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I am going to make a prediction model. What do I need to know?
Published in Thomas A. Gerds, Michael W. Kattan, Medical Risk Prediction, 2021
Thomas A. Gerds, Michael W. Kattan
Disease-specific survival is a popular outcome in cancer research when there are competing risks. However, it should be avoided in particular when the aim is prediction. The reason is that disease-specific survival does not have a clinically meaningful interpretation for the patient. It is the probability of not dying of the disease of interest assuming another cause of death does not occur, which is hypothetical. Instead one should treat death due to other causes as competing risks and predict the absolute risk of disease-specific death (cumulative incidence).
Renal Cell Cancer
Published in Pat Price, Karol Sikora, Treatment of Cancer, 2020
A number of prognostic scoring systems have been developed and validated for use in assessing risk of progression for patients who have undergone definitive treatment of their primary tumors. These include the University of California, Los Angeles integrated staging system (UISS) which incorporates all pathological sub-types (ECOG Performance Status, Fuhrman grade, TNM stage) and the Mayo Clinic stage, size, grade, and necrosis (SSIGN) score validated for use in clear cell carcinoma.3 They estimate disease-specific survival and overall survival respectively. A further, large, retrospective multivariate analysis of risk factors for relapse has also been reported by Leibovich et al.6
Decision-making and communication
Published in Peter Hoskin, Peter Ostler, Clinical Oncology, 2020
Survival is the commonest end point for a large trial comparing two or more treatments for cancer. Whilst apparently straightforward in its definition time, cause of death may be difficult to trace, particularly in trials continuing for many years and where the condition has a long natural history for example patients in trials of prostate and breast cancer. It is important to define the cause of death. This will allow a comparison of not only overall survival but also disease-specific survival, i.e. counting only those patients dying from the disease under investigation. It is always important, however, to analyse all causes of death, since this may on occasions reveal an excess of deaths from the complications of the treatment. A typical example of this is the long-term analysis of the results of radiotherapy for breast cancer, where a reduction in breast cancer death rate is seen in patients receiving radiotherapy, but overall survival differences between those receiving radiotherapy and those who did not is less. The explanation for this apparent anomaly was explained by an excess of non-cancer deaths, predominantly cardiovascular disease in the radiotherapy group which partially negated the reduction in breast cancer deaths.
Prognostic value of the nodal yield in oral squamous cell carcinoma: a systematic review and meta-analysis
Published in Expert Review of Anticancer Therapy, 2023
Jiajia Li, Yubo Xu, Jie Zhang, Shaohai Wang, Xiaoyu Wang, Huayan Guo, Guojun Miao
As shown in Figures 4 and 5, four studies reported results on disease-specific survival and disease-free survival. When pooling the results, both disease-specific survival (HR = 1.594, 95%CI = 0.996–2.552, p = 0.052; I2 = 81%) or disease-free survival (HR = 1.508, 95%CI = 0.924–2.460, p = 0.100; I2 = 41%) were not associated the lymph node yield. We conducted a sensitivity analysis to explore the potential source of heterogeneity by excluding the studies sequentially (Supplementary Figure s2). When the study by Jaber et al. [21] was excluded, heterogeneity decreased to I2 = 49%; meanwhile, the relationship between the lymph node yield and disease-specific survival was significant (HR = 1.715, 95% CI: 1.320–2.228, p < 0.001).
Significance of Pretreatment C-Reactive Protein, Albumin, and C-Reactive Protein to Albumin Ratio in Predicting Poor Prognosis in Epithelial Ovarian Cancer Patients
Published in Nutrition and Cancer, 2021
Naoko Komura, Seiji Mabuchi, Kotaro Shimura, Mahiru Kawano, Yuri Matsumoto, Tadashi Kimura
Primary endpoint of the current study is to investigate prognostic significance of CRP/Alb in EOC patients. Secondary endpoint is to compare the predictive ability of CRP/Alb with that of CRP or albumin. Continuous data were compared between the groups using Student’s t-test, Wilcoxon rank-sum test, or median test. Frequency counts and proportions were compared between groups using the χ2 test or two-tailed Fisher’s exact test. We performed univariate analysis by comparing the Kaplan-Meier curves using the log-rank test. Cox’s proportional hazards regression analysis was performed to identify significant independent prognostic factors for survival. Disease-specific death was defined as the death from an ovarian cancer. Disease-specific survival rate was defined as the percentage of patients who have not died from an ovarian cancer in a defined study period. The time period begins at the date of start of treatment and ends at the time of death from ovarian cancer. P values <0.05 were considered significant. All analyses were performed using JMP software, version 14.0 (SAS Institute, Cary, NC, USA).
Clinical management of squamous cell carcinoma of the tongue: patients not eligible for free flaps, a systematic review of the literature
Published in Expert Review of Anticancer Therapy, 2021
Giuseppe Colella, Raffaele Rauso, Davide De Cicco, Ciro Emiliano Boschetti, Brigida Iorio, Chiara Spuntarelli, Renato Franco, Gianpaolo Tartaro
Over the past decades, the evaluation of patients’ health-related quality of life (HRQOL) has become a common practice in oral cancer management. Since mortality and disease-specific survival still represent the primary outcome, impact of therapeutic strategies on patients’ everyday life is considered as a second endpoint nowadays [66]. Management of HRQOL issues represent a real challenge for surgeons, thus the authors focused their attention on searching for the best reconstruction [67,68,69]. However, the influence of different reconstructive procedures on HRQOL still give rise to some doubts, due to the lack of high level of evidence and the great differences reported in published papers. In the opinion of the authors, there are several confounding that must be taken in consideration (as well as tumor location, adjuvant radiotherapy, patients, tumor size and stage, extent of resection). Future studies should accurately evaluate how these factors influence the HRQOL, observing a large sample of patients and paying the best attention in allocating patients to the cohorts, thus reducing the number of confounding factors and selection biases.