Net Survival in Survival Analyses for Patients with Cancer: A Scoping Review (original) (raw)
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Cancer, 2017
Robust comparisons of population-based cancer survival estimates require tight adherence to the study protocol, standardized quality control, appropriate life tables of background mortality, and centralized analysis. The CONCORD program established worldwide surveillance of population-based cancer survival in 2015, analyzing individual data on 26 million patients (including 10 million US patients) diagnosed between 1995 and 2009 with 1 of 10 common malignancies. In this Cancer supplement, we analyzed data from 37 state cancer registries that participated in the second cycle of the CONCORD program (CONCORD-2), covering approximately 80% of the US population. Data quality checks were performed in 3 consecutive phases: protocol adherence, exclusions, and editorial checks. One-, 3-, and 5-year age-standardized net survival was estimated using the Pohar Perme estimator and state- and race-specific life tables of all-cause mortality for each year. The cohort approach was adopted for patie...
Estimating survival in advanced cancer: a comparison of estimates made by oncologists and patients
Supportive Care in Cancer, 2019
Purpose To compare estimates of expected survival time (EST) made by patients with advanced cancer and their oncologists. Methods At enrolment patients recorded their "understanding of how long you may have to live" in best-case, most-likely, and worst-case scenarios. Oncologists estimated survival time for each of their patients as the "median survival of a group of identical patients". We hypothesized that oncologists' estimates of EST would be unbiased (~50% longer or shorter than the observed survival time [OST]), imprecise (< 33% within 0.67 to 1.33 times OST), associated with OST, and more accurate than patients' estimates of their own survival. Results Twenty-six oncologists estimated EST for 179 patients. The median estimate of EST was 6.0 months, and the median OST was 6.2 months. Oncologists' estimates were unbiased (56% longer than OST), imprecise (27% within 0.67 to 1.33 times OST), and significantly associated with OST (HR 0.88, 95% CI 0.82 to 0.93, p < 0.01). Only 41 patients (23%) provided a numerical estimate of their survival with 107 patients (60%) responding "I don't know". The median estimate by patients for their most-likely scenario was 12 months. Patient estimates of their most-likely scenario were less precise (17% within 0.67 to 1.33 times OST) and more likely to overestimate survival (85% longer than OST) than oncologist estimates. Conclusion Oncologists' estimates were unbiased and significantly associated with survival. Most patients with advanced cancer did not know their EST or overestimated their survival time compared to their oncologist, highlighting the need for improved prognosis communication training. Trial registration ACTRN1261300128871 This research has been presented in part by Smith-Uffen MES, Martin AJ, Johnson SB et al: Estimating survival in advanced cancer: a comparison of estimates made by oncologists and patients.
Choosing the net survival method for cancer survival estimation
European Journal of Cancer, 2013
Background: A new net survival method has been introduced by Pohar Perme et al. (2012 [4]) and recommended to substitute the relative survival methods in current use for evaluating population-based cancer survival. Methods: The new method is based on the use of continuous follow-up time, and is unbiased only under non-informative censoring of the observed survival. However, the populationbased cancer survival is often evaluated based on annually or monthly tabulated follow-up intervals. An empirical investigation based on data from the Finnish Cancer Registry was made into the practical importance of the censoring and the level of data tabulation. A systematic comparison was made against the earlier recommended Ederer II method of relative survival using the two currently available computer programs (Pohar Perme (2013) [10] and Dickman et al. (2013) [11]). Results: With exact or monthly tabulated data, the Pohar-Perme and the Ederer II methods give, on average, results that are at five years of follow-up less than 0.5% units and at 10 and 14 years 1-2% units apart from each other. The Pohar-Perme net survival estimator is prone to random variation and may result in biased estimates when exact follow-up times are not available or follow-up is incomplete. With annually tabulated follow-up times, estimates can deviate substantially from those based on more accurate observations, if the actuarial approach is not used. Conclusion: At 5 years, both the methods perform well. In longer follow-up, the Pohar-Perme estimates should be interpreted with caution using error margins. The actuarial approach should be preferred, if data are annually tabulated.
