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Topic: Survival analysis

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	Survival analysis - Wikipedia, the free encyclopedia
		Survival analysis is a branch of statistics which deals with death in biological organisms and failure in mechanical systems.
		In the case of biological survival, death is unambiguous, but for mechanical reliability, failure may not be well-defined, for there may well be mechanical systems in which failure is partial, a matter of degree, or not otherwise not localized in time.
		The likelihood function for a survival model, in the presence of censored data, is formulated as follows.
	en.wikipedia.org /wiki/Survival_analysis (1589 words)

	Survival
		Survival analysis is used to study the pattern of survival or failure over time.
		Survival analysis usually should be used if the variable of interest is a time to an event.
		Survival function: S(t) (also called the survivorship function) the survival function shows the fraction of the original group who survive at various points in time.
	www.uncp.edu /home/frederick/DSC510/Survival.htm (1310 words)

	Introduction to Survival Analysis with SAS Seminar
		The term survival analysis is used predominately in biomedical sciences where the interest is in observing time to death either of patients or of laboratory animals.
		There are certain aspects of survival analysis data, such as censoring and non-normality, that generate great difficulty when trying to analyze the data using traditional statistical models such as multiple linear regression.
		The point of survival analysis is to follow subjects over time and observe at which point in time they experience the event of interest.
	www.ats.ucla.edu /stat/sas/seminars/sas_survival/default.htm (3991 words)

	eBMJ -- Statistics at Square One: Survival analysis (Site not responding. Last check: 2007-10-20)
		Survival analysis is concerned with studying the time between entry to a study and a subsequent event.
		The survival curve is unchanged at the time of a censored observation, but at the next event after the censored observation the number of people "at risk" is reduced by the number censored between the two events.
		To compare two survival curves produced from two groups A and B we use the rather curiously named log rank test,1 so called because it can be shown to be related to a test that uses the logarithms of the ranks of the data.
	bmj.bmjjournals.com /collections/statsbk/12.shtml (1478 words)

	Kaplan Meier Survivial Analysis using Prism (Site not responding. Last check: 2007-10-20)
		With some experiments, the outcome is a survival time, and you want to compare the survival of two or more groups.
		Survival curves show, for each plotted time on the X axis, the portion of all individuals surviving as of that time.
		The term "survival" is a bit misleading; you can use survival curves to study times required to reach any well-defined end point (e.g., re-occlusion of a grafted blood vessel, first metastasis, discharge from the hospital).
	www.graphpad.com /prism/tutorials/survival/survival_analysis.htm (842 words)

	Breast cancer: patient characteristics and survival analysis at Salmaniya Medical Complex, Bahrain
		Survival analysis using the Kaplan-Meier method showed 38 cancer-related deaths (32.5%) among the 117 subjects in the study.
		For the 21 cases in age group 30-39 years, the cumulative survival rate was 64.00% (51.20%); for the 39 cases in age group 40-49 years, 67.35% (unchanged); for 21 cases in age group 50-59 years, 84.82% (72.70%); for 19 cases in age group 60-69 years, 43.86% (29.24%).
		Survival analyses by clinical stage showed an 87.5% cumulative survival rate after 5 years for stage I, 74.48% for stage II, 73.46% for stage III and 48.0% for stage IV.
	www.emro.who.int /publications/emhj/0503/01.htm (3114 words)

	Survival/Failure Time Analysis
		The major distributions that have been proposed for modeling survival or failure times are the exponential (and linear exponential) distribution, the Weibull distribution of extreme events, and the Gompertz distribution.
		An assumption of the proportional hazard model is that the hazard function for an individual (i.e., observation in the analysis) depends on the values of the covariates and the value of the baseline hazard.
		The purpose of a stratified analysis is to test the hypothesis whether identical regression models are appropriate for different groups, that is, whether the relationships between the independent variables and survival are identical in different groups.
	www.statsoft.com /textbook/stsurvan.html (2842 words)

	Event History & Survival Analysis
		Survival methods are explicitly designed to deal with censoring and time-dependent covariates in a statistically correct way.
		Event History and Survival Analysis is a complete course covering both the theory and practice of survival methodology.
		Those who are relatively new to survival analysis concepts and who are in a pharmaceutical job would benefit immensely by taking this class in the first or second year of their work.
	www.statisticalhorizons.com /gpage1.html (1456 words)

	Introduction \| CRM Product Performance Report
		The Interval Survival Probability (F) is the estimate of probability of surviving to the end of the interval assuming the device was working at the beginning of the interval.
		Cumulative Survival Probability (G) is the estimate of the unconditional probability of surviving to the end of the interval.
		The probability of surviving to 132 months in the example is estimated for the table to be 0.341, or 34.1%.
	www.medtronic.com /crm/performance/introduction/introduction.html (2035 words)

