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Predicting depression in rheumatoid arthritisThe signal importance of pain extent and fatigue and comorbidity.

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 61, No. 5, May 15, 2009, pp 667– 673
DOI 10.1002/art.24428
© 2009, American College of Rheumatology
ORIGINAL ARTICLE
Predicting Depression in Rheumatoid Arthritis:
The Signal Importance of Pain Extent and Fatigue,
and Comorbidity
FREDERICK WOLFE1
AND
KALEB MICHAUD2
Objective. To determine the incidence of self-reported depression (SRD) in rheumatoid arthritis and to identify and rank
clinically useful predictors of depression.
Methods. We assessed 22,131 patients for SRD between 1999 and 2008. We collected demographic, clinical and treatment
data, household income, employment and work disability status, comorbidity, scales for function, pain, global, and
fatigue, the Regional Pain Scale (RPS), the Symptom Intensity (SI) scale (a linear combination of the RPS and the fatigue
scales) and linear combinations of the Health Assessment Questionnaire, pain and global severity. We used logistic
regression analyses with multivariable fractional polynomial predictors, and Random Forest analysis to determine the
importance of the predictors.
Results. The cross-sectional prevalence of self-reported depression was 15.2% (95% confidence interval [95% CI] 14.7–
15.7%) and the incidence rate was 5.5 (95% CI 5.3–5.7) per 100 patient years of observation. The cumulative risk of SRD
after 9 years was 38.3% (95% CI 36.6 – 40.1%). Almost all variables were significant predictors in logistic models. In
Random Forest analyses, the SI scale, followed by comorbidity, best predicted self-reported depression, and no other
variable or combination of variables improved prediction compared with the SI scale.
Conclusion. Pain extent and fatigue (SI scale) are the dominant predictors of SRD. These variables, also of central
importance in the symptomatology of fibromyalgia, are powerful markers of distress. A strong case can be made for the
inclusion of these assessments in routine rheumatology practice. In addition, actual knowledge of comorbidity provides
important insights into the patient’s global health and associated perceptions.
INTRODUCTION
Depressive symptoms and depression are more common in
persons with rheumatoid arthritis (RA) (1) and other
chronic illnesses (2) compared with healthy subjects, and
add substantially to the distress of a chronic illness (3). In
patients with RA, social disadvantage (lower education
level, unmarried status, lower income), female sex, and
illness severity are among common groups of variables
that contribute to depression, as they do across all chronic
illnesses. In patients with RA, specific measures that contribute to depressive illnesses include pain, functional
loss, and impact on daily activities (1,4,5).
1
Frederick Wolfe, MD: University of Kansas School of
Medicine and National Data Bank for Rheumatic Diseases,
Wichita, Kansas; 2Kaleb Michaud, PhD: University of Nebraska Medical Center, Omaha, and National Data Bank for
Rheumatic Diseases, Wichita, Kansas.
Address correspondence to Frederick Wolfe, MD, National Data Bank for Rheumatic Diseases, 1035 North Emporia, Suite 288, Wichita, KS 67214. E-mail: fwolfe@
arthritis-research.org.
Submitted for publication August 25, 2008; accepted in
revised form January 16, 2009.
Among the problems faced by researchers regarding RA
and depressive illness are to identify all of the factors that
contribute to depressive illness, and to quantify the relative importance of these factors. Relative importance is
crucial, otherwise one is left with a multitude of factors,
but also the impossibility of adequately understanding the
contribution and clinical relevance of these factors. However, to a large extent, useful determination of relative
importance of depressive illness not been done. Most studies of RA and depression have not examined a full range of
variables, have not dealt with nonlinear relationships, and
have foundered on differences between causal models and
clinical models.
Causal factors related to depression in the absence of
chronic illness include traumatic experiences, genetic factors, temperament, interpersonal relations (6), family vulnerability (7), and hypothalamic⫺pituitary⫺adrenal (HPA)
axis dysregulation (8). However, these factors are almost
impossible to measure clinically.