Predicting Survival for Patients with Metastatic Disease
International Journal of Radiation Oncology*Biology*Physics
This prospective study aimed to determine the accuracy of radiation oncologists in predicting the survival of patients with metastatic disease receiving radiation therapy and to understand factors associated with their accuracy. Methods and Materials: This single-institution study surveyed 22 attending radiation oncologists to estimate patient survival. Survival predictions were defined as accurate if the observed survival (OS) was within the correct survival prediction category (0-6 months, >6-12 months, >12-24 months, and >24 months). The physicians made survival estimates for each course of radiation, yielding 877 analyzable predictions for 689 unique patients. Data analysis included Stuart's Tau C, logistic regression models, ordinal logistic regression models, and stepwise selection to examine variable interactions. Results: Of the 877 radiation oncologists' predictions, 39.7% were accurate, 26.5% were underestimations, and 33.9% were overestimations. Stuart's Tau C showed low correlation between OS and survival estimates (0.3499), consistent with the inaccuracy reported in the literature. However, results showed less systematic overprediction than reported in the literature. Karnofsky performance status was the most significant predictor of accuracy, with greater accuracy for patients with shorter OS. Estimates were also more accurate for patients with lower Karnofsky performance status. Accuracy by patient age varied by primary site and race. Physician years of experience did not correlate with accuracy. Conclusions: The sampled radiation oncologists have a 40% accuracy in predicting patient survival. Future investigation should explore how survival estimates influence treatment decisions and how to improve survival prediction accuracy.
Cancer Survival Estimates Due to Non-Uniform Loss to Follow-Up and Non-Proportional Hazards
Asian Pacific journal of cancer prevention : APJCP, 2017
Background: Cancer survival depends on loss to follow-up (LFU) and non-proportional hazards (non-PH). If LFU is high, survival will be over-estimated. If hazard is non-PH, rank tests will provide biased inference and Cox-model will provide biased hazard-ratio. We assessed the bias due to LFU and non-PH factor in cancer survival and provided alternate methods for unbiased inference and hazard-ratio. Materials and Methods: Kaplan-Meier survival were plotted using a realistic breast cancer (BC) data-set, with >40%, 5-year LFU and compared it using another BC data-set with <15%, 5-year LFU to assess the bias in survival due to high LFU. Age at diagnosis of the latter data set was used to illustrate the bias due to a non-PH factor. Log-rank test was employed to assess the bias in p-value and Cox-model was used to assess the bias in hazard-ratio for the non-PH factor. Schoenfeld statistic was used to test the non-PH of age. For the non-PH factor, we employed Renyi statistic for infe...
International Journal of Cancer, 2009
Model-based projections were shown to be useful for deriving most up-to-date population-based cancer survival estimates. However, the performance of these projections, which can be derived by various approaches, has only been evaluated in very few cancer patient populations. Using incidence and follow-up data for 22 common cancers from 9 long-standing population-based cancer registries from diverse parts of Europe, we compared the performance of model-based period and cohort analysis for predicting 5-year relative survival of patients diagnosed in 1996-2000 against standard survival analysis approaches (cohort, complete and period analysis). Overall, model-based predictions provided a best estimate of the later observed actual survival in 135 of 198 occasions, compared to 25, 18 and 33 occasions for cohort, complete and period analysis, respectively. Projections based on cohort and period type modeling performed essentially equally well on average, and their performance was better for more common cancers, in registries with larger population bases, and for cancers subjected to continuous clinical progress and/or ongoing screening efforts. Projections from model-based analysis may contribute to improved timeliness of monitoring of concurrent trends in population-based cancer survival in cancer registries operating in different populations and socioeconomic environments.
Correcting for misclassification and selection effects in estimating net survival in clinical trials
BMC Medical Research Methodology
Background: Net survival, a measure of the survival where the patients would only die from the cancer under study, may be compared between treatment groups using either "cause-specific methods", when the causes of death are known and accurate, or "population-based methods", when the causes are missing or inaccurate. The latter methods rely on the assumption that mortality due to other causes than cancer is the same as the expected mortality in the general population with same demographic characteristics derived from population life tables. This assumption may not hold in clinical trials where patients are likely to be quite different from the general population due to some criteria for patient selection. Methods: In this work, we propose and assess the performance of a new flexible population-based model to estimate long-term net survival in clinical trials and that allows for cause-of-death misclassification and for effects of selection. Comparisons were made with cause-specific and other population-based methods in a simulation study and in an application to prostate cancer clinical trial data. Results: In estimating net survival, cause-specific methods seemed to introduce important biases associated with the degree of misclassification of cancer deaths. The usual population-based method provides also biased estimates, depending on the strength of the selection effect. Compared to these methods, the new model was able to provide more accurate estimates of net survival in long-term clinical trials. Conclusion: Finally, the new model paves the way for new methodological developments in the field of net survival methods in multicenter clinical trials.
Clinical Survival Predictors in Patients With Advanced Cancer
Archives of Internal Medicine, 2000
Aim of the study was to examine prospectively the prognostic predictive value of some parameters including the five clinical variables that constitute the PPI and to validate the PPI in a cohort of terminally ill Egyptian cancer patients referred to a specialized palliative care unit (PCU).