Period analysis provides more up-to-date estimates of cancer patient survival than traditional methods, but there is a trade-off between up-to-dateness and precision. Our objective was to compare the performance of period and complete analysis in terms of up-to-dateness and precision of survival estimates.
Five-year relative survival data actually observed for patients diagnosed with 1 of 20 common forms of cancer in Finland in 36 overlapping 5-year periods between 1958-1962 and 1993-1997 were compared with period estimates and various variants of complete estimates of 5-year relative survival potentially available during these periods.
At comparable levels of up-to-dateness, survival estimates from period analysis were more precise than survival estimates from complete analysis. At comparable levels of precision, period analysis provided more up-to-date survival estimates than did complete analysis.
These results further encourage more widespread use of period analysis as a standard tool for up-to-date monitoring of cancer patient survival by population-based cancer registries.
Resections for pancreatic cancer have been performed for 65 years, with approximately 20,000 reported. A number of authors claim a 5-year survival rate of 30% to 58%. Review of the literature reveals only about 1,200 5-year survivors; however, 10 times as many individual resected survivors have been reported (in various countries), and nonresected survivors are overlooked. This high survival percentage is obtained by reducing the subset on which calculations are based and by using methods such as the Kaplan-Meier method, which produces higher figures as increasing numbers of patients are lost to follow-up. After adjustments, hardly more than 350 resected survivors could be found. Revision of statistical methods is urgently needed.
In ungulates, parturition is correlated with a reduction in movement rate. With advances in movement-based technologies comes an opportunity to develop new techniques to assess reproduction in wild ungulates that are less invasive and reduce biases. DeMars et al. (2013, Ecology and Evolution 3:4149-4160) proposed two promising new methods (individual- and population-based; the DeMars model) that use GPS inter-fix step length of adult female caribou (Rangifer tarandus caribou) to infer parturition and neonate survival. Our objective was to apply the DeMars model to caribou populations that may violate model assumptions for retrospective analysis of parturition and calf survival. We extended the use of the DeMars model after assigning parturition and calf mortality status by examining herd-wide distributions of parturition date, calf mortality date, and survival. We used the DeMars model to estimate parturition and calf mortality events and compared them with the known parturition and calf mortality events from collared adult females (n = 19). We also used the DeMars model to estimate parturition and calf mortality events for collared female caribou with unknown parturition and calf mortality events (n = 43) and instead derived herd-wide estimates of calf survival as well as distributions of parturition and calf mortality dates and compared them to herd-wide estimates generated from calves fitted with VHF collars (n = 134). For our data, the individual-based method was effective at predicting calf mortality, but was not effective at predicting parturition. The population-based method was more effective at predicting parturition but was not effective at predicting calf mortality. At the herd-level, the predicted distributions of parturition date from both methods differed from each other and from the distribution derived from the parturition dates of VHF-collared calves (log-ranked test: ?2 = 40.5, df = 2, p
The natural development of cancers as well as the measures to fight the disease are often long processes that require decades of follow up. Available information on long-term survival will thus often appear outdated and irrelevant. A few years ago, period-survival analysis was proposed as a means to obtain more up-to-date information on long-term cancer survival. This article assesses period and conventional cohort-based survival analyses on their ability to predict future survival. Based on historical data from the nationwide Swedish Cancer Registry 5-, 10- and 15-year relative survival actually observed for patients diagnosed at one particular point in time are compared to the most recent period and cohort-based survival estimates available at that point in time. The study shows that period analysis can, in most cases, be used to provide more up-to-date long-term estimates of cancer survival. Period analysis reduces the time lag of the survival estimates by some 5-10 years for all cancers combined and especially affects the survival estimates for small intestine carcinoids, meningioma and intracranial neurinoma of the brain, non-seminoma testicular cancer, chronic lymphocytic leukaemia and Hodgkin's lymphoma.
The comparative analysis of morbidity of population of the Sibirsky federal okrug was implemented on the territories with presence/absence of the regional diagnostic center The sampling of eleven statistical materials was used. It is established that higher level of the diagnostics using special examination techniques in the diagnostic centers permits to increase the detect of pathology (oncologic included) in population and to assess adequately the need in important curative rehabilitating technologies.
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package.