Application of Survival Analysis to Examine Outcomes in a Multi-Site Rural Intervention Research Study

2.50
Hdl Handle:
http://hdl.handle.net/10755/152748
Type:
Presentation
Title:
Application of Survival Analysis to Examine Outcomes in a Multi-Site Rural Intervention Research Study
Abstract:
Application of Survival Analysis to Examine Outcomes in a Multi-Site Rural Intervention Research Study
Conference Sponsor:Sigma Theta Tau International
Conference Year:2004
Conference Date:July 22-24, 2004
Author:Wilhelm, Susan, RN, PhD
P.I. Institution Name:University of Nebraska
Title:Postdoctoral Student
Purpose: The purpose of this presentation is to illustrate the use of survival analysis as a strategy to address problems associated with missing (censored) data. Definition of Concept: Survival analysis originated from demographic and medical research that investigated time from treatment until death. However, it is applicable to areas other than mortality. For example, survival analysis has been used in studies to determine time taken to exercise to maximum tolerance. Theory: Markov’s theory, which formed the basis of survival analysis, is concerned with events that are conditional upon those that precede them and affect those that come afterwards. This concept was applied to the analysis of data in the social sciences. Graphical representations of survival (or failure) rate as a function of time provide richer descriptive information, such as identification of times of highest risk, than do simple analyses of mean or median times to occurrence. Differences between groups in terms of their survival functions can also be tested. Additional techniques are available for building and testing models to predict survival from other variables. All methods accommodate censored data. Application: Several approaches to survivor analysis were illustrated using data from an intervention study designed to increase the number of days of breastfeeding for first-time mothers. Approximately 17% of the dataset’s 73 cases were censored. Life tables, graphical presentations of survival functions with nonparametric group comparisons, and Cox regression were employed. Conclusions: Maximum data were available for analysis using this statistical analysis technique. Modest-size studies in which the dependent variable is time to occurrence of events may benefit from the use of this technique.
Repository Posting Date:
26-Oct-2011
Date of Publication:
22-Jul-2004
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleApplication of Survival Analysis to Examine Outcomes in a Multi-Site Rural Intervention Research Studyen_GB
dc.identifier.urihttp://hdl.handle.net/10755/152748-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Application of Survival Analysis to Examine Outcomes in a Multi-Site Rural Intervention Research Study</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2004</td></tr><tr class="item-conference-date"><td class="label">Conference Date:</td><td class="value">July 22-24, 2004</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Wilhelm, Susan, RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Nebraska</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Postdoctoral Student</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">slwilhel@unmc.edu</td></tr><tr><td colspan="2" class="item-abstract">Purpose: The purpose of this presentation is to illustrate the use of survival analysis as a strategy to address problems associated with missing (censored) data. Definition of Concept: Survival analysis originated from demographic and medical research that investigated time from treatment until death. However, it is applicable to areas other than mortality. For example, survival analysis has been used in studies to determine time taken to exercise to maximum tolerance. Theory: Markov&rsquo;s theory, which formed the basis of survival analysis, is concerned with events that are conditional upon those that precede them and affect those that come afterwards. This concept was applied to the analysis of data in the social sciences. Graphical representations of survival (or failure) rate as a function of time provide richer descriptive information, such as identification of times of highest risk, than do simple analyses of mean or median times to occurrence. Differences between groups in terms of their survival functions can also be tested. Additional techniques are available for building and testing models to predict survival from other variables. All methods accommodate censored data. Application: Several approaches to survivor analysis were illustrated using data from an intervention study designed to increase the number of days of breastfeeding for first-time mothers. Approximately 17% of the dataset&rsquo;s 73 cases were censored. Life tables, graphical presentations of survival functions with nonparametric group comparisons, and Cox regression were employed. Conclusions: Maximum data were available for analysis using this statistical analysis technique. Modest-size studies in which the dependent variable is time to occurrence of events may benefit from the use of this technique.</td></tr></table>en_GB
dc.date.available2011-10-26T11:48:18Z-
dc.date.issued2004-07-22en_GB
dc.date.accessioned2011-10-26T11:48:18Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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