The goal of this study was to identify and quantify
useful clinically important predictors of depressive symptoms. We used 2 major methods to identify predictors
of self-reported depression, logistic regression using frac667
668
tional polynomial predictors (9), and Random Forest analysis (10,11) to identify the predictors and to rank them
quantitatively. In addition, we have reported the incidence
rate, cumulative incidence, and cumulative risk of selfreported depression in order to place the predictive data in
proper perspective and to measure the burden of selfreported depression in RA.
PATIENTS AND METHODS
Patient population. We studied participants in the National Data Bank for Rheumatic Diseases (NDB) longitudinal study of rheumatic disease outcomes. NDB participants are diagnosed by US rheumatologists and are
recruited from their practices. Participants indicated in
their rheumatologist’s office that they were willing to participate in long-term survey research (12). Patients are
followed prospectively with semiannual, detailed, 28-page
questionnaires, as previously described (13–15). Approximately 8% of patients discontinue participation per year.
This report utilized NDB data in a longitudinal cohort
analysis of 29,524 adult participants (ages 18 –103 years),
of whom 22,131 had RA and 3,717 had a noninflammatory
rheumatic disease, 1,002 had systemic lupus erythematosus (SLE), and 2,674 had fibromyalgia. In contrast to unselected patients from rheumatology practices, 7,597 patients with RA were enrolled specifically as part of a
treatment safety registry. Safety registry patients (a subset
of the 22,131 RA study patients) were enrolled in their
rheumatologist’s office at the time they started a specific
therapy. Diagnostic groups were mutually exclusive.
Three hundred thirty-three patients with both RA and SLE
or with both SLE and fibromyalgia were excluded from the
study. Patients with RA could have other conditions with
the exception of SLE.
Patients were enrolled continuously beginning in 1999
and ending in January 2008. Rheumatic disease diagnoses
were made or confirmed by the patient’s rheumatologist.
Noninflammatory rheumatic diseases included diagnoses
such as osteoarthritis, back pain syndromes, tendonitis,
etc., excluding fibromyalgia. Study variables were assessed at entry into the NDB and at every subsequent
semiannual questionnaire.
Study variables. Demographic variables included age,
sex, ethnicity, education level, marital status, and total
household income. Work and disablity status variables
were based on patient self-report. Specifically, we asked
patients which of the following categories best characterized their current work status: paid work, housework,
student, unemployed, disabled, or retired. Validation
studies have demonstrated the reliability of the work and
disability assessments (16).
Questions relating to depression and mood included
self-reported depression and the Medical Outcomes Study
Short Form 36 (SF-36) mood and mental component summary (MCS) scales (17). We assessed self-reported depression by a single question in each semiannual survey (paraphrasing from a table of questions), “Have you had a
problem with depression in the last 6 months.” As evi-
Depression Prediction in RA
Table 1. Prevalence of self-reported depression in RA
and other rheumatic disease*
Group
N
Percentage (95% CI)
Fibromyalgia
SLE
RA
NIRD
2,674
1,002
22,131
3,717
37.9 (36.1–39.8)
32.5 (29.6–35.4)
15.2 (14.7–15.7)
14.7 (13.6–15.8)
* RA ⫽ rheumatoid arthritis; 95% CI ⫽ 95% confidence interval;
SLE ⫽ systemic lupus erythematosus; NIRD ⫽ noninflammatory
rheumatic disease.
dence of validity, self-reported depression was significantly associated with the SF-36 mood and MCS scale. The
area under the receiver operating characteristic (ROC)
curve for mood was 0.826 and Kendall’s tau-a was 0.164;
the area under the ROC curve for the MCS scale was 0.823
and Kendall’s tau-a was 0.166.
Comorbidity was measured by a patient-reported composite comorbidity score (range 0 –9) comprised of 11
present or past comorbid conditions including pulmonary
disorders, myocardial infarction, other cardiovascular disorders, stroke, hypertension, diabetes mellitus, spine/hip/
leg fracture, depression, gastrointestinal ulcer, other gastrointestinal disorders, and cancer (18,19). For the
purposes of this study, self-reported depression was omitted from the scale so that it would only assess nondepressive comorbid conditions. As a consequence, the range in
this study was 0 – 8.
