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1P50HD055751-01 (Dr. Robert D.
Gibbons PI of Statistical Core)
National Institute of Mental Health 08/06/2007 - 07/31/2012
Autism Center of Excellence: Translational Studies of Insistence of Sameness
in Autism
The UIC ACE will focus over the next 5 years
on the genetics, neurobiology, cognitive
and affective processes, and pharmacology of insistence on sameness
(IS) in autism spectrum
disorders (ASD). A large sample of children
with self-reported autism spectrum disorder will be screened
by the Assessment
Core for further screening by administration
of the ADI-R to the parents. Profanes meeting ADI-R criteria
for autistic disorder
will be recruited for further study if
they are also classified by the ADI-R IS items as high (N=150)
or low IS (N=100). In
addition, high IS subjects will need to
score 15 or more on the sum of two IS factors on the RBS-R to
avoid floor effects
for the pharmacogenetic trial.
These 250
subjects will all be included in project
I, Genetics of Serotonin in Autism: Neurochemical
and Clinical Endophenotypes, along with
225 previously studied subjects and their
parents for a total of 475 trios. This
project will study 25 serotonin- related
genes for association with autism and with
IS more specifically. Resequencing of strong
candidate genes will be conducted with
all of the subjects in the pharmacogenetic
Project III and with the low IS subjects
in Project II. In addition, the 250 subjects
will have serotonin measures collected
for analysis with genetic and phenotype
measures.
In Project II: Translational Studies of
Cognitive, Affective and Neurochemical Processes Underlying
Insistence on Sameness in Autism, fMRI studies of IS will be
conducted on 50 high IS subjects also in Project III, 50 low
IS subjects (also in Project I) and 50 control subjects. In
addition, rat studies in which parallel behavioral and neurochemical
approaches will be used.
Project III: The Pharmacogenetics of Treatment
for Insistence on Sameness in Autism has been designed to replicate
and extend a preliminary study of escitalopram treatment of
IS related irritability in ASD.
Project IV: Autism-Associated Serotonin
Transporter (SERT) Mutations will provide characterization of
mutations previously found to be associated with high IS behaviors
in subjects with autism. The UIC ACE is an exciting center that
brings together experts in a diverse set of disciplines to comprehensively
study IS, one of the two cardinal features described by Kanner
in 1943.
U18HS016973 (Dr. Robert D.
Gibbons PI of Statistical Core)
National Institute of Health (AHRQ - CERT) 09/01/2007 - 08/31/2011
Tools for Optimizing Prescribing, Monitoring and Education
Efforts to maximize the benefits and minimize
the risks associated with drugs continue to be impeded by suboptimal
prescribing, inadequate monitoring of patients' outcomes, and
inadequate prescriber education and support to overcome these
limitations. As a result, the US healthcare system suffers from
persistent problems of underuse, overuse and misuse of drugs,
with unacceptably high rates of preventable errors, adverse
effects and suboptimal patient health outcomes. To address these
problems, we propose a CERT organized around the following theme:
Tools for Optimizing Prescribing, Monitoring and Education (TOP-MED).
The long term objective of our CERT is
to improve the safety, efficacy and cost-effectiveness of drug
therapy by increasing the appropriateness of prescribing and
the quality of monitoring. The short term objective is to develop,
redesign, refine, integrate, test, deploy and disseminate tools
and training materials in five key areas: drug formularies,
drug utilization review, lab-pharmacy linkages, N-of-1 trials
and pharmacoeconomics. To achieve these objectives, the TOP-MED
CERT will pursue the following specific aims:
1. Revitalize the drug formulary as an
evidenced based tool for directing drug therapy decisions.
2. Re-engineer drug usage review (DUR)
systems and processes so that data analysis is easier, more
timely and more likely to yield valid generalizations.
3. Reduce prescribing errors and enhance
recognition of adverse drug effects in high hazard contexts
by linking lab and pharmacy information systems and generating
clinical alerts when problems are detected.
4. Develop, deploy and evaluate an N-of-1
trial service, integrated into a formulary restriction program,
in order to support the goal of individualized therapy without
succumbing to the unsafe, unscientific experimentation that
is often now the norm.
5. Implement and study the impact of pharmacoeconomic
support to enhance formulary decision making,
as well as evaluate the cost-effectiveness of other interventions.
