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    ZuguiZhang.ppt ZuguiZhang.ppt Presentation Transcript

    • Simulated Car Crashes and Crash Predictors in Drivers with Parkinson’s Disease Qian Shi, Xia Mao, Zugui Zhang, Minggen Lu
    • 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
    • Background
      • Iowa Driving Simulator (IDS)
    • 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.
    • 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.
    • 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.
    • 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()
    • 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 + beta.group×group[i]+beta.age×(age[i]-mean(age[])
      • Prior:
      • Alpha ~ dflat() Beta.group ~ dflat() Beta.age ~dflat()
    • 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
    • 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.
    • 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.
    • 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.
    • 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 .
    • Results
      • Example plots of convergence diagnoses
    • 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))
    • 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.
      • Questions
      • and
      • Comments