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Predicting cancer mortality: Developing a new cancer care variable using mixed methods and the quasi-statistical approach. | LitMetric

Predicting cancer mortality: Developing a new cancer care variable using mixed methods and the quasi-statistical approach.

Health Serv Res

Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA.

Published: December 2013

AI Article Synopsis

  • - The study aimed to show how qualitative data can enhance quantitative analyses, particularly in relation to cancer patient mortality outcomes.
  • - Researchers used a mix of qualitative and quantitative methods on 909 cancer patients, collecting demographic info and personal views to create a key variable related to self-perception.
  • - Results indicated that patients with a more positive view of themselves had lower mortality rates, suggesting that qualitative insights can reveal important predictors for health outcomes in cancer care.

Article Abstract

Objective: To demonstrate the value of using a variable derived from qualitative analysis in subsequent quantitative analyses.

Data Sources/study Setting: Mixed methods data were combined with 10-year mortality outcomes. Participants with cancer were recruited from services at a large teaching hospital, and mortality data were from the Social Security Death Index.

Study Design: An observational concurrent or convergent mixed methods design was used to collect demographics and structured ratings along with qualitative data from 909 cancer patients at baseline.

Data Collection/extraction Methods: Coding rules for qualitative data were defined for open-ended responses from cancer participants speaking about their view of self, and a variable was numerically coded for each case. Mortality outcomes were matched to baseline data, including the view of self variable.

Principal Findings: Individuals with an improved view of self had a significantly lower mortality rate than those for whom it was worse or unchanged, even when adjusting for age, gender, and cancer stage.

Conclusions: Statistical analysis of qualitative data is feasible and can identify new predictors with health services' implications associated with cancer mortality. Future studies should consider the value of testing coded qualitative variables in relation with key health care outcomes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4097837PMC
http://dx.doi.org/10.1111/1475-6773.12116DOI Listing

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