Survival Data Mining

2011 ◽  
pp. 1896-1902
Author(s):  
Qiyang Chen
Keyword(s):  
Data Mining ◽  
Failure Analysis ◽  
Survival Data ◽  
Event History Analysis ◽  
Real Life ◽  
Event History ◽  
Event Analysis ◽  
History Analysis ◽  
Lifetime Data Analysis ◽  
The Times

Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can only happen at most once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data Analysis (LDA), Time to Event Analysis (TEA), Event History Analysis (EHA), and Time Failure Analysis (TFA) depending on the type of application the method is used for (Elashoff, 1997). Survival Data Mining (SDM) is a new term being coined recently (SAS, 2004). There are many models and variations on the different models for SA or failure analysis. This chapter discusses some of the more common methods of SA with real life applications. The calculations for the various models of SA are very complex. Currently, there are multiple software packages to assist in performing the necessary analyses much more quickly.

Survival Analysis and Data Mining

2011 ◽  
pp. 1077-1082
Author(s):  
Qiyang Chen ◽  
Alan Oppenheim ◽  
Dajin Wang
Keyword(s):  
Data Mining ◽  
Survival Analysis ◽  
Failure Analysis ◽  
Survival Data ◽  
Event History Analysis ◽  
Real Life ◽  
Event Analysis ◽  
History Analysis ◽  
Lifetime Data Analysis ◽  
The Times

Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can happen at most only once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data Analysis (LDA), Time to Event Analysis (TEA), Event History Analysis (EHA), and Time Failure Analysis (TFA), depending on the type of application for which the method is used (Elashoff, 1997). Survival Data Mining (SDM) is a new term that was coined recently (SAS, 2004). There are many models and variations of SA. This article discusses some of the more common methods of SA with real-life applications. The calculations for the various models of SA are very complex. Currently, multiple software packages are available to assist in performing the necessary analyses much more quickly.

Event History Analysis in Demography

1995 ◽  
Vol 49 (2) ◽  
pp. 355-357
Author(s):  
Johannes Huinink
Keyword(s):  
Event History Analysis ◽  
Event History ◽  
History Analysis

From source-oriented databases to event-history data files: a twelve-step action plan for the analysis of individual and household life histories

1998 ◽  
Vol 10 (1-3) ◽  
pp. 1-9
Author(s):  
Onno Boonstra ◽  
Maarten Panhuysen
Keyword(s):  
Life Histories ◽  
Event History Analysis ◽  
Action Plan ◽  
Event History ◽  
Flat File ◽  
Event History Data ◽  
History Analysis ◽  
Good Technique ◽  
Data Files ◽  
The Way

Population registers are recognised to be a very important source for demographic research, because it enables us to study the lifecourse of individuals as well as households. A very good technique for lifecourse analysis is event history analysis. Unfortunately, there are marked differences in the way the data are available in population registers and the way event history analysis expects them to be. The source-oriented approach of computing historical data calls for a ‘five-file structure’, whereas event history analysis only can handle fiat files. In this article, we suggest a series of twelve steps with which population register data can be transposed from a five-file structured database into a ‘flat file’ event history analysis dataset.

Gender Differences in the Link Between Marital Status and the Risk of Cognitive Impairment: Results From the Korean Longitudinal Study of Aging

2021 ◽  
pp. 009141502110241
Author(s):  
Yujin Kim
Keyword(s):  
Cognitive Impairment ◽  
Longitudinal Study ◽  
Marital Status ◽  
Event History Analysis ◽  
Population Aging ◽  
Event History ◽  
Never Married ◽  
History Analysis ◽  
Married Men ◽  
And Gender