Questions related to RA severity included the Health
Assessment Questionnaire (HAQ) disability index (20,21),
visual analog scales (VAS) for pain and patient global
severity, the Patient Activity Scale (PAS) (22), the Regional
Pain Scale (RPS) (23), and the Symptom Intensity (SI) scale
(24). The PAS is formed by multiplying the HAQ score by
3.33 and then dividing the sum of the VAS pain, the VAS
global, and the HAQ score by 3. This yields a 0 –10 scale.
The PAS is a composite patient measure of RA activity.
The RPS is a self-administered count of the number of
painful nonarticular regions with a range of 0 –19. The SI
scale measures the combination of fatigue and pain extent.
Derived from the fatigue VAS and the RPS, the SI scale
combines these 2 measures in a continuous form according
to the following formula: (VAS fatigue ⫹ [RPS/2])/2. This
yields a scale with a 0 –9.75 range.
To ascertain death status, we searched the National
Death Index (25) annually for deaths between 1998
through 2007 and also received reports of deaths from
family and friends.
Statistical analyses. The cross-sectional prevalence of
self-reported depression was calculated at a randomly selected observation, assuming a Poisson distribution (Table
1). Differences between patients reporting and not reporting depression were examined at a randomly selected observation using t-tests and chi-square tests as appropriate
(Table 2).
Kaplan-Meier survival estimates were used to determine
the annual rate of self-reported depression, after excluding
patients who reported depression on entry (Figure 1). The
Wolfe and Michaud
669
Table 2. Demographic and clinical characteristics of depressed and nondepressed
patients with RA*
Categories and variables
Demographic
Age, years
Sex, % male
Education, years
Married, %
White, %
RA duration, median years
Psychological status
SF-36 mood scale
SF-36 MCS score
Antidepressant use, %
Income and work status
Total income
Employed, %
Disabled (self-report), %
Comorbidity index (range 0–9)
RA severity variables
Global severity (0–10)
Pain (0–10)
HAQ DI (0–3)
Patient activity score (0–10)
Fatigue (0–10)
Regional pain score (0–19)
Symptom intensity scale (0–10)
Treatment, % patients
Biologic therapies
DMARDs
Prednisone
NSAIDs
Opioids
Depressed
(n ⴝ 3,364)†
Not depressed
(n ⴝ 18,767)†
57.2 ⫾ 13.0
15.7
13.0 ⫾ 2.3
68.7
95.6
12.2
61.6 ⫾ 13.5
24.1
13.2 ⫾ 2.3
72.1
95.9
12.6
52.3 ⫾ 19.6
38.2 ⫾ 10.8
35.9
76.4 ⫾ 16.4
52.5 ⫾ 10.1
8.5
$39,258 ⫾ $28,609
26.5
31.4
1.9 ⫾ 1.6
$45,200 ⫾ $29,091
33.0
12.9
1.3 ⫾ 1.3
5.1 ⫾ 2.5
5.6 ⫾ 2.8
1.5 ⫾ 0.7
5.2 ⫾ 2.1
6.5 ⫾ 2.7
8.6 ⫾ 5.8
5.4 ⫾ 2.3
3.5 ⫾ 2.5
3.8 ⫾ 2.8
1.1 ⫾ 0.7
3.6 ⫾ 2.2
4.3 ⫾ 2.9
5.1 ⫾ 4.9
3.4 ⫾ 2.3
40.6
70.6
43.4
60.3
39.6
36.2
76.8
38.1
60.3
21.4
* Values are the mean ⫾ SD unless otherwise indicated. RA ⫽ rheumatoid arthritis; SF-36 ⫽ Short Form
36; MCS ⫽ mental component summary; HAQ ⫽ Health Assessment Questionnaire; DI ⫽ disability index;
DMARDs ⫽ disease-modifying antirheumatic drugs; NSAIDs ⫽ nonsteroidal antiinflammatory drugs.
