2.50
Hdl Handle:
http://hdl.handle.net/10755/165514
Category:
Abstract
Type:
Presentation
Title:
AN ANALYTIC STRATEGY FOR MEASURING AND MODELING CANCER SYMPTOMS
Author(s):
Doorenbos, Ardith; Given, Barbara; Given, Charles; Virbitsky, Natalya
Author Details:
Ardith Doorenbos, PhD, RN, Michigan State University, East Lansing, Michigan, USA; Barbara Given, PhD, RN, FAAN; Charles Given, PhD; Natalya Virbitsky, PhD(c)
Abstract:
Purpose: To propose an analytically based strategy for measuring and modeling cancer symptoms. Description of the Methodological Issue: Research on cancer symptoms has focused on single symptoms, a summary score of a number of symptoms, and more recently on symptom clusters. However, the research literature does not provide clear direction for determining which approach is preferable, or how to incorporate these symptom measures into the context of multilevel longitudinal studies. Theoretical Framework: Item response theory guided this methodological enquiry. It postulates that characteristics of symptoms, such as symptom intractability, interact with a person’s characteristics or condition to determine susceptibility to a given symptom. Methods: The analyses used data amalgamated from three different descriptive cross-sectional studies. This data set consists of 21 symptoms reported across time by 1,389 individuals with cancer. Analysis: A three level Hierarchical Linear Model (HLM) was used. Level-1 is the item response model which consists of symptom presence. Level-2 is the trajectory of each individual representing change over time of symptoms within person, and the person related variables that change over time such as depression and activities of daily living. Level-3 explains that trajectory via person-specific characteristics, such as age, cancer site, and gender. Implications and Recommendations: The combined item response and hierarchical linear models extends the usual item response model allowing for multiple symptoms to be measured and examined rather than single symptom, or a symptom summary score. Additionally, it provides information on how symptoms group with one another. Significance: Item response models can be used as a means to organize symptoms as a multivariate dependent variable. Embedding an item response model in a hierarchical linear model allows researchers to treat symptoms as a multivariate outcome at higher levels. This permits examination of symptoms both longitudinally and at the individual level.
Repository Posting Date:
27-Oct-2011
Date of Publication:
27-Oct-2011
Conference Date:
2005
Conference Name:
30th Annual Oncology Nursing Society Congress
Conference Host:
Oncology Nursing Society
Conference Location:
Orlando, Florida, USA
Note:
This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.

Full metadata record

DC FieldValue Language
dc.type.categoryAbstracten_US
dc.typePresentationen_GB
dc.titleAN ANALYTIC STRATEGY FOR MEASURING AND MODELING CANCER SYMPTOMSen_GB
dc.contributor.authorDoorenbos, Ardithen_US
dc.contributor.authorGiven, Barbaraen_US
dc.contributor.authorGiven, Charlesen_US
dc.contributor.authorVirbitsky, Natalyaen_US
dc.author.detailsArdith Doorenbos, PhD, RN, Michigan State University, East Lansing, Michigan, USA; Barbara Given, PhD, RN, FAAN; Charles Given, PhD; Natalya Virbitsky, PhD(c)en_US
dc.identifier.urihttp://hdl.handle.net/10755/165514-
dc.description.abstractPurpose: To propose an analytically based strategy for measuring and modeling cancer symptoms. Description of the Methodological Issue: Research on cancer symptoms has focused on single symptoms, a summary score of a number of symptoms, and more recently on symptom clusters. However, the research literature does not provide clear direction for determining which approach is preferable, or how to incorporate these symptom measures into the context of multilevel longitudinal studies. Theoretical Framework: Item response theory guided this methodological enquiry. It postulates that characteristics of symptoms, such as symptom intractability, interact with a person’s characteristics or condition to determine susceptibility to a given symptom. Methods: The analyses used data amalgamated from three different descriptive cross-sectional studies. This data set consists of 21 symptoms reported across time by 1,389 individuals with cancer. Analysis: A three level Hierarchical Linear Model (HLM) was used. Level-1 is the item response model which consists of symptom presence. Level-2 is the trajectory of each individual representing change over time of symptoms within person, and the person related variables that change over time such as depression and activities of daily living. Level-3 explains that trajectory via person-specific characteristics, such as age, cancer site, and gender. Implications and Recommendations: The combined item response and hierarchical linear models extends the usual item response model allowing for multiple symptoms to be measured and examined rather than single symptom, or a symptom summary score. Additionally, it provides information on how symptoms group with one another. Significance: Item response models can be used as a means to organize symptoms as a multivariate dependent variable. Embedding an item response model in a hierarchical linear model allows researchers to treat symptoms as a multivariate outcome at higher levels. This permits examination of symptoms both longitudinally and at the individual level.en_GB
dc.date.available2011-10-27T12:20:01Z-
dc.date.issued2011-10-27en_GB
dc.date.accessioned2011-10-27T12:20:01Z-
dc.conference.date2005en_US
dc.conference.name30th Annual Oncology Nursing Society Congressen_US
dc.conference.hostOncology Nursing Societyen_US
dc.conference.locationOrlando, Florida, USAen_US
dc.description.noteThis is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.-
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