Barrier, JC

Crismon, ML

Lawson, KA

Mascarenas, C

Rascati, KL

Wilson, JP

Worchel, J

Yang, M

Elsevier - Publisher Connector 

A38 Abstracts
OBJECTIVES: Updated risk equations are available for predict-
ing outcome in people with type 2 diabetes (T2D): the UKPDS
Outcomes Model (UKPDS 68). It is important to assess the valid-
ity of applying risk equations to populations other than those
from which they were derived. The objective was to evaluate
how well the UKPDS-68 equations predicted vascular morbidity
and mortality in real-life data from Cardiff, UK, and compare
estimates with the previous UKPDS Risk Engine equations
(UKPDS-RE [from UKPDS publications 56 and 60]).
METHODS: The equations were incorporated into a stochastic
simulation model that estimated the incidence and prevalence of
complications (DiabForecaster). Predicted results from the model
were compared with population data from Cardiff for coronary
heart disease (CHD), stroke and all cause mortality. The annual
incidence of newly diagnosed T2D, baseline modiﬁable risk
factors and demographic proﬁles were matched to the Cardiff
data. RESULTS: Internal validation, using a baseline cohort
matched to the UKPDS study, demonstrated that the model pre-
dicted 12-year cumulative incidence in line with previous UKPDS
publications. Real life and predicted event rates for CHD were:
116, 153 and 137 events/1000 T2D patients/yr for the Cardiff
data, UKPDS-RE and UKPDS-68, respectively. For stroke: 178,
153 and 128 events/1000 T2D patients/yr, respectively. For all
cause mortality: 418, 430, and 475 events/1000 T2D patients/yr,
respectively. CONCLUSIONS: All UKPDS equations demon-
strated internal validity when compared with published UKPDS
data, however both UKPDS-RE and UKPDS-68 equations over
predicted the incidence of CHD and mortality and under pre-
dicted stroke. While all endpoints predicted were reasonably
concordant with observational data discrepancies between
UKPDS-68 and UKPDS-RE are worthy of further investigation.
PDB24
EFFECTS OF INDUCING CORRELATION AMONG
CHOLESTEROL PARAMETERS ON OUTCOMES IN
SIMULATION OF PHARMACEUTICAL EFFECTIVENESS
Frick KD1, Sorensen SV2, Hollenbeak C3,Wade A2
1Johns Hopkins University, Baltimore, MD, USA; 2United BioSource
Corporation, Center for Health Economics and Policy, Bethesda, MD,
USA; 3Surgery and Health Evaluation Sciences, Penn State College of
Medicine, Hershey, PA, USA and Visiting Scientist, United BioSource
Corporation, Center for Health Economics & Policy, Bethesda, MD,
USA
OBJECTIVES: To determine whether inducing correlation
among triglyceride, HDL, and LDL levels in a pharmaceutical
treatment Monte Carlo simulation affects parameters’ means
and variances; proportion with all parameters controlled; and
summary statistics of estimated total cholesterol. METHODS:
Means, standard deviations, and correlations among the choles-
terol parameters were estimated from NHANES data for meta-
bolic syndrome (MS) and diabetic patients with all parameters
uncontrolled. For simulation, distributions were ﬁt to the data.
Analyses used 1000 replications of populations of 1000. Popu-
lations were generated without correlated parameters and with
correlation induced in the uncorrelated data. Estimated changes
with fenoﬁbrate, statins, and a combination were taken from the
literature. Total cholesterol was approximated using HDL, plus
LDL, plus 20% of triglycerides. Differences in means and ratios
of variances comparing uncorrelated and correlated results were
calculated for each replication. Null hypotheses were rejected
when the interval the middle 95% of replications spanned did
not include zero for differences and one for ratios. RESULTS:
Correlations were higher for diabetic than MS patients. Despite
the data’s and distribution’s non-normality, induced correlations
were close to NHANES correlations. Correlation did not affect
the summary statistics of individual parameters or the propor-
tion with all parameters under control. Correlation affected
results for total cholesterol, the sum of other parameters. For
MS, variance of total cholesterol was less than 7% lower with
uncorrelated data than with correlated data. For diabetic
patients, variance of total cholesterol was more than 20% higher
with uncorrelated data. Findings held for subpopulations with
and without all parameters controlled after taking medication.
Variance results were similar across treatments. Total cholesterol
means differed primarily for MS subgroups. CONCLUSIONS:
Summary statistics (particularly variance) for sums of parame-
ters are affected by correlation in Monte Carlo simulations.
Underestimated and overestimated variances increase the risk of
Type I and II error respectively.
PDB25
MARGINAL STRUCTURAL MODELS—AN EXPLANATION AND
ILLUSTRATION
Gause D, Plauschinat C, Kahler K
Novartis Pharmaceuticals, East Hanover, NJ, USA
OBJECTIVES: 1) Provide a concise explanation of “inverse
probability of treatment weights” (IPTWs) for estimating mar-
ginal structural models (MSM), pointing out its advantages and
disadvantages to alternative methods of adjusting for covariates