In the context of South Korea, characterized by increasing population aging and a changing family structure, this study examined differences in the risk of cognitive impairment by marital status and investigated whether this association differs by gender. The data were derived from the 2006–2018 Korean Longitudinal Study of Aging. The sample comprised 7,568 respondents aged 45 years or older, who contributed 30,414 person-year observations. Event history analysis was used to predict the odds of cognitive impairment by marital status and gender. Relative to their married counterparts, never-married and divorced people were the most disadvantaged in terms of cognitive health. In addition, the association between marital status and cognitive impairment was much stronger for men than for women. Further, gender-stratified analyses showed that, compared with married men, never-married men had a higher risk of cognitive impairment, but there were no significant effects of marital status for women.

scholarly journals Using Event History Analysis in Historical Research: With Illustrations from a Study of the Passage of Women's Protective Legislation

1998 ◽  
Vol 43 (S6) ◽  
pp. 33-55 ◽  
Author(s):  
Holly J. McCammon
Keyword(s):  
Event History Analysis ◽  
Historical Research ◽  
Event History ◽  
Industrial Workers ◽  
Social Scientists ◽  
Policy Makers ◽  
History Analysis

Historians and social scientists often investigate the conditions that influence the occurrence of particular events. For instance, a researcher might be concerned with the causes of revolutionary action in some countries or the forces that unleash racial rioting in major cities. Or perhaps the researcher wishes to examine why industrial workers decide to strike or what prompts policy-makers to pass new legislation. In each of these examples, a qualitative shift occurs, from a circumstance without racial rioting in a particular city, for instance, to one with racial rioting. Event history analysis can aid researchers in uncovering the conditions that lead to such a shift.

Sex and Experience in the Academy Award Nomination Process

2004 ◽  
Vol 95 (2) ◽  
pp. 589-592 ◽  
Author(s):  
Anne E. Lincoln
Keyword(s):  
Sex Differences ◽  
Age Differences ◽  
Event History Analysis ◽  
Event History ◽  
Male And Female ◽  
History Analysis ◽  
Academy Award ◽  
Award Nomination ◽  
Age Disparity

Research has indicated significant age differences between male and female Academy Award nominees and winners. However, this discrepancy may be associated with sex differences in actors' ages when they first begin their acting careers. The present research uses event history analysis to investigate the duration of Academy Award nominees' careers from career start (first film) to first three Academy Award nominations. Analysis suggested controlling for an actor's age at first film explains the sex-age disparity between Academy Award nominees and winners.

Political Imprisonment and Adult Functioning: A Life Event History Analysis of Palestinians

2015 ◽  
Vol 28 (3) ◽  
pp. 223-231 ◽  
Author(s):  
Clea McNeely ◽  
Brian K. Barber ◽  
Carolyn Spellings ◽  
Robert Belli ◽  
Rita Giacaman ◽  
...  
Keyword(s):  
Event History Analysis ◽  
Event History ◽  
Life Event ◽  
History Analysis ◽  
Adult Functioning ◽  
Political Imprisonment

Understanding the Adoption of Voter Identification Laws in the American States

2017 ◽  
Vol 45 (4) ◽  
pp. 560-588 ◽  
Author(s):  
Daniel R. Biggers ◽  
Michael J. Hanmer
Keyword(s):  
Event History Analysis ◽  
Political Power ◽  
The State ◽  
Event History ◽  
Federal Legislation ◽  
The Past ◽  
Voter Identification ◽  
Extensive Analysis ◽  
American States ◽  
History Analysis

Recently, many states have reversed the decades-long trend of facilitating ballot access by enacting a wave of laws requesting or requiring identification from registrants before they vote. Identification laws, however, are not an entirely new phenomenon. We offer new theoretical insights regarding how changes in political power influence the adoption of identification laws. In the most extensive analysis to date, we use event history analysis to examine why states adopted a range of identification laws over the past several decades. We consistently find that the propensity to adopt is greatest when control of the governor’s office and legislature switches to Republicans (relationships not previously identified), and that this likelihood increases further as the size of Black and Latino populations in the state expands. We also find that federal legislation in the form of the Help America Vote Act seems to enhance the effects of switches in partisan control.

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