple competing outcomes. CSH is the fundamental measure of outcome in competing risks problems. It is appropriate for evaluating treatment effects in the presence of competing events. Results of CSH analysis for primary and competing outcomes should always be reported even when EFS or CIF approaches are called for. EFS is appropriate for evaluating the composite effect of an intervention, only when combining different endpoints is clinically and biologically meaningful, and the treatment has similar effects on all event types. CIF is useful for evaluating the likelihood of benefit from an intervention over a meaningful period. CIF should be used for absolute risk calculations instead of the widely used complement of the Kaplan-Meier (1 - KM) estimator.
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              Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function.

              Typically, regression models for competing risks outcomes are based on proportional hazards models for the crude hazard rates. These estimates often do not agree with impressions drawn from plots of cumulative incidence functions for each level of a risk factor. We present a technique which models the cumulative incidence functions directly. The method is based on the pseudovalues from a jackknife statistic constructed from the cumulative incidence curve. These pseudovalues are used in a generalized estimating equation to obtain estimates of model parameters. We study the properties of this estimator and apply the technique to a study of the effect of alternative donors on relapse for patients given a bone marrow transplant for leukemia.
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                Author and article information

                Contributors
                wzhb403@163.com
                Journal
                World J Surg Oncol
                World J Surg Oncol
                World Journal of Surgical Oncology
                BioMed Central (London )
                1477-7819
                4 February 2020
                4 February 2020
                2020
                : 18
                : 30
                Affiliations
                [1 ]GRID grid.268415.c, Department of Gastrointestinal Surgery, , Affiliated Hospital of Yangzhou University, ; Yangzhou, 225100 People’s Republic of China
                [2 ]GRID grid.268415.c, Department of Gastrointestinal Surgery, Northern Jiangsu People’s Hospital, Clinical Medical School, , Affiliated Hospital of Yangzhou University, ; Yangzhou, 225002 People’s Republic of China
                [3 ]Department of General Surgery, Jiangsu Provincial Hospital of Integrated Traditional and Western Medicine, Nanjing, 210046 People’s Republic of China
                Author information
                http://orcid.org/0000-0001-8948-2517
                Article
                1805
                10.1186/s12957-020-1805-3
                7001222
                32019568
                94f3bb20-a930-4307-a1f2-7deedf3bd2f6
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 24 September 2019
                : 23 January 2020
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Surgery
                elderly patients,colorectal cancer,competing-risk,nomogram,prognostic analysis
                Surgery
                elderly patients, colorectal cancer, competing-risk, nomogram, prognostic analysis

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