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Period Analysis (period + analysis)
Selected AbstractsUp-to-date cancer survival: Period analysis and beyondINTERNATIONAL JOURNAL OF CANCER, Issue 6 2009Hermann Brenner Abstract Since its introduction in 1996, period analysis has been shown to be useful for deriving more up-to-date cancer survival estimates, and the method is now increasingly used for that purpose in national and international cancer survival studies. However, period analysis, like other commonly employed methods, is just a special case from a broad class of design options in the analysis of cancer survival data. Here, we explore a broader range of design options, including 2 model-based approaches, for deriving up-to-date estimates of 5- and 10-year relative survival for patients diagnosed in the most recent 5-year interval for which data are available. The performance of the various designs is evaluated empirically for 20 common forms of cancer using more than 50-year long time series of data from the Finnish Cancer Registry. Period analysis as well as the 2 model-based approaches, one using a "cohort-type model" and another using a "period-type model", all performed better than traditional cohort or complete analysis. Compared with "standard period analysis", the cohort-type model further increased up-to-dateness of survival estimates, whereas the period-type model increased their precision. While our analysis confirms advantages of period analysis over traditional methods in terms of up-to-dateness of cancer survival data, further improvements are possible by flexible use of model-based approaches. © 2008 Wiley-Liss, Inc. [source] Period analysis of variable stars by robust smoothingJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2004Hee-Seok Oh Summary., The objective is to estimate the period and the light curve (or periodic function) of a variable star. Previously, several methods have been proposed to estimate the period of a variable star, but they are inaccurate especially when a data set contains outliers. We use a smoothing spline regression to estimate the light curve given a period and then find the period which minimizes the generalized cross-validation (GCV). The GCV method works well, matching an intensive visual examination of a few hundred stars, but the GCV score is still sensitive to outliers. Handling outliers in an automatic way is important when this method is applied in a ,data mining' context to a vary large star survey. Therefore, we suggest a robust method which minimizes a robust cross-validation criterion induced by a robust smoothing spline regression. Once the period has been determined, a nonparametric method is used to estimate the light curve. A real example and a simulation study suggest that the robust cross-validation and GCV methods are superior to existing methods. [source] Up-to-date cancer survival: Period analysis and beyondINTERNATIONAL JOURNAL OF CANCER, Issue 6 2009Hermann Brenner Abstract Since its introduction in 1996, period analysis has been shown to be useful for deriving more up-to-date cancer survival estimates, and the method is now increasingly used for that purpose in national and international cancer survival studies. However, period analysis, like other commonly employed methods, is just a special case from a broad class of design options in the analysis of cancer survival data. Here, we explore a broader range of design options, including 2 model-based approaches, for deriving up-to-date estimates of 5- and 10-year relative survival for patients diagnosed in the most recent 5-year interval for which data are available. The performance of the various designs is evaluated empirically for 20 common forms of cancer using more than 50-year long time series of data from the Finnish Cancer Registry. Period analysis as well as the 2 model-based approaches, one using a "cohort-type model" and another using a "period-type model", all performed better than traditional cohort or complete analysis. Compared with "standard period analysis", the cohort-type model further increased up-to-dateness of survival estimates, whereas the period-type model increased their precision. While our analysis confirms advantages of period analysis over traditional methods in terms of up-to-dateness of cancer survival data, further improvements are possible by flexible use of model-based approaches. © 2008 Wiley-Liss, Inc. [source] Cancer survival in Germany and the United States at the beginning of the 21st century: An up-to-date comparison by period analysisINTERNATIONAL JOURNAL OF CANCER, Issue 2 2007Adam Gondos Abstract Transatlantic cancer survival comparisons are scarce and involve mostly aggregate European data from the late 1980s. We compare the levels of cancer patient survival achieved in Germany and the United States (US) by the beginning of the 21st century, using data from the Cancer Registry of Saarland/Germany and the SEER Program of the US. Age-adjusted 5- and 10-year relative survival for 23 common forms of cancer derived by period analysis for the 2000,2002 period were calculated, with additional detailed age- and stage-specific analyses for cancers with the highest incidence. Among the 23 cancer sites, 5 (10) year relative survival was significantly higher for 1 (2) and 8 (5) cancers in Germany and the US, respectively. In Germany, survival was significantly higher for patients with stomach cancer, whereas survival was higher in the US for patients with breast, cervical, prostate, colorectal and oral cavity cancer. Among the most common cancers, age-specific survival differences were particularly pronounced for older patients with breast, colorectal and prostate cancer. Survival advantages of breast cancer patients in the US were mainly due to more favorable stage distributions. This comprehensive survival comparison between Germany and the US suggests that although survival was similar for the majority of the compared cancer sites, long-term prognosis of patients continues to be better in the US for many of the most common forms of cancer. Among these, differences between patients with breast and prostate cancer are probably due to more intensive screening activities. © 2007 Wiley-Liss, Inc. [source] Assessing future changes in extreme precipitation over Britain using regional climate model integrationsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 11 2001P.D. Jones Abstract In a changing climate it is important to understand how all components of the climate system may change. For many impact sectors, particularly those relating to flooding and water resources, changes in precipitation intensity and amount are much more important than changes in temperature. This study assesses possible changes in extreme precipitation intensities estimated through both quantile and return period analysis over Britain. Results using a regional climate model (with greenhouse gas changes following the IS92a scenario for 2080,2100) indicate dramatic increases in the heaviest precipitation events over Britain. The results provide information to alter design storm intensities to take future climate change into account, for structures/projects that have long life times. Copyright © 2001 Royal Meteorological Society [source] Interpreting trends in cancer patient survivalJOURNAL OF INTERNAL MEDICINE, Issue 2 2006P. W. DICKMAN Abstract Data on cancer patient survival are an invaluable tool in the evaluation of therapeutic progress against cancer as well as other lethal diseases. As with all quantitative information routinely used in evidence-based clinical management , including diagnostic tests, prognostic markers and comparisons of therapeutic interventions , data on patient survival require evaluation based on an understanding of the underlying statistical methodology, methods of data collection and classification, and, most notably, clinical and biologic insight. This article contains an introduction to the methods used for estimating cancer patient survival, including cause-specific survival, relative survival and period analysis. The methods, and their interpretation, are illustrated through presentation of trends in incidence, mortality and patient survival for a range of different cancers. Our aim was to lay out the strengths and limitations of survival analysis as a tool in the evaluation of progress in the diagnosis and treatment of cancer. [source] | ||||||||||