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Published on: February 19, 2021
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Understanding the unique patient-causal singularism and patient reported outcomes
1Health Outcomes Insights Ltd, Oxfordshire, UK. keith@healthoutinsights.com.
Summary
This study proposes a novel mixed-methods approach combining patient-reported outcome measures (PROMs) and qualitative data. This method enhances causal inference in research by analyzing individual patient experiences, moving beyond group-level quantitative findings.
Area of Science:
- Clinical Research Methodology
- Qualitative and Mixed-Methods Research
- Health Services Research
Background:
- Patient-reported outcome measures (PROMs) are crucial in randomized control trials (RCTs) for assessing treatment efficacy and safety.
- Current quantitative PROM data primarily offers aggregated group-level insights, limiting understanding of individual patient experiences.
- Qualitative case-study research, while rich in individual experience, traditionally lacks the robustness for causal inference.
Purpose of the Study:
- To propose a novel single mixed-methods case analysis framework.
- To integrate quantitative PROM data with qualitative narrative data for individual patient analysis.
- To advance the concept of 'Causal singularism' for robust causal inference in individual case studies.
Main Methods:
- Development of a proposed single mixed-methods case analysis design.
- Integration of quantitative data from Patient-Reported Outcome Measures (PROMs).
- Incorporation of qualitative narrative data to capture individual patient experiences.
Main Results:
- The proposed method facilitates a detailed analysis of individual patient treatment experiences.
- It bridges the gap between quantitative group-level data and qualitative individual insights.
- The framework supports the application of causal inference principles to single-case analyses.
Conclusions:
- A mixed-methods approach combining PROMs and narrative data can yield robust insights into individual patient experiences.
- This methodology enhances the potential for causal inference in single-case research.
- The proposed 'Causal singularism' framework offers a new avenue for understanding treatment effects at the individual level.
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