Statistical Analysis of Hip Hypermobility and Psychosocial Outcomes

Statistical Analysis of Hip Hypermobility and Psychosocial Outcomes

Project Overview

This project investigates whether psychosocial factors such as pain catastrophizing, pain interference, and pain self-efficacy predict hip hypermobility (Beighton Score) in a clinical population. Leveraging robust statistical methods and reproducible analytics, the analysis provided actionable findings for clinicians and researchers. Using both multiple linear regression and multiple imputation, we found:

  • Key predictors of hypermobility are age and gender, not psychosocial scores
  • Younger females tend to have higher hypermobility
  • Pain and self-efficacy scores showed little/no association with Beighton Score

Methodology

  • Data: 445 patients from a Baylor Scott & White hip clinic with Beighton Score, age, gender, and three psychosocial surveys (PCS, PROMIS-PI, PSEQ)
  • Outcome: Beighton Score (continuous; dichotomized for logistic regression)
  • Predictors: Age, gender, pain catastrophizing (PCS), pain interference (PROMIS-PI), pain self-efficacy (PSEQ)
  • Handling Missing Data: Used Multiple Imputation by Chained Equations (MICE) to address 47-64% missingness in psychosocial variables
  • Statistical Analysis:
    • Multiple Linear Regression (MLR) for continuous Beighton Score
    • Post-hoc Logistic Regression for dichotomized outcome (>=4 vs <4)
    • Age and gender included as covariates; all models checked for multicollinearity and validated via diagnostic plots

Key Results

  • Age and gender were the only significant predictors:
    • Younger females exhibited higher Beighton Scores (males scored ~3.7 points lower, age slope ~-0.06 per year)
  • Psychosocial outcomes: No significant association found in either linear or logistic regression, regardless of missing data strategy
  • Model diagnostics: No problematic multicollinearity; regression assumptions verified- No significant association found between psychosocial measures (PCS, PROMIS-PI, PSEQ) and hypermobility, regardless of imputation method

Tools & Technologies

  • Languages: R
  • Libraries: mice, broom, ggplot2, modelsummary

Visualizations & Report

Interactive Dashboard

Explore all results and rerun analyses with new data:

Further Reading

Conclusion & Next Steps

  • Psychosocial screening did not enhance risk assessment for hypermobility in this clinical sample.
  • Future research: Apply methods to general-population data to clarify external validity; explore additional biopsychosocial predictors.