Cutting
across
these five aims will be initiatives to
disseminate and deploy these tools in a more rigorous and far-reaching
fashion; to
provide support to make the tools easier
to use; to enhance the buy-in, motivation and enthusiasm of
prescribers to use
the tools; to link the tools with each
other in order to gain greater synergies, and to share the knowledge
gained and lessons
learned across multiple institutions. Headquartered
at the University of Illinois at Chicago (UIC), and leveraging
collaborations
with the Cook County Bureau of Health Services,
Northwestern Memorial Hospital, University of Washington, the
VA Center for
Medication Safety, Advocate Health Care,
the Illinois Hospital Association and the Illinois Department
of Public Health, the
TOPMED CERT will be able to focus on the
needs of multiple AHRQ priority populations.
R01 MH66302-05 (Dr. Robert D.
Gibbons PI)
National Institute of Mental Health 09/20/2006 - 08/31/2011
Mental Health Computerized Adaptive Testing - Competitive Renewal
The aim of this investigation is to develop
and evaluate computerized adaptive testing
programs and algorithms for the assessment
of depression. In the original study we
demonstrated the feasibility of item response
theory (IRT), and computerized adaptive testing
(CAT) in the development and administration
of a large (626 item) mental health rating
scale. Using an item bank of 626 mood and
anxiety disorder symptom items, we found
that
(a) the majority of the items in
the item bank (90%) were discriminating
of high and low levels of
mood disorders,
(b)
the bi-factor IRT model did an excellent
job of accounting for the clustering of
items within symptom domains,
(c) on average,
CAT administration of the test resulted
in a 95% reduction in the number of items
administered to an individual subject (24
out of 626 items using simulated CAT and
31 items for live CAT testing), and
(d)
the correlation between the CAT based impairment
rating and the score based on all 626 items
was r=0.93.
Based on these very encouraging preliminary results,
this competitive
renewal proposes to use IRT and CAT to
develop a CAT Depression Inventory (CAT-DI). The specific objectives
of the renewal are
(1) create a depression item bank by collecting
items from a review of approximately 100 existing depression
scales and depression
items previously identified as a part of
the PROMIS network,
(2) calibrate the depression item bank using
a variety of IRT
models (unidimensional, bi-factor, multidimensional)
using a balanced incomplete blocks (BIB) design administered
to 800
depressed patients and 200 non-depressed
controls,
(3) obtain a new sample of 300 subjects (200 depressed,
100 non-depressed)
that take all of the items in the bank,
perform a simulated CAT, and optimize the tuning parameters
of the CAT,
(4) obtain
a new sample 300 subjects (200 depressed,
100 non- depressed) for live CAT testing,
(5) apply the final
CAT-DI to a community
sample of 700 patients (approximately 200
meeting criteria for major depressive disorder - MOD) to test
validity (comparison
of impairment estimates in patients with
and without MOD), predicting MOD, and establishing normative
ranges for patient screening,
and
(6) conduct 20 cognitive interviews
of patients from a behavioral health clinic who have taken the
CAT-DI as a qualitative research
approach to beta-testing of the instrument.
R56 MH078580-01 (Dr. Robert D. Gibbons PI)
National Institute of Mental Health 09/30/2006 - 08/31/2008
Antidepressant Treatment and Suicidality: Methodological and Biostatistical
Solutions
The purpose of this proposal is to develop,
test, and apply new statistical design and
analytical methodologies that can be used to identify low base
rate drug - adverse event
(AE) interactions. These new methods will
then be applied to a wide range of existing non-experimental datasets
to examine the
relationship between SSRIs and suicide ideation,
attempts, and completion. We have designed this research project
as an integral
collaboration between biostatisticians, research
psychiatrists and clinicians, economists, and pharmacoepidemiologists,
working
with large ecological and electronic patient
databases covering years where antidepressant use is varying dramatically.
The
first set of aims will lead to the development of new biostatistical
methods for making inferences from spontaneous
reporting and electronic
medical record databases. Specifically, we
will
1. develop new statistical designs and analyses
for identifying drug-AE interactions using both spontaneous (SRS)
and active reporting systems (ARS);
2. develop biostatistical methods to address
selection and reporting bias in electronic medical record databases;
3. develop statistical methods for large-scale
drug-AE screening.
The second set of aims involves
the critical application of these methods to existing large scale
databases in order to examine
the role of antidepressants in suicidality
among different populations. The datasets range from the spontaneous
reporting system (MedWatch),
to electronic medical record databases (e.g.,
VA, PHARMetrics, Kaiser, and PHARMO, Indian Health Service), to
the synthesis of
information from randomized clinical trials
(RCTs). The work in this proposal will be carried out by a research
consortium that
will study national and international drug
safety issues. The multidisciplinary group includes the areas
of statistics (Drs.