† By self-report. Differences between groups are significant at P ⱕ 0.5, except for NSAID use (P ⫽ 0.989).
cumulative hazard of self-reported depression was determined by the Nelson-Aalen estimator. Differences between
categories and ordered groups were tested by the log rank
test for ordered trends. To further validate the study mea-
Figure 1. Variable importance of predictors of depression in rheumatoid arthritis in Random Forest analyses ascertained by the
mean decrease in accuracy criterion. SI scale ⫽ Symptom Intensity Scale; RPS ⫽ Regional Pain Scale; PAS ⫽ Patient Activity
Scale; HAQ ⫽ Health Assessment Questionnaire disability index.
sures, we used time-varying Cox regression analysis to
assess the association of self-reported depression, comorbidity, and SI scale with mortality, after adjusting for age
and sex.
Random Forest analysis was used to determine variable
importance and out-of-bag error rates (10,11) using the R
statistical package. The out-of-bag error rate is a robust
measure of misclassification error. Variable importance
is described by the mean decrease in accuracy criterion
(Figure 2), and represents a ranking of variables in terms
of their importance as predictors. Figure 2 shows the 16
best predictors. We also examined the Gini index, but did
not display those results in the figure, as the mean decrease in accuracy is thought to be a better measure.
“. . .the Gini Index reflects the overall goodness of fit,
while the predictive accuracy depends on how well the
model actually predicts. The two are related, but they
measure different things. Breiman argues that the decrease
in predictive accuracy is the more direct, stable and meaningful indicator of variable importance” (personal communication) (26). Classification tree analysis used the rpart
recursive partitioning R analysis programs, and the 1 stan-
670
Depression Prediction in RA
SF-36 MCS score. They tended to be younger, female, with
lower household income, less employment, and greater
work disability. The ethnicity of the total RA sample was
as follows: non-Hispanic white 95.9%, black 1.9%, Asian
0.4%, Native American 0.4%, Hispanic 1.1%, and other
0.24%. With respect to illness characteristics, they had
more comorbidity and worse scores on all RA severity
scales, and they used more opioids, biologic agents, and
corticosteroids but less disease-modifying antirheumatic
drugs (Table 2).
Figure 2. The cumulative risk (solid line) of self-reported depression in rheumatoid arthritis over 9 years of followup. Estimated
cumulative risk at 9 years was 38.3% (95% confidence interval
[95% CI; shaded area] 36.6 – 40.1%).
dard error rule was applied to truncate trees created by
rpart (27).
In addition, we examined the effect of predictor variables on self-reported depression by fractional polynomial
logistic regression using Stata’s MFP (multivariable fractional polynomial models) procedure (28). We used fractional polynomials because the association of some of the
study variables with self-reported depression was nonlinear. To be consistent with the Random Forest analyses, we
used the same predictor variables. Because of colinearity
with PAS and SI scales, we removed the HAQ and fatigue
from the analyses. All data analyses were carried out using
Stata, version 10.0 (28), and the R statistical package (29).
The level of statistical significance was set at 5%, and all
tests were 2-tailed.
RESULTS
Prevalence and incidence of self-reported depression.
The cross-sectional prevalence of self-reported depression
in 22,131 patients with RA was 15.2% (95% confidence
interval [95% CI] 14.7–15.7%) (Table 1). Among the
14,534 patients with RA who were not members of the
drug safety registry, the cross-sectional prevalence was
15.4% (95% CI 14.8 –15.9%). These percentages were similar to the 14.5% noted in patients with noninflammatory
rheumatic disorders, but considerably less than was found
in patients with SLE (32.5%) and fibromyalgia (37.9%).
Among patients who did not report depression on entry
into the study cohort, the annualized incidence rate for
self-reported depression was 5.5 (95% CI 5.3–5.7) per 100
patient years. The estimated cumulative risk of self-reported depression at 9 years of followup was 38.3% (95%
CI 36.6 – 40.1%) (Figure 1).
Characteristics of patients reporting and not reporting
depression. Patients with RA reporting and not reporting
depression at a randomly selected observation point differed in essentially all characteristics, as shown in the
univariate analyses of Table 2. Depressed patients had
considerably greater use of antidepressants (35.9% versus
8.5%) and had worse scores on the SF-36 mood scale and
Predictors of self-reported depression. To better understand these differences, we performed 2 multivariable analyses using the nonpsychological variables shown in Table
2 as predictor variables. In particular, we were interested
in which variables best contributed to identification and
prediction of self-reported depression. Random Forest analyses of variable importance (Figure 2) show that the SI
scale, followed by comorbidity, the RPS, the PAS, the
patient’s global, and fatigue, are the most important variables as judged by reduction in the misclassification rate.