in observational studies, and 2) illustrate use of MSM for com-
paring impact of drug use on medical costs. METHODS: Con-
founding can be controlled by stratiﬁcation or with covariates in
regression. When there are many confounders adjustment using
propensity scores is sometimes used but these are 1) not easily
generalized with more than two comparison groups, and 2) may
result in residual confounding when matching is used, and is not
helpful with time dependent covariates affected by the exposures
being compared. An alternative is based on modeling the “mar-
ginal” distribution of counterfactuals associated with each group
(as described by Robins, Hernan, and Brumback [2000]). This
is accomplished using weights related to propensity scores. A
MSM will be illustrated by comparing outpatient medical costs
for patients taking diabetic drug treatments, adjusting for both
baseline and subsequent time dependent diagnoses and lab data.
A multinomial logit model is used to estimate each subject’s con-
ditional probability of receiving TZD, sulfonylurea, or met-
formin given their history of baseline and time dependent
covariates. RESULTS: These predicted probabilities are basis for
IPTWs used to estimate mean marginal outpatient costs for each
drug group. CONCLUSIONS: Effects of confounders are broken
when their associations with drug treatment groups are broken,
and this can be done using MSM where the data is reweighted
such that confounders have similar distributions within drug
comparison groups.
PDB26
ANTIPSYCHOTIC UTILIZATION AND TREATMENT-EMERGENT
DIABETES—A METHODOLOGICAL COMPARISON USING A
CLAIMS DATABASE
Yang M1, Barner JC1, Rascati KL1, Lawson KA1,Wilson JP1,
Crismon ML1,Worchel J2, Mascarenas C3
1University of Texas at Austin, Austin,TX, USA, 2Central Texas
Veterans Health Care System, Austin,TX, USA, 3South Texas Veterans
Health Care System, San Antonio,TX, USA
OBJECTIVE: To evaluate the robustness of the relationship
between antipsychotic utilization and treatment-emergent dia-
betes among patients newly initiated on therapy, when method-
ologies were varied while controlling for covariates.
METHODS: Seven models were created based on the following
methodological variations: 1) study designs (retrospective cohort
and case-control); 2) treatment exposure assignment (intent-to-
treat (ITT) and as-treated (AT)); and 3) statistical approaches
A39Abstracts
(propensity scoring, standard and conditional logistic regression,
and Cox proportional hazards function). Control variables
included: demographics (age, gender, race, and region), general
health comorbidities (hypertension and dyslipidemia), mental
health comorbidities (bipolar disorder, depression, post-
traumatic stress disorder, schizophrenia, and substance abuse),
antipsychotic drug utilization patterns (monotherapy, switching,
and combination therapy), treatment duration, medication re-
exposure, and treatment initiation year. Claims databases from
the Veterans Administration North Texas Health Care System
and the South Texas Veterans Health Care System (1995–2004)
were used. RESULTS: Of the eligible patients (N = 8949), regard-
less of variations in methodologies of the seven models, there
were no signiﬁcant differences in diabetes risk among patients
who were: 1) initiated on the second generation antipsychotics
(SGAs) compared to those on the ﬁrst generation antipsychotics
(FGAs); 2) initiated on olanzapine compared to those on risperi-
done; and 3) exposed to olanzapine or quetiapine compared to
those exposed to FGAs. Inconsistent results among the models
were observed when comparisons were made between: 1)
patients initiated on quetiapine (increased risk vs. no difference)
compared to those on risperidone, and 2) patients exposed to
risperidone (decreased risk vs. no difference) compared to those
exposed to FGAs. Differences occurred among the following.
METHODS: ITT retrospective cohort and ITT case-control; and
AT retrospective cohort and AT case-control. CONCLUSIONS:
With respect to antipsychotic utilization, results of the various
models using different methodologies were largely consistent.
Advantages and disadvantages of the seven models will be 
presented.
PDB27
ANTIPSYCHOTIC UTILIZATION AND TREATMENT-EMERGENT
DIABETES—COMPARISON OF RESULTS WITH AND WITHOUT
PROPENSITY SCORING
Yang M1, Barner JC1, Lawson KA1, Rascati KL1, Crismon ML1,
Wilson JP1,Worchel J2, Mascarenas C3
1University of Texas at Austin, Austin,TX, USA, 2Central Texas
Veterans Health Care System, Austin,TX, USA, 3South Texas Veterans
Health Care System, San Antonio,TX, USA
OBJECTIVE: To examine whether the relationship between
antipsychotic utilization and treatment-emergent diabetes among
patients newly initiated on therapy varied, when propensity
scoring was (Model I) and was not (Model II) used. METHODS:
Claims databases from the Veterans Administration North Texas
Health Care System and the South Texas Veterans Health Care
System (1995–2004) were used. Both Models I and II utilized a
retrospective cohort design and an intent-to-treat method to