Gibbons, (PI), Brown (co-Pi), Bhaumik, Duan,
Hur, Marcus, SahaRay), psychiatry from adult, child, and genetic
perspectives (Brent,
Mann, Reynolds, Tsuang), health economics/econometrics
(Meltzer, Heckman), pediatrics (Leslie), and pharmacoepidemiology
(Valuck).
Collaboration with members of the VA (Cunningham,
Valenstein), Kaiser Permanente (Clarke, Gullion), PHARMO (Erkens)
and the Indian
Health Service (Perez) is an integral part
of the proposal.
RO1 MH069353-03 (Dr. Dulal Bhaumik PI)
National Institute of Mental Health 05/01/2005 - 03/31/2008
Statistical Testing for Generalized Mixed-effects Models
Over the last decade, mental health services
researchers have made widespread use of
generalized mixed-effects regression models for analysis of
clustered and longitudinal
data. Much of the work in this area has
involved the development of efficient methods of statistical
estimation, based on maximum
marginal likelihood, empirical Bayes, and
fully Bayesian estimation strategies. Generalization of the
original model for continuous
and normally distributed data to the case
of non-linear mixed-effects regression models for binary, ordinal,
nominal, and Poisson
distributions are now generally available
and enjoy widespread use.
Furthermore, computer software has now
been developed and is either freely available over the Internet
or commercially available. With the speed of this development
and acceptance by the research community, it is therefore somewhat
surprising that so little research has been conducted on the
issue of hypothesis testing for generalized mixed-effects regression
models. Indeed, traditional approaches of large sample tests
based on likelihood ratios and Wald-type statistics are all
that are generally available. These approaches are limited due
to their large sample properties in addition to well-known limitations
for testing models with varying numbers of random effects.
In addition to the absence of an arsenal
of tools for statistical testing, the literature is also quite
limited with respect to statistically rigorous approaches to
computing statistical power for clustered and longitudinal designs.
For non-linear mixed-models (e.g., binary and ordinal cases),
the literature on statistical power is virtually nonexistent,
and gross oversimplification of the study design, estimation,
and testing procedures must be used to obtain any estimates
of the number of measurements needed at each level of nesting
that are required to test a hypothesis with a reasonable balance
of Type I and II errors.
The primary goal of this proposal is to
fill this void by
(1) studying the large and small sample properties
of various existing and new tests suitable for generalized linear
and non-linear mixed-effects regression models,
(2) developing
statistically rigorous approaches to computing
statistical power for this class of models
that is now so widely used by behavioral,
social, and biological scientists in general,
and health and mental health services researchers
in particular, and
(3)
developing a computer program for computing
statistical power for linear and non-linear
mixed-effects regression models (MIXPWR),
and to incorporate these new tests into
the existing programs (MIXREG, MIXOR, MIXPREG,
MIXNO), which are distributed freely
from the MIXREG/MIXOR
homepage.
Preliminary
results reveal that the new small sample
tests that we have derived provide the
ability to detect dramatically smaller
effects in small samples and increased
statistical power over traditional large
sample tests even when sample sizes are
large. The net result is the ability to
use rigorous statistical methods for analysis
of longitudinal and clustered data, even
in small and difficult to recruit populations
such as minorities, homeless, and those
at high risk for suicide.
R01 MH67198-01 (Dr. Hua Yun
Chen PI)
National Institute of Mental Health 07/01/2004 - 04/30/2008
Multivariate Probit Model for Health Services Research
In this study, we will develop a general mixed-effects
multivariate probit regression model for
the simultaneous analysis of repeatedly
measured multivariate binary data. Correlations
between multiple binary measures at a single
point in time are modeled as a factor analytic
process, and correlation among
the repeated measurements over time are
modeled as a random-effects process. The
net result is that we can now model the
effects of design variables (e.g., changes
in the health care delivery system) and
case mix variables (e.g., age, sex, and
race) on multivariate utilization patterns.