The out-of-bag misclassification rate was 30.4%. We also
applied recursive partitioning to this set of variables. In
these analyses, only the SI scale was required for identification of self-reported depression, and no other variables
were significant in the model by the 1 standard error rule.
Values of the SI scale ⬎3.625 best classified patients as
depressed or not depressed.
In the second multivariable approach, we used fractional polynomial logistic regression to identify nonpsychological predictor variables of self-reported depression
using the variables from Table 2. All variables were significant predictors of self-reported depression except for ethnicity and household income. Interestingly, duration was
a significant predictor in this model after nonlinear transformation, but not in untransformed form. Graphic examination of the effect of this predictor suggested that the
effect of duration on self-reported depression increased for
the first 5 years of RA, after which increasing duration had
no effect. Although it was not a primary importance measure selected for this study, when using the mean decrease
in the Gini coefficient as a criterion, duration of RA ranked
fourth, after SI scale, age, and PAS.
The effect of key predictor variables on the cumulative
risk of self-reported depression. The effect of the 2 major
predictors on the cumulative hazard of self-reported depression is shown in Figures 3 and 4. Figure 3 shows the
cumulative hazard function for 4 categories of comorbidity. Although the risk increases with comorbidity classification, there is a striking increase in the risk associated
with ⱖ4 comorbid conditions.
The best overall predictor of self-reported depression
was the SI scale. The SI scale values for the quartiles for
the SI scale are Q1 0 –1.5, Q2 1.75–3, Q3 3.25–5, and Q4
5.25–9.75. Increasing quartiles were associated with a
striking increase in the cumulative risk of self-reported
depression, as shown in Figure 4.
In time-varying Cox regression analyses, the hazard ratios (HRs) and P values for the association of mortality and
the key study variables for self-reported depression were
Wolfe and Michaud
Figure 3. The effect of each of 5 comorbidity categories on the
cumulative hazard of self-reported depression. The comorbidity
scale categories were significant by the log rank test for ordered
trend, P ⬍ 0.001.
HR 1.3, P ⫽ 0.002, for comorbidity categories were HR 1.3,
P ⬍ 0.001, and for SI scale quartiles were HR 1.4, P ⬍
0.001.
DISCUSSION
There are several important findings in the results of this
study. The first is that the cumulative risk of self-reported
depression in patients with RA is substantial, reaching
almost 40% at 9 years of followup. By contrast, the crosssectional percentage of patients with self-reported depression was only 15.2%. This indicates that many patients
with RA will experience self-reported depression over the
course of RA, but that self-reported depression will also
tend to remit or be intermittent in most instances. This is
in accord with the observations of Katz and Yelin, who
noted that only 4% of patients had persistent depression
(“every year”) (30).
The prevalence of depressive symptoms or self-reported
depression depends on the method of ascertainment and
Figure 4. The effect of each quartile of Symptom Intensity (SI)
scale on the cumulative hazard of self-reported depression. The SI
scale quartiles were significant by the log rank test for ordered
trend, P ⬍ 0.001. Q1 ⫽ 0 –1.5; Q2 ⫽ 1.75–3; Q3 ⫽ 3.25–5; Q4 ⫽
5.25–9.75.
671
the setting. In a sample of 75,858 patients who visited
primary care physicians for any reason, the prevalence of
clinically significant depressive symptoms was found to
be 20.9%, but the percentage of patients citing depression
as a reason for visit was 1.2% (31). From telephone interviews conducted during 1985 in a national probability
sample of 1,232 noninstitutionalized US residents age ⱖ65
years, 9.9% had high depressive symptoms by the Center
for Epidemiologic Studies Depression scale (ⱖ16) (32).
In a meta-analysis of RA depression studies, the increase
of depression in patients compared with healthy controls
was found to be moderate (effect size 0.21) (1). The results
of the current study were similar to those of Katz and
Yelin, who also surveyed a longitudinal sample of patients
with RA. Using the Geriatric Depression Scale, they found
an annualized prevalence of depressive symptoms to be
15–17% (30).
