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Predicting cancer mortality: Developing a new cancer care variable using mixed methods and the quasi-statistical
Susan L Zickmund1, Suzanne Yang, Edward P Mulvey
1Center 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.
Health Services Research
|October 22, 2013
Summary
Analyzing qualitative data numerically revealed that an improved view of self significantly lowers cancer mortality rates. This demonstrates the value of integrating qualitative insights into quantitative health services research.
Area of Science:
- Health Services Research
- Psychosocial Oncology
- Mixed Methods Research
Background:
- Qualitative data often provides rich insights but is challenging to integrate into quantitative analyses.
- Cancer patient data, including demographics and mortality, are available for longitudinal study.
Purpose of the Study:
- To demonstrate the value of a variable derived from qualitative analysis in subsequent quantitative analyses.
- To explore the relationship between patients' view of self and cancer mortality outcomes.
Main Methods:
- A mixed methods design was employed with 909 cancer patients.
- Qualitative data from open-ended responses regarding patients' view of self were coded into a numerical variable.
- This variable was integrated with demographic data and 10-year mortality outcomes from the Social Security Death Index.
Main Results:
- An improved view of self was significantly associated with a lower mortality rate.
- This association remained significant after adjusting for age, gender, and cancer stage.
Conclusions:
- Statistical analysis of qualitative data is feasible and can identify novel predictors of cancer mortality.
- Coded qualitative variables hold potential for identifying new health service implications and predicting key healthcare outcomes.
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