Generalizations of the model
will include extension to ordinal response
data (e.g., no use, mild use, moderate
use, high use, or 0 visits, 1 visit, 2
visits, 3 or more visits), mixtures of
discrete and continuous responses (e.g.,
the joint analysis of service utilization
and cost), and extension to a multivariate
logistic regression model. An integral
part of the project will be to both explore
and develop alternative approaches to likelihood evaluation
(fixed-point and adaptive quadrature, Laplace
approximation, and Monte Carlo
integration), parameter estimation (Newton
Raphson, Fisher scoring, and the EM algorithm),
and hypothesis testing. A large-scale
simulation study will be conducted to study
the statistical properties of the general
model and various alternative formulations.
Finally,
the model will be applied in the analysis
of data collected by Dr. Margarita Alegria
at the University of Puerto Rico on
the effects of health care reform on
longitudinal mental health services utilization.
In addition to development of the statistical
theory and estimation procedure, we propose
to develop a WINDOWS based freeware computer
program, MIXMVP, to be distributed from
the MIXREG/MIXOR
homepage.
No general multivariate probit regression
software is currently available.
R01 MH65556-03 (Gibbons,
Hur, Bhaumik, Hedeker, DSI)
National Institute of Mental Health 07/19/2002 - 05/31/2007
Mixed-Effects ZIP Models for Mental Health Services Research
This project involves the development of a mixed-effects
zero-inflated Poisson (ZIP) regression model
for the analysis of health services utilization data. The ZIP
model provides a
method for simultaneously modelling the presence
or absence of an event (e.g., service utilization) and
the intensity of utilization conditional on its use. Statistically,
the model
is a mixture of a logistic (use vs no use)
and Poisson (intensity of use) regression models. The same or
different covariates can
be related to the two outcomes. The purpose
of the grant is to extend the model to the case of a mixture of
fixed and random
effects so that it can be used in analysis
of clustered and/or longitudinal health services data.
R01 MH66302-03 (Gibbons, Bock,
Bhaumik, Kupfer (WPIC), Frank (WPIC), Kessler (Harvard),
Weiss (Minnesota), DSI)
MTA Follow-up Study 09/20/2002 - 07/31/2006
Mental Health Computerized Adaptive Testing
Mental health research relies heavily on antiquated
systems of measurement. The construction of traditional mental
health scales is based largely on subjective judgement, and at
best, application of methods from classical test theory to determine
a scale's psychometric properties. In this application we borrow
strength from major advances in test construction and administration
that have been developed in the fields of educational measurement
and modern psychometric theory. In particular, we propose to use
Item Response Theory (IRT) to calibrate a large item pool of 626
mood disorder items and then adaptively administer them such that
a given subject can be evaluated on a small subset of the items
to any practical degree of accuracy. The use of the IRT model
allows us to evaluate the intensity of the mood disorder for different
subjects who have taken potentially different numbers of items
selected from the item pool. Using computerized adaptive testing
(CAT) we can then adaptively select the most appropriate set of
items for each subject based on his/her responses to previous
items, beginning from a small screening set of items that characterize
low to high levels of impairment. The net result is that large "item
banks" can be developed that thoroughly characterize a particular
disorder. Although it is not routinely possible for any one subject
to be evaluated on all of the items, CAT permits each subject
to be evaluated on a small subset of the total item pool, with
minimal and controllable loss of information.
Contract (Gibbons, Marcus, Hur,
Chen, and Bhaumik)
National Institute of Mental Health 01/01/2000 - 12/31/2007
Statistical Center for analysis of the Long-term MTA Follow-up Study
The NIMH Multimodal Treatment Study for Attention-Deficit/Hyperactivity
Disorder (MTA) is the world's largest controlled study of the
treament of children with ADHD. NIMH has recently contracted with
the original investigators to provide a long-term naturalistic
followup study of the children who were enrolled in the original
randomized MTA study. Little is known regarding the analysis of
data from naturalistic followups of randomized clinical trials.
NIMH has contracted with the Center for Health Statistics at UIC
to provide statistical research on this topic and to aid the MTA
investigators in applying that work in analysis of the followup
data. The statistical effort includes research in the areas of
generalized mixed-effects regression models, analysis of observational
data, propensity score matching, instrumental variables, and growth
mixture models.
R01 MH056146-04 (Hedeker, Gibbons,
Bhaumik, DSI)
National Institute of Mental Health 07/01/1999 - 07/31/2003
Statistical Models for Nested Service Utilization Data
The goal of this project is to generalize a
variety of mixed-effects regression models
to the case of three-level data (e.g., Clinics, subjects
and measurement occasions). The project involves
development of the statistical theory and
use of the theory in modifying the MIXREG,
MIXOR, MIXPREG, and MIXNO computer programs
to the case of three-level designs. |