assign treatment exposure. Covariates included demographics
(age, gender, race, and region), general health comorbidities
(hypertension and dyslipidemia), mental health comorbidities
(bipolar disorder, depression, post-traumatic stress disorder,
schizophrenia, and substance abuse), antipsychotic drug utiliza-
tion patterns (monotherapy, switching and combination
therapy), treatment duration, medication re-exposure, and treat-
ment initiation year. Logistic regression was used to analyze data.
RESULTS: Eligible patients (N = 8949) had a 6.0% annual dia-
betes incidence rate. No signiﬁcant difference was found in dia-
betes risk between patients treated with the ﬁrst generation
antipsychotics (FGAs) and the generation antipsychotics (SGAs).
Findings were consistent among most covariates. In both models,
when compared to monotherapy, antipsychotic combination
therapy increased diabetes risk, while switching did not. Antipsy-
chotic re-exposure, early treatment initiation years, and a shorter
treatment duration were associated with an increased diabetes
risk. Results varied with the following two variables: nonwhites
were associated with an increased risk in Model I, while there
was no association in Model II; depression was associated with
an increased risk in Model II, while there was no association in
Model I. All other variables were not signiﬁcant and were con-
sistent between the two models. CONCLUSIONS: SGAs were
not associated with an increased diabetes risk when compared
to FGAs. Including propensity scoring in a retrospective cohort
design may alter the results of some covariates; however, it did
not change the results regarding the class of antipsychotics uti-
lized and risk of diabetes mellitus.
PDB28
DEVELOPMENT OF NEW INDICES OF GLYCEMIC CONTROL
IN PATIENTS WITH DIABETES USING DIGITAL SIGNAL
PROCESSING
Gold KF1, Rodbard D1, Garﬁnkel S2
1American Institutes for Research, Washington, DC, USA, 2American
Institutes for Rerearch, Chapel Hill, NC, USA
OBJECTIVES: The objective is to examine the performance of
a variety of ﬁlters used in digital signal processing and evaluate
the ﬁlter coefﬁcients as potential indices of glycemic control.
Digital signal processing has played an extensive role in sepa-
rating signal from noise in biological signals for decades. Cas-
caded adaptive ﬁlters, high pass ﬁlters, Kalman ﬁlters as well as
discrete Fourier transforms have been used to produce clean
signals and obtain the frequency spectrum for ECG signals. With
the rapid advances in Continuous Glucose Monitoring (CGM)
sensors, a nearly continuous stream of glucose levels is made
available with wavelike postprandial excursions. Extraction of
key information beyond descriptive statistics from the mass of
glucose data has been illusive. This is no clean classiﬁcation as
in the case of cardiac arrhythmias. Little prior work in the
medical literature exists except a Kalman ﬁlter simulation using
a sinusoidal function to represent a postprandial excursion
(Palerm, 2005), and a brief note by Knobbe (2005) discussing
Kalman ﬁlters in CGM. METHODS: The mathematical form of
each ﬁlter is presented along with identiﬁcation of associated for-
mulas for each of their ﬁlter coefﬁcients, a table of characteris-
tic ﬁlter coefﬁcients, coefﬁcient ranges, stability and evidence of
substantive interpretability. Archetype postprandial excursions
are represented by truncated Taylor series. RESULTS: Charac-
teristics compared include: stability of ﬁlter coefﬁcient under
trivial curve variation representing the post-prandial excursion
waveform, range of values, sensitivity to substantial variation in
the waveform, usefulness in combination with other statistics
and sensitivity and speciﬁcity in predicting hypoglycemic events.
CONCLUSIONS: Several signal processing ﬁlters appear to be
effective for extraction of information from CGM series, and
some of these appear promising as a basis for new indices of
glycemic control and for classiﬁcation of patients and effects of
medications such as rapidly acting insulin analogs that blunt the
post-prandial excursions.
DIABETES—Patient Reported Outcomes
PDB29
IMPACT OF TYPE OF PHARMACY (CHAIN VERSUS
INDEPENDENT) ON MEDICATION ADHERENCE IN PATIENTS
WITH TYPE 2 DIABETES: A RETROSPECTIVE DATABASE
ANALYSIS
Sheehan C, Kalsekar ID, Peak A
Butler University, Indianapolis, IN, USA
OBJECTIVE: Studies have indicated that independent pharma-
cies outperform chain pharmacies in one-on-one personal atten-
tion and quality of patient counseling. However, there are no
studies examining whether these beneﬁts translate into outcomes


PDB26 ANTIPSYCHOTIC UTILIZATION AND TREATMENT-EMERGENT DIABETES—A METHODOLOGICAL COMPARISON USING A CLAIMS DATABASE 

file:///data/core-remote/dit/data/elsevier/pdf/07b/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMTA5ODMwMTUxMDY0NDI0NQ==.pdf

PDB26 ANTIPSYCHOTIC UTILIZATION AND TREATMENT-EMERGENT DIABETES—A METHODOLOGICAL COMPARISON USING A CLAIMS DATABASE

Abstract

Similar works

Full text

Available Versions

Elsevier - Publisher Connector

Elsevier - Publisher Connector