Comparing a Distributed Parameter Model-Based System Identification Technique with More Conventional Methods for Inverse Problems

Computation of nonparametric, mixed effects, maximum likelihood, biosensor data based-estimators for the distributions of random parameters in an abstract parabolic model for the transdermal transport of alcohol

The existence and consistency of a maximum likelihood estimator for the joint probability distribution of random parameters in discrete-time abstract parabolic systems was established by taking a nonparametric approach in the context of a mixed effects statistical model using a Prohorov metric framework on a set of feasible measures. A theoretical convergence result for a finite dimensional approximation scheme for computing the maximum likelihood estimator was also established and the efficacy of the approach was demonstrated by applying the scheme to the transdermal transport of alcohol modeled by a random parabolic partial differential equation (PDE). Numerical studies included show that the maximum likelihood estimator is statistically consistent, demonstrated by the convergence of the estimated distribution to the "true" distribution in an example involving simulated data. The algorithm developed was then applied to two datasets collected using two different transdermal alcohol biosensors. Using the leave-one-out cross-validation (LOOCV) method, we found an estimate for the distribution of the random parameters based on a training set. The input from a test drinking episode was then used to quantify the uncertainty propagated from the random parameters to the output of the model in the form of a 95 error band surrounding the estimated output signal.

Validation of transdermal alcohol concentration data collected using wearable alcohol monitors: A systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of transdermal alcohol content (TAC) data (i.e. index test) collected with wearable alcohol monitors for assessment of alcohol use or any other alcohol related outcome (e.g., excessive alcohol use) among adults 18 and older.

METHODS

We will systematically search MEDLINE, EMBASE, PsycINFO, and the Social Sciences Citation Index (SSCI, Web of Science) for TAC validation studies. The reference standards for this systematic review study are alcohol use data collected through self-reports, breathalyzers, or blood samples. If enough studies are available to conduct a meta-analysis, we will use a hierarchical regression approach to pool the results and obtain summary point estimates.