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Results for “Event History Analysis”

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At least 19 recordsLinked to original sources
PubMed1996

Interval-censored event history analysis.

"Event history analysis...has [over time] branched out in two directions: on the one hand, surveys collecting retrospective information on respondent's life course, family and occupational histories, residential mobility...; on the other, individual biographies compiled by extracting information from administrative sources (vital registration data, census schedules, notifications of residence...). Each method has constraints--the second, in particular, because of depending on the availability of administrative data: job changes are never registered and in France, neither are residential moves. That limits observation to the information collected when a census is held or a vital event registered. [The authors] test here the validity of these incomplete data by comparing them to information supplied by an ad hoc survey."

Data Collection↗
PubMed1998

Predictors of cessation of marijuana use: an event history analysis.

Event history analysis was applied to monthly life and drug histories of a representative community sample of 706 marijuana users, followed from ages 15-16 to 34-35, to investigate factors associated with cessation of marijuana use from adolescence to adulthood. In addition to age and gender, the most important determinants of cessation are the phenomenology of marijuana use, social role participation, depressive symptoms and deviance. Frequent users, those who started using early and those who use illicit drugs other than marijuana are more likely to continue their marijuana use. Using marijuana for social reasons accelerates cessation, using to change one's mood reduces cessation. Becoming pregnant and a parent is the most important social role leading to marijuana cessation for women. There is also a very important experimental effect of the interview itself on the reported timing of a cessation. The effect of a social context favorable to marijuana use appears to reflect selection rather than social influence.

Adolescent↗
PubMed1991

Discrete-time event history analysis using segmented hazards.

Event history analysis is a means of explaining variation in the timing of events in individual life histories. This article describes methods for overcoming two difficult problems likely to be encountered in applications of event history analysis to studies of aging and human development. First, in many studies the ages of occurrence of critical life events are recorded in discrete units such as years, but the probability distributions of life events are usually specified in continuous-time form. We show how to estimate models for discrete-time data based on an underlying continuous-time specification. Second, the standard distributions for life events often fail to capture the complex age-dependence seen in actual data. We show how to construct a model using segmented hazards, that is, a composite of different functions for different segments of time. To illustrate these points, we study the age of first intercourse of 11,883 subjects from the National Longitudinal Study of Youth.

Adolescent↗
PubMed2002

Multi-state models for event history analysis.

An introduction to event history analysis via multi-state models in given. Examples include the two-state model for survival analysis, the competing risks and illness-death models, and models for bone marrow transplantation. Statistical model specification via transition intensities and likelihood inference is introduced. Consequences of observational patterns are discussed, and a real example concerning mortality and bleeding episodes in a liver cirrhosis trial is discussed.

Biometry↗
PubMed1993

[New perspectives in life-event history analysis].

"In the last decade, life-event history analysis, also called failure time data analysis or survival analysis, has been widely adopted by demographers. This methodology in demography allows [us] to overcome major hurdles especially when analyzing longitudinal survey data. This paper describes the new perspectives opened to research in that field, and is illustrated by new results and examples of research projects. The authors concentrate on four issues: the analysis of incorrect and imperfect data, the analysis based on more complex data and lastly the study of interaction between phenomena." (SUMMARY IN ENG AND SPA)

Demography↗
PubMed1999

Modelling and exploring human sleep with event history analysis.

