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  • 1. Simulated Car Crashes and Crash Predictors in Drivers with Parkinson’s Disease Qian Shi, Xia Mao, Zugui Zhang, Minggen Lu
  • 2. Background
    • Parkinson’s disease (PD)
    • - have many kinds of cognitive and visual impairments
    • - can alter the abilities on which safe driving depends.
    • Research on relationship of car accidents and neurological diseases
    • - interested in estimating the risk of car crashes for drivers with and without neurological diseases
    • - mainly rely on results from driving simulations
  • 3. Background
    • Iowa Driving Simulator (IDS)
  • 4. Objectives
    • Primary goals:
    • To estimate the risk (probability) of simulated car crashes for drivers with PD, as well as that for drivers without neurological diseases.
    • To test the hypothesis that older drivers with mild to moderate PD are at greater risk for simulated car crashes than control participants of similar ages.
  • 5. Objectives
    • Secondary goals:
    • To determine how such crashes are predicted by visual/cognitive measurements.
    • To compare the estimates of significant predictors obtained by Bayesian model and those obtained by frequentist method.
  • 6. Data
    • Subjects : 24 participants with PD (age: 66.58 10.31) and 70 participants without dementia (age: 68.59 6.24)
    • Experiments : all participants drove in the same simulated environments with high-fidelity collision avoidance scenarios and were tested on the same batteries of cognitive and visual tasks.
    • Outcome : counts of simulated car crashes within two groups.
    • Main covariates : age; education level; visual and cognitive measurements.
  • 7. Methods - First phase
    • Step 1: Determine a “best” transformation, and obtain estimates of simulated car cash risks and crude OR for the two groups.
    • Likelihood:
    • Crash[i] ~ dbern(p[i])
    • Transformation(p[i])= alpha + beta×group[i]
    • Prior:
    • Alpha ~ dflat()
    • Beta ~ dflat()
  • 8. Methods - First phase
    • Step 2: Assess the association of simulated car crash risk and Parkinson’s disease status, after adjusting for age, gender and the education level.
    • Association between covariates and response and predictor variables.
    • Likelihood:
    • Crash[i] ~ dbern(p[i])
    • logit(p[i])= alpha +×group[i]+beta.age×(age[i]-mean(age[])
    • Prior:
    • Alpha ~ dflat() ~ dflat() Beta.age ~dflat()
  • 9. Methods - Second phase
    • Step 1: Determine significant predictors by stepwise selection with logistic regression in SAS .
    • Step 2: Fit a frequentist multivariate logistic regression model including the significant predictors.
    • Step 3: Fit a Bayesian model including the significant predictors
  • 10. Results - First phase
    • Comparison of three transformations:
    • Convergence is satisfied well for all three transformations.
    • DICs are very similar (logit:124.297, Probit:124.273 , Cloglog:124.260 ).
    • For ease of interpretation, we chose logit transformation to do subsequent analysis.
    • * Estimates are based on MCMC 1001-5000 iterations. Point estimates for OR’s are the medians.
  • 11. Results - First phase
    • Comparison of simulated car crash risk for the two groups
    • Estimates of OR of car crash for the two groups
    • * Estimates are based on MCMC 3001-10000 iterations.
  • 12. Results - Second phase
    • Selection of significant predictors.
    • Recall — 30 minutes delay score for Rey Auditory Verbal Learning Test, which is a rigorous measure of anterograde verbal memory.
    • CS – Contrast sensitivity (CS) is assessed using the Pelli-Robson chart. This test provides a measure of low to medium spatial frequency sensitivity.
  • 13. Results - Second phase
    • Comparison of frequentist method and Bayesian method
    • Frequentist’s estimates of OR based on multivariate logistic regression model.
    • Bayesian Estimates of OR based on MCMC 2001-10000 iterations .
  • 14. Results
    • Example plots of convergence diagnoses
  • 15. Conclusions
    • The risk of simulated car crash for Parkinson’s patient is 79.16%, with a 95% credible set of (61.4%, 92.53%).The risk for the control group is 57.02%, with a 95% credible set of (45.16%, 68.61%).
    • Old drivers with mild to moderate PD are at greater risk for simulated car crashes than control participants of similar ages. (OR=2.989, 95% credible set=(1.059, 10.57))
  • 16. Conclusions
    • Anterograde verbal memory (recall) and contrast sensitivity are significant predictors of car crashes for people of these ages.
    • Frequentist method and Bayesian method based on non-informative priors yield similar point estimates of OR for Recall and CS. The Bayesian 95% credible set for CS is slightly shorter than frequentist 95% confidence interval for CS.
  • 17.
    • Questions
    • and
    • Comments