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IMPACT OF MEASUREMENT SCALES AND MISSING DATA STRATEGIES ON CREATININE BASED EVALUATION IN KIDNEY TRANSPLANT RECIPIENTS
Keywords:
GEE, longitudinal, missing, models, scales, imputationDOI:
https://doi.org/10.17654/0973514325031Abstract
Selecting appropriate measurement scales and data formats is crucial for accurate analysis and interpretation of clinical research data. This study evaluated the impact of different measurement scales and data formats on descriptive statistics and comparative analyses using a real medical dataset of kidney transplant recipients. Creatinine measurements from 112 kidney transplant recipients were analyzed using four different outcome formats: longitudinal continuous, univariate continuous, longitudinal categorical, and univariate categorical. Descriptive statistics, comparison tests, and modeling techniques were applied to each outcome format. Four approaches for handling missing data were also compared. Continuous outcomes were more effective in detecting group differences and predictor effects compared to categorical outcomes. Categorical scales were useful for identifying high-risk groups. Missing data affected model results, with complete case analysis and multiple imputations often performing best. Treatment effects on creatinine outcomes varied depending on the measurement approach. Sex, age group, and graft loss status were significant predictors in most models. The choice of measurement scale and data format significantly influenced statistical results and clinical interpretations in the analysis of creatinine biomarkers in kidney transplant patients.
Received: July 29, 2025
Revised: September 24, 2025
Accepted: October 10, 2025
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