In this paper we propose the use of statistical models of event history analysis for investigating human sleep. These models provide appropriate tools for statistical evaluation when sleep data are recorded continuously over time or on a fine time grid, and are classified into sleep stages such as REM and nonREM as defined by Rechtschaffen and Kales (1968). In contrast to conventional statistical procedures, event history analysis makes full use of the information contained in sleep data, and can therefore provide new insights into non-stationary properties of sleep. Probabilities of or intensities for transitions between sleep stages are the basic quantities for characterising sleep processes. The statistical methods of event history analysis aim at modelling and estimating these intensities as functions of time, taking into account individual sleep history and assessing the influence of factors of interest, such as hormonal secretion. In this study we suggest the use of non-parametric approaches to reveal unknown functional forms of transition intensities and to explore time-varying and non-stationary effects. We then apply these techniques in a study of 30 healthy male volunteers to assess the mean population intensity and the effects of plasma cortisol concentration on the transition between selected sleep stages as well as the influence of elapsed time in a current REM period on the intensity for a transition to nonREM. The most interesting findings are that (a) the intensity of the nonREM-to-REM transitions after sleep onset in young men shows a periodicity which is similar to that of nonREM/REM cycles; (b) 30-45 min after sleep onset, young men reveal a great propensity to pass from light sleep (stages 1 or 2) into slow-wave sleep (SWS) (stages 3 or 4); (c) high cortisol levels imposed additional impulses on the transition intensity of (i) wake to sleep around 2 h after sleep onset, (ii) nonREM to REM around 6 h later, (iii) stage 1 or stage 2 sleep to SWS around 2, 4 and 6 h later and (iv) SWS to stage 1 or stage 2 sleep about 2 h later. Moreover, high cortisol concentrations at the beginning of REM periods favoured the change to nonREM sleep, whereas later their influence on a nonREM change became weak and weaker. As sleep data are also available as event-oriented data in many studies in sleep research, event history analysis applied additionally to conventional statistical procedures, such as regression analysis or analysis of variance, could help to acquire more information and knowledge about the mechanisms behind the sleep process.

Adult↗
PubMed1987

Dynamic models for the maintenance of smoking cessation: event history analysis of late relapse.

This paper presents event history analysis as an approach to examining the dynamic nature of the maintenance of smoking cessation. Data from a 1-year follow-up of 172 adult male and 209 adult female ex-smokers is used to estimate the rate at which individuals relapse and return to abstinence. Results indicate that the rate of relapse in both males and females (3.9 and 3.6% per month, respectively) is roughly half that of the rate of return to abstinence (7.5 and 6.3% per month, respectively). No evidence was found for a "safe point" during the observation interval. Individual characteristics that affect the rate at which ex-smokers relapse and return to abstinence are identified. Characteristics that influenced the rate of return to abstinence were completely different from those affecting the rate of relapse, a finding that suggests covariate asymmetry. Implications of dynamic analysis for conceptualizations of maintenance are discussed.

Behavior Therapy↗
PubMed1986

Demographic event history analysis: a selective review.

This is a selective review of the literature concerning demographic event history analysis. "We have attempted to emphasize work that we consider to be particularly important or innovative, to note some of the difficulties that may arise with the use of event history analysis, and to point to several substantive areas where research is still poorly developed."

Data Collection↗
PubMed2006

Event history analysis and the cross-section.

Examples are given of problems in event history analysis, where several time origins (generating calendar time, age, disease duration, time on study, etc.) are considered simultaneously. The focus is on complex sampling patterns generated around a cross-section. A basic tool is the Lexis diagram.

Adult↗
PubMedDate not supplied

Event history analysis and inference from observational epidemiology.

Systematic inclusion of time in observational epidemiological studies may help strengthen the inference to be drawn, but new epidemiological challenges arise, such as time-dependent confounders - covariates which may change from being confounders to being intermediate variables. The focus of this presentation concerns two sets of tools: event history analysis and structural nested failure time models, both applied to a particularly intricate problem in observational epidemiology, of empirically assessing the graft-versus-leukaemia effect after bone marrow transplantation.

Bone Marrow Transplantation↗
PubMed1997

Adjusting for attrition in event-history analysis.

"This paper will investigate the issue of weighting for panel attrition in event-history models by comparing alternative treatments of sampling weights in a divorce model for members of the 1986 [U.S.] Survey of Income and Program Participation (SIPP). Three distinct weighting procedures are compared. These are based on (1) the initial selection probability weights; (2) the 1986 SIPP panel weights; and (3) the monthly attrition-adjusted weights. The paper also compares these weighted estimates with the estimates of a structural model in which attrition is treated as an error-correlated competing alternative to divorce." The results indicate that in many instances divorces in the SIPP end up being recorded as attrition.

