Volume 5, Number
5, October 2003
Articles from
"Statistical Methodology in Alzheimer's Disease Research II", May
2002
Pages 349-355
Julia L. Bienias, Laurel A. Beckett, David A. Bennett, Robert S.
Wilson, Denis A. Evans
Design of the Chicago Health and Aging Project (CHAP)
Abstract: The design of the Chicago Health and Aging
Project (CHAP) is described. CHAP is a longitudinal population study
of common chronic health problems of older persons, especially of
risk factors for incident Alzheimer’s disease, in a biracial
neighborhood of the south side of Chicago. Special attention is
given to three unusual design features of the study. One feature is
that clinical evaluation for Alzheimer’s disease is confined to a
stratified random sample of all participants. This feature results
in substantial cost savings and substantially less bias than
screening approaches but has the disadvantages of adding analytic
complexity and requiring the use of indirect means to identify a
disease-free cohort for the development of incident Alzheimer’s
disease. The second unusual feature is efficiently combining in
analyses the successive independent multiple samples that are drawn,
one from each data collection cycle. The third unusual feature is
entering successive age cohorts of community residents into the
study as they attain 65 years of age. This has the advantages of
enhancing direct investigation of the effect of age on the action of
risk factors for Alzheimer’s disease and direct examination of
cohort effects. The interaction of these features is described,
especially as they pertain to a study in which data are collected in
successive waves. The results from these waves must be combined for
effective analysis of the relation among risk factors and incident
disease.
Pages 357-365
Danielle J. Harvey, Laurel A. Beckett, Dan M. Mungas
Multivariate Modeling of Two Associated Cognitive Outcomes in a
Longitudinal Study
Abstract: Longitudinal studies of Alzheimer’s disease
provide information about cognitive decline and predictors of this
decline. However, overall cognitive function is comprised of many
underlying processes, each of which may respond differently over
time and may be affected by different predictors. In addition to
studying how these processes decline independently, one might also
be interested in how the processes decline together. Multivariate
growth models, an extension and modification of random effects
models, provide a means of dealing with these issues and enable
assessing the association between the processes of interest. This
technique allows for separate random effects and predictors for each
process in the same model, thereby providing simultaneous estimates
of the model parameters and variability for each process. We can
then determine if factors associated with decline in one process are
also associated with decline in another process and the extent to
which the processes differ. We provide data that include information
on two underlying processes of cognitive function, namely memory and
executive function, to illustrate this methodology.
Pages 367-373
Larry F. Hughes, Kyle Perkins, Benjamin D. Wright, Heather Westrick
Using a Rasch Scale to Characterize the Clinical Features of
Patients with a Clinical Diagnosis of Uncertain, Probable, or
Possible Alzheimer Disease at Intake
Abstract: Objective: This study examined the clinical features of
patients with clinical diagnoses of probable Alzheimer disease (AD),
possible AD, and uncertain. Design: Case study comparing three
groups of AD patients diagnosed at their initial visit to an
Alzheimer outpatient clinic. Setting: Southern Illinois University
School of Medicine’s Center for Alzheimer Disease and Related
Disorders (CADRD) assessment sites (20) in rural Illinois.
Participants: 300 patients assessed at CADRD between January 1, 1994
and July 1, 2000. Measurements: Patients were given an extensive
clinical battery consisting of physical and neurologic examination,
mental status testing including the Mini-Mental State Exam (MMSE),
Short Blessed Dementia (SBD) and Blessed Dementia Scale (ADL),
medical history evaluation, and laboratory tests. Other data
included age at visit, gender, and medical history variables.
Results: Mean MMSE, SBD, and ADL scores differed significantly
between groups (p’s<.01). In all three cognitive tests, the
uncertain group was the least impaired while the probable AD group
was the most impaired. A Rasch model indicated that only the
cognitive measures were useful in discriminating between the three
diagnostic groups. Conclusion: In general, probable AD patients were
distinguished from possible AD patients by the severity of their
dementia as measured by the MMSE, ADL and SBD as well as
Hachinski-Ischemic Score (HIS) scores. A Rasch model did well at
predicting group membership based upon dementia measures only. The
uncertain group differed from the AD groups in age and dementia
severity as measured by the MMSE, ADL and SBD. Noting differences
between this and previous studies, we speculate disparity may be
related to differences in population ethnicity.
Pages 375-382
Ming Ji, Chengjie Xiong, Michael Grundman
Hypothesis Testing of A Change Point During Cognitive Decline Among
Alzheimer’s Disease Patients
Abstract: In this paper, we present a statistical
hypothesis test for detecting a change point over the course of
cognitive decline among Alzheimer’s disease patients. The model
under the null hypothesis assumes a constant rate of cognitive
decline over time and the model under the alternative hypothesis is
a general bilinear model with an unknown change point. When the
change point is unknown, however, the null distribution of the test
statistics is not analytically tractable and has to be simulated by
parametric bootstrap. When the alternative hypothesis that a change
point exists is accepted, we propose an estimate of its location
based on the Akaike’s Information Criterion. We applied our method
to a data set from the Neuropsychological Database Initiative by
implementing our hypothesis testing method to analyze Mini Mental
Status Exam scores based on a random-slope and random-intercept
model with a bilinear fixed effect. Our result shows that despite
large amount of missing data, accelerated decline did occur for MMSE
among AD patients. Our finding supports the clinical belief of the
existence of a change point during cognitive decline among AD
patients and suggests the use of change point models for the
longitudinal modeling of cognitive decline in AD research.
