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Screening for data clustering in multicenter studies: the residual intraclass correlation.

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This study introduces a method to measure data clustering in multicenter studies, identifying physicians with outlying measurements. The residual intraclass correlation (RICC) quantifies clustering, aiding data quality monitoring and variable selection for prediction models.

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Area of Science:

  • Multicenter study design
  • Statistical modeling in healthcare research
  • Data quality assessment

Background:

  • Multicenter studies face challenges with center-specific variations due to equipment, protocol deviations, and patient population differences.
  • These variations can impact measurement reliability and introduce bias in research findings.
  • Identifying and quantifying such clustering is crucial for accurate data analysis.

Purpose of the Study:

  • To develop and present a statistical method for measuring the degree of data clustering in multicenter studies.
  • To identify variables significantly influenced by physician-level variations.
  • To detect physicians exhibiting outlying measurement patterns.

Main Methods:

  • Utilized regression models with fixed effects for patient case-mix adjustment and random cluster intercepts to assess physician-level clustering.
  • Proposed the residual intraclass correlation (RICC) to quantify the proportion of residual variance attributable to clusters.
  • Applied the RICC and R2 metrics to a dataset of 2407 patients undergoing ovarian tumor diagnosis, examining 18 tumor characteristics, 4 patient characteristics, and CA-125 levels.

Main Results:

  • Demonstrated significant variation in RICC across variables, ranging from 2.2% for age to 25.1% for fluid in the pouch of Douglas.
  • Identified seven variables with RICC exceeding 15%, indicating substantial systematic differences at the physician level.
  • Found that accounting for ultrasound machine quality reduced RICC for certain blood flow measurements.

Conclusions:

  • Recommended addressing data clustering during the monitoring and analysis phases of multicenter studies.
  • Highlighted the residual intraclass correlation (RICC) as an effective percentage-based metric for quantifying clustering.
  • Suggested RICC as a valuable tool for data quality monitoring and pre-model variable screening.