THESIS DEFENSE ABSTRACT
A
Hierarchical Multivariate Two-Part Model for Profiling Providers' Effects on
Healthcare Charges
John
Robinson,
PhD Candidate, Johns Hopkins Department of Biostatistics
Procedures for analyzing and comparing healthcare providers'
effects on health services delivery and outcomes have been referred to as
provider profiling. In a typical profiling procedure, patient-level responses
are measured for clusters of patients treated by providers that in turn, can be
regarded as statistically exchangeable. Thus, a hierarchical model naturally
represents the structure of the data.
When provider effects on multiple responses are profiled, fitting a multivariate
model, rather than a series of univariate models, can capture associations among
responses at both the provider and patient levels. When responses are in the
form of charges for healthcare services and sampled patients include non-users
of services, charge variables are a mix of zeros and highly-skewed positive
values that present a modeling challenge. For analysis of regressor effects on
charges for a single service, a frequently used approach is a two-part model (Duan,
Manning, Morris, and Newhouse 1983) that combines logistic or probit regression
on any use of the service and linear regression on the log of positive charges
given use of the service.
Here, we extend the two-part model to the case of charges for multiple services,
using a log-linear model and a general multivariate lognormal model, and employ
the resultant multivariate two-part model as the within-provider component of a
hierarchical model. The log-linear likelihood is reparameterized as proposed by
Fitzmaurice and Laird (1993), so that regressor effects on any use of each
service are marginal with respect to any use of other services. The general
multivariate lognormal likelihood is constructed in such a way that variances of
log of positive charges for each service are provider-specific but correlations
between logs of positive charges for different services are uniform across
providers. A data augmentation step is included in the Gibbs sampler used to fit
the hierarchical model, in order to accommodate the fact that values of log of
positive charges are undefined for unused service.
We apply this hierarchical multivariate two-part model to analyze the effects of
primary care physicians on their patients' annual charges for two services,
primary care and specialty care. Along the way, we also demonstrate an approach
for incorporating prior information about the effects of patient morbidity on
response variables, to improve the accuracy of provider profiles that are based
on patient samples of limited size.
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