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Assessing the representativeness of large medical data using population stability index
Sheng-Chieh Lu1,2, Wenye Song3, Andre Pfob4,5
1Department of Symptom Research, The University of Texas MD Anderson Cancer Center, 6565 MD Anderson Blvd, Houston, TX, 77030, USA. slu4@mdanderson.org.
BMC Medical Research Methodology
|February 21, 2025
Summary
Population stability index (PSI) effectively detects sample differences in aggregated health data, offering a reliable method for epidemiological research. This approach enhances confidence in the representativeness of study findings for the general population.
Area of Science:
- Epidemiology
- Biostatistics
- Health Informatics
Background:
- Sample representativeness is crucial for epidemiological research validity.
- Traditional methods require raw data, often unavailable for large or aggregated datasets.
- Population stability index (PSI) is a metric used to assess data drift in AI.
Purpose of the Study:
- To evaluate the capability of PSI in detecting sample differences using aggregated, population-based cancer data.
- To compare PSI's performance against traditional statistical tests like Chi-Square and Cramér's V.
Main Methods:
- Utilized United States cancer statistics from the SEER database (2000, 2015-2020).
- Calculated PSI scores to assess yearly data distribution shifts for age, sex, and cancer site.
- Compared PSI results with Chi-Square and Cramér's V tests.
Main Results:
- PSI scores varied significantly, indicating moderate to substantial differences in cancer population characteristics between 2000 and later years.
- Chi-Square tests, despite significance, showed potential for Type-I errors due to large sample sizes.
- PSI demonstrated effectiveness in identifying shifts even with small effect sizes detected by other metrics.
Conclusions:
- PSI is a viable tool for assessing sample differences in healthcare studies with aggregated or large datasets.
- PSI can improve confidence in the representativeness of epidemiological findings.
- Incorporating PSI enhances the reliability of research applied to broader populations.
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