	Survival Selection Tab
		On the Selection tab for a Survival session, there are checkboxes for standard case selections made for survival runs.
		When you calculate survival statistics that do not use expected rate data (such as observed survival rates), you have the option to turn these exclusions on or off.
		Using these exclusions for observed survival allows you to analyze the same cohort that would be used in relative survival calculations -- assuming you are working with the same expected rate file.
	seer.cancer.gov /seerstat/508_WebHelp/Survival_Selection_Tab.htm (686 words)

	Statistics Solutions: Event History Analysis and Survival Analysis
		As such it is a specialized subfield of time series analysis which uses techniques, such as Poisson regression, which are designed to analyze rare events (time series in which most data are non-events).
		Event history analysis can be a form of panel study in which the periods of observation are not arbitrarily spaced but instead measurement is taken at each stage of a sequence of events.
		If events are well distributed over time, yes, but the point of event analysis is the analysis of rare events which are not well distributed.
	www.statisticssolutions.com /Event-History-Survival-Analysis.htm (465 words)

	Survival analysis for a large scale forest health issue: Missouri oak decline (Site not responding. Last check: 2007-10-20)
		Survival analysis for a large scale forest health issue: Missouri oak decline
		Abstract: Survival analysis methodologies provide novel approaches for forest mortality analysis that may aid in detecting, monitoring, and mitigating of large-scale forest health issues.
		Survival analyses, such as those applied in this study, may enable testing of forest health hypotheses using large-scale inventories.
	www.ncrs.fs.fed.us /pubs/3437 (302 words)

	Amazon.com: Bayesian Survival Analysis: Books: Joseph G. Ibrahim,Ming-Hui Chen,Debajyoti Sinha (Site not responding. Last check: 2007-10-20)
		Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics.
		The analysis of time-to-event data, generally called survival analysis, arises in many fields of study, including medicine, biology, engineering, public health, epidemiology, and economics.
		It is often the case in fitting survival data to a Cox model that one finds the proportional hazard assumptions fail to hold.
	www.amazon.com /Bayesian-Survival-Analysis-Joseph-Ibrahim/dp/0387952772 (3433 words)

	GSBS Course: Survival Analysis
		Survival data are commonly encountered in scientific investigations, especially in clinical trials and epidemiologic studies.
		One of the primary topics is the estimation of survival function based on censored data, which include parametric failure-time models, and nonparametric Kaplan-Meier estimate of the survival distribution.
		The most applicable to clinical trials and applied work is regression analysis for censored survival data.
	www.uth.tmc.edu /gsbs/courses/gs010023.html (156 words)

	Papers on Discrete-Time Survival Analysis
		Extended comprehensive presentation of the application of single-spell discrete-time survival analysis to investigate the onset and cessation of critical human conditions (such as developmental stage, psychological condition, addiction, etc.) and their treatment.
		Parallel presentation to the Singer and Willett (1991) paper that overviews the implementation and argues for the advantages of survival analysis for investigating the educational career, particularly student dropout and teacher attrition.
		Overview of how survival analysis can be used to investigate human development in order to address questions of whether and when developmental milestones occur.
	gseacademic.harvard.edu /~willetjo/dsta.htm (355 words)

	Survival Analysis Features Available in NCSS
		Survival analysis includes several techniques to study data in which the response variable is elapsed time.
		Procedures include survival distribution analysis (including the Kaplan-Meier survival distribution estimate), log rank tests, and proportional hazards regression.
		NCSS is one of the most accurate statistical analysis programs available.
	www.ncss.com /survival.html (208 words)

	Updated Overall Survival Analysis Presented on Nexavar Phase III Trial - PipelineReview.com \| Business Intelligence ...
		The updated analysis confirmed that overall survival was longer for Nexavar than for placebo patients.
		The primary endpoint of the study is overall survival, with progression-free survival (PFS), overall response rate, quality of life, and safety also being assessed.
		Overall survival results presented at ASCO 2006 were based on an analysis of 367 survival events (patient deaths) that had occurred by November 30, 2005.
	www.pipelinereview.com /joomla/content/view/3924/110 (1409 words)

