Simulated Car Crashes and Crash Predictors in Drivers with Parkinsons Disease

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Simulated Car Crashes and Crash Predictors in
Drivers with Parkinsons Disease

• Qian Shi, Xia Mao,
• Zugui Zhang, Minggen Lu

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Background

• Parkinsons 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

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Background

• Iowa Driving Simulator (IDS)

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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.

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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.

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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.

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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
• Crashi dbern(pi)
• Transformation(pi) alpha betagroupi
• Prior
• Alpha dflat()
• Beta dflat()

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Methods - First phase

• Step 2 Assess the association of simulated car
  crash risk and Parkinsons disease status, after
  adjusting for age, gender and the education
  level.
• Association between covariates and response and
  predictor variables.
• Likelihood
• Crashi dbern(pi)
• logit(pi) alpha beta.groupgroupibeta.age
  (agei-mean(age)
• Prior
• Alpha dflat() Beta.group dflat()
  Beta.age dflat()

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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

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Results - First phase

• Comparison of three transformations
• Convergence is satisfied well for all three
  transformations.
• DICs are very similar (logit124.297,
  Probit124.273 , Cloglog124.260 ).
• For ease of interpretation, we chose logit
  transformation to do subsequent analysis.
• Estimates are based on MCMC 1001-5000
  iterations. Point estimates for ORs are the
  medians.

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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.

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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.

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Results - Second phase

• Comparison of frequentist method and Bayesian
  method
• Frequentists estimates of OR based on
  multivariate logistic regression model.
• Bayesian Estimates of OR based on MCMC
  2001-10000 iterations.

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Results

• Example plots of convergence diagnoses

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Conclusions

• The risk of simulated car crash for Parkinsons
  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. (OR2.989,
  95 credible set(1.059, 10.57))

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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.

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• Questions
• and
• Comments