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