Pages 383-390
Kathleen A. Lane, Sujuan Gao, Siu L. Hui, Jill R. Murrell, Kathleen
S. Hall, Hugh C. Hendrie
Apolipoprotein E and Mortality in African-Americans and
Yoruba
Abstract: The literature on the association between apolipoprotein E
(ApoE) and mortality across ethnic and age groups has been
inconsistent. No studies have looked at this association in
developing countries. We used data from the Indianapolis-Ibadan
Dementia study to examine this association between APOE and
mortality in 354 African-Americans from Indianapolis and 968 Yoruba
from Ibadan, Nigeria. Participants were followed up to 9.5 years for
Indianapolis and 8.7 years for Ibadan. Subjects from both sites were
divided into 2 groups based upon age at baseline. A Cox proportional
hazards regression model adjusting for age at baseline, education,
hypertension, smoking history and gender in addition to
time-dependent covariates of cancer, diabetes, heart disease,
stroke, and dementia was fit for each cohort and age group. Having
ApoE e4 alleles significantly increased mortality risk in
Indianapolis subjects under age 75 ( hazard ratio: 2.00; 95% CI:
1.19 – 3.35; p=0.0089). No association was found in Indianapolis
subjects 75 and older (hazard ratio: 0.71; 95% CI: 0.45 - 1.10;
p=0.1238), Ibadan subjects under 75 (hazard ratio: 1.04; 95% CI:
0.78 to 1.40; p=0.7782), or Ibadan subjects over 75 (hazard ratio:
1.21; 95% CI: 0.83 to 1.75; p=0.3274).
Pages 391-398
Marta S. Mendiondo, J. Wesson Ashford, Richard J. Kryscio, Frederick
A. Schmitt
Designing a Brief Alzheimer Screen (BAS)
Abstract: Context: With advances in the treatment of Alzheimer’s
disease (AD), clinical focus has shifted to early patient
identification. Memory recall tests and category fluency distinguish
normal individuals from early AD patients. Objective: Develop a
brief test for general practitioners to screen for AD. Design:
Examination of items from the MMSE and category fluency. Setting and
Participants: A Brief Alzheimer Screen (BAS) was developed from
cognitive assessments on 406 normal subjects and 342 mild AD
patients CERAD (Consortium to Establish a Registry for AD) dataset.
The derived measure was then applied to a second validation sample.
Main Outcome Measure: Logistic regression was used to derive a
predictive equation, which was then applied to two validation
samples to estimate sensitivity and specificity. Results: The
resulting logistic model for discriminating between mild AD and
controls included: recall of 3 words, number of animals named in 30
seconds, date, and spelling of WORLD backwards, (p<0.001 for each)
accounting for 77% of the variance. When applied to the validation
samples, sensitivity and specificity were over 99% and 87%,
respectively. Conclusions: These data support the use of the BAS as
a potential screen of patients over 60 years of age.
Pages 399-407
Jianzhao Shen, Paul Crane, Sujuan Gao
A Latent Variable Model Approach for Assembling and Scoring
Screening Tests for Dementia
Abstract: In dementia studies, the diagnosis of dementia often
relies on results of screening tests aimed at measuring various
dimensions of cognitive functions. The current practice of scoring a
screening test involves simply summing the correct responses from
each item. However, this method may be imprecise and inefficient in
the predictive power of the score for dementia. We propose a latent
variable model approach for the scoring and item selection of such
tests. We model the item responses to be random variables based on
latent variables. We also model the disease outcomes to be a
function of the latent variables. Maximum likelihood estimates are
obtained by maximizing the joint likelihood functions of disease and
the item responses over a specified distribution function for the
latent variables. Variances of model parameters are estimated using
a nonparametric bootstrap method. We illustrate the approach using a
screening test for dementia from a community-based study.
Pages 409-418
Chengjie Xiong, J. Philip Miller, John C. Morris
Testing Correlation of Cognitive Decline at Adjacent Stages
of Dementia
Abstract: This paper studies the correlation of cognitive
progression for subjects whose dementia has made a transition from a
milder stage of severity to the next stage of impairment. We model
the progression of cognitive decline at adjacent stages of dementia
by using a general linear mixed model. We also propose a three-step
procedure to detect the best configuration of the covariance
matrices for the random components in the model. After the best
configuration of covariance matrices for the random components in
the model is determined, we then recommend another two-step process
to test whether there exists a significant correlation between the
rate of cognitive decline before the transition, the cognitive
status at the transition time, and the rate of cognitive decline
after the transition. In addition, we present asymptotic confidence
interval estimates for the correlations associated with a
transition. This method is applied to several composite psychometric
factor scores in the longitudinal database from the Alzheimer’s
Disease Research Center (ADRC) at Washington University in St.
Louis.
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