	Amazon.com: Survival Analysis (Wiley Classics Library): Books: Rupert G. Jr. Miller (Site not responding. Last check: 2007-10-20)
		Miller "Survival analysis is a loosely defined statistical term that encompasses a variety of statistical techniques for analyzing positive-valued random variables..." (more)
		A concise summary of the statistical methods used in the analysis of survival data with censoring.
		Survival analysis is a loosely defined statistical term that encompasses a variety of statistical techniques for analyzing positive-valued random variables.
	www.amazon.com /Survival-Analysis-Wiley-Classics-Library/dp/0471255483 (995 words)

	Survival Analysis for All-or-None Compliance - Biometry Research Group
		Analysis of survival data from a randomized trial with all-or-none compliance: estimating the cost-effectiveness of a cancer screening program.
		It is first necessary to load the packages in the "composite linear models" section.
		To reproduce the unconstrained and constrained intent-to-treat calculations in the manuscript, load ezhiptt.m and type fitallitt[t].
	www.cancer.gov /prevention/bb/survival_analysis.html (88 words)

	CSCAR at the University of Michigan: Workshops and Seminars: Survival Analysis
		This workshop, held over two days, covers basic concepts of and common analytical approaches for time-to-event data, known variously as survival analysis (in biological and medical sciences), event history analysis (in social sciences), or reliability analysis (in engineering).
		Brenda Gillespie is the Associate Director of CSCAR and Assistant Professor in the Department of Biostatistics at The University of Michigan.
		She has extensive experience as a statistical consultant, and specializes in the various methods for analysis of censored data.
	www.umich.edu /~cscar/workshops/survival.html (358 words)

	Survival Analysis
		Anderson, P.K. 1991: Survival analysis 1982-1991: The second decade of the proportional hazards regression model.
		Crowley,J. and Breslow, N. 1984: Statistical analysis of survival data.
		Singer,J.D. and Willett,J.B. 1991: Modeling the days of our lives: using survival analysis when designing and analyzing longitudinal studies of duration and the timing of events.
	www.vetschools.co.uk /EpiVetNet/survival_analysis.htm (137 words)

	ISER - Survival Analysis with Stata: Course EC968
		Part II: Introduction to the analysis of spell duration data' given by Professor Stephen P. Jenkins of the Institute for Social and Economic Research.
		This is a program for estimating 'split population' survival models, otherwise known in biostatistics as 'cure' models.
		In the standard survival model, all cases are assumed to fail within finite time.
	www.iser.essex.ac.uk /teaching/degree/stephenj/ec968 (1138 words)

	NIOSH: Life Table Analysis System \| CDC/NIOSH
		Life table analyses originated as a form of survival analysis in which survival times are grouped into intervals.
		Rates for the cohort under observation might then be compared with external rates for some large (typically unexposed) population to obtain an estimate of the relative survival of the cohort compared with the external population.
		The Life Table Analysis System (LTAS) was developed at the National Institute for Occupational Safety and Health (NIOSH) during the 1970s.
	www.cdc.gov /niosh/LTAS (328 words)

	An Introduction to Survival Analysis Using Stata, Revised Edition
		An Introduction to Survival Analysis Using Stata, Revised Edition, is an ideal tutorial for professional data analysts who want to learn survival analysis or want to learn to use Stata to analyze survival data.
		Because survival analysis requires specialized data management and analysis procedures, Stata provides the st family of commands for organizing and summarizing survival data.
		This book develops from first principles the statistical concepts unique to survival data and assumes that the reader has only a knowledge of basic probability and statistics and a working knowledge of Stata.
	www.stata-press.com /books/saus.html (453 words)

	Introduction to Survival Analysis Using Empirical Hazards
		This course introduces survival analysis in the context of business data mining.
		Empirical hazards provide a window on customer behavior that is useful in itself and also provides the basis for calculating survival curves.
		A background in business analysis, statistics, or mathematics is helpful but is not essential.
	support.sas.com /training/us/crs/bisa.html (273 words)

	News: Updated Overall Survival Analysis Presented on Nexavar Phase III Trial. Genetic Engineering News - Biotechnology ...
		News: Updated Overall Survival Analysis Presented on Nexavar Phase III Trial.
		These data, while they did not reach the pre- specified result required to stop the OS analysis early, suggest a favorable survival trend for patients who received Nexavar.
		Overall survival results presented at ASCO 2006 were based on an analysis of 367 survival events (patient deaths) that had occurred by
	www.genengnews.com /news/bnitem.aspx?name=2204737 (1332 words)

	Statistical Horizons
		Statistical Horizons offers short courses on Survival Analysis, Categorical Data Analysis and Missing Data.
		These courses are regularly presented as public seminars, and can also be given on-site at your firm.
		February 9-10, Las Vegas, NV This is a 2-day version of our 5-day course on Survival Analysis.
	www.statisticalhorizons.com (59 words)

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