Predicting Cancer Mortality: Developing a New Cancer Care Variable Using Mixed Methods and the Quasi‐Statistical Approach

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.

Achieving Integration in Mixed Methods Designs—Principles and Practices

“…Zickmund and colleagues used qualitatively elicited patient views of self transformed to a numerical variable, and mortality data to conduct hierarchical multivariable logistical modeling (Zickmund et al. ).…”

“…Merging in mixed methods goes beyond content analysis by comparing the transformed qualitative data with a quantitative database. Zickmund and colleagues used qualitatively elicited patient views of self transformed to a numerical variable, and mortality data to conduct hierarchical multivariable logistical modeling (Zickmund et al 2013).…”

Using a Whole Person Approach to Support People With Cancer

“… 14 , 15 It is the combination of these data that facilitates deeper understanding of how to improve service provision and patient outcomes. 16 - 18 …”

“…14,15 It is the combination of these data that facilitates deeper understanding of how to improve service provision and patient outcomes. [16][17][18] Internationally, there is increasing evaluation of the shortterm outcomes of the models of supportive and integrative oncology. [19][20][21][22][23][24][25][26][27] Few of these evaluations, however, extend to understand the outcomes and experiences of clients and patients over an extended period, such as 1 year or more, or the longer-term implications once they have stopped using these services.…”

Integrating Mixed Methods in Health Services and Delivery System Research

“…Zickmund et al. () provide a richly documented example of integration through data transformation using a mixed methods convergent design. By carefully transforming semistructured interviews of cancer patients into numerical values, they identify the importance of self‐view in cancer care and present results as a narrative in outcomes of care.…”