Americas↗
PubMed1993

Event-history analysis for left-truncated data.

"This paper aims at providing a practical guidance for coping with social science event-history data that are left-truncated, especially when the length of exposure prior to observation is known. Only the case of single events is treated, although much of the discussion should be applicable to the case of repeated events, in which only the first spell is likely to be left-truncated. Marital dissolution data from the Panel Study of Income Dynamics (PSID) are used as an example...."

Americas↗
PubMed2004

An event history analysis of recurrent child maltreatment reports in Florida.

OBJECTIVE: The purpose of this study was to (a) describe the timing of maltreatment recurrence and (b) measure associations between child demographics and characteristics of initial reports with recurrent maltreatment. METHOD: Using administrative data from the Florida Department of Children and Families, case histories of 189,375 children with an initial maltreatment report in 1998 or 1999 were examined using event history techniques, also known as survival analysis, to assess the relationship between predictor variables and the likelihood and timing of recurrence. Specifically, data were analyzed using descriptive and multivariate analytic methods, including life-table analysis and logistic regression analysis. RESULTS: Over 26% of the sample had a maltreatment recurrence during the 2-year follow-up period, and the risk of recurrence was greatest in the first 4 months after the initial incident. Results also suggest the presence of unique associations between recurrence and a child's race/ethnicity and age, as well as indication level, maltreatment type, and service disposition as specified in the initial report. CONCLUSIONS: Results indicate that recurrent reports are more likely for young and White children whose first report resulted in a substantiation of neglect and the provision of in-home preventive services. This research highlights the importance of further investigating the relationship between chronic maltreatment and other influential variables.

Adolescent↗
PubMed2006

Behavioral trajectories as predictors in event history analysis: male calling behavior forecasts medfly longevity.

A recent study on wild male Mediterranean fruit flies [Papadopoulos, N.T., Katsoyannos, B.I., Kouloussis, N.A., Carey, J.R., Müller, H.-G., Zhang, Y., 2004. High sexual signalling rates of young individuals predict extended life span in male Mediterranean fruit flies. Oecologia 138, 127-134] provided evidence that intense sexual signalling (calling behavior) is associated with longer life span. We demonstrate here an approach based on functional data analysis methodology for predicting individual remaining longevity and the distribution of remaining lifetime from individual behavioral trajectories. A key methodological concept is the time evolution of mean functions and eigenfunctions. This methodology is applied to the event history of calling behavior of male medflies. The results demonstrate complex relationships between male calling behavior and subsequent longevity that complement previous biodemographic analyses of these data. A high level of recent calling activity is found to be associated with increased remaining lifetime for an individual male fly, while calling activity at early ages plays no role for remaining longevity.

Animals↗
PubMed2003

How important are parents and partners for smoking cessation in adulthood? An event history analysis.

BACKGROUND: The aim of this study is to assess the effect of parental and partner's education and smoking behavior on an individual's chance of smoking cessation over the life course. METHODS: Self-reported life histories of smoking behavior, education, and relationships were recorded in face-to-face interviews with a random general-population sample of 850 respondents and their partners (if present). The data were collected in 2000. A discrete-time event history model is applied in the analyses of cessation over the life course. RESULTS: Parents' education and smoking behavior (during adolescence) and partners' education have no significant influence on cessation. Living with an ex-smoker or never-smoker increases the likelihood of quitting, compared to being single or living with a partner who smokes. Respondents whose partners were ex-smokers are almost five times more likely to quit smoking than single respondents. They are almost twice as likely to quit compared to those living with a never-smoker. CONCLUSIONS: The difference between having and not having a partner seems as important for cessation as the difference between having a partner who smokes, has never smoked, or has stopped smoking. An ex-smoking partner stimulates cessation more than a partner who has never smoked. Studies into cessation should take into account partners' smoking histories.

Adult↗