Self-reported depression is not a usual measure in research studies, but it is a reasonable one, representing
patients’ perceptions and depressive symptoms, and it is
related to conventional measures of mood. As noted in
Patients and Methods, self-reported depression and the
SF-36 mood and MCS scales had ROC curve association
values of 0.826 and 0.823, respectively, and we found that
it was associated with mortality. It should be clear, however, that self-reported depression is a measure of depressive symptoms, not of major depression. It should also be
noted that our self-reported depression question might not
identify depressed patients who are being successfully
treated for depression and now considered themselves not
depressed.
Most of the literature about single-item depression questionnaires reviews them with respect to major depression.
In 197 terminally ill cancer patients, a single-item questionnaire correctly identified all depressed patients (33). A
short, 2-question questionnaire had a very high sensitivity
and specificity in patients in multiple sclerosis (34), and a
Veterans Administration single-item screening questionnaire was 88% specific and 78% sensitive (35). However,
in another study, the Yale single-item screening questionnaire had a sensitivity of 65.3% and a specificity of 87.3%
(36) compared with the Beck Depression Index cutoff (37).
Clearly, the cutoff level is important; in the current study
we allowed the patient to determine the cutoff (present or
absent).
The major finding of this study, however, concerns selfreported depression predictors. The most important predictor was the SI scale (Figure 2). The SI scale is a linear
combination of the fatigue VAS and the RPS, each of
which separately was an important predictor of self-reported depression. Widespread pain, the extent of which
is measured by the RPS, is increasingly recognized as a
central rheumatic disease variable. An entirely subjective
variable and a key finding in fibromyalgia, we also found
that it was associated with mortality. The SI scale is associated with severe RA, and with physical and mental distress (24,38). Of particular importance was the result of the
recursive partitioning analyses, which suggested that once
the results of the SI scale were known, other variables did
not increase the ability to correctly classify patients as
depressed or not depressed. However, the result was opti-
672
mized for best overall classification, but not primarily for
self-reported depression sensitivity. In clinical practice,
other variables will also be of interest, particularly comorbidity.
Comorbidity was the second most important predictor of
self-reported depression. It, too, was associated with mortality. Next in rank were the RPS, PAS, and patient global
and fatigue scales (Figure 2). As noted above, the duration
of RA was the fourth most important variable after SI scale,
age, and the PAS when the Gini index method of assessing
importance was used. However, the importance indicated
by this method appeared to be determined by the association of self-reported depression and duration only within
the first 5 years. Therefore it appears that the default
method is more clinically valid and meaningful (Figure 1).
This is in accord with Breiman’s comments favoring the
reduction in accuracy method (27).
Although many factors contribute to self-reported depression, the most important factors are severity measures,
as perceived and reported by the patients, followed by
comorbidity. Further insight into the effect of RA severity
comes from the predictive value of the use of opioids,
disabled status, and reduced income, which is often the
result of RA (39). The component played by demography
can be seen in the contribution of younger age, reduced
income, and marital status; however, ethnicity and education were not important predictors in these analyses. Ethnicity has been shown to be important in other studies, in
which depression was increased among Hispanics and
blacks, but decreased in Asians (40). However, there were
not enough minority subjects in the current study to adequately investigate ethnicity. Symptoms may also contribute to self-reported depression indirectly. Pain and fatigue
may contribute to depression beyond their physical/subjective manifestations. For example, they could have disruptive effects on social relationships and life activities
that might, in turn, contribute to depression. However, we
were not able to evaluate this in the current study.
In speaking of prediction, we note that there are 2 types,
prediction with respect to causal factors, and prediction
with respect to case identification. The model we have
presented here is a clinical model that is concerned with
case identification, not causality. Not only are we not able
to measure traumatic experiences, genetic factors, temperament, interpersonal relations (6), family vulnerability (7),
and HPA axis dysregulation (8), but we cannot always
clearly distinguish cause and effect in the variables included in our model. Depression may increase pain and
fatigue, just as pain and fatigue may increase the likelihood of depression (4,41). In some cases, self-reported
depression may have started before covariate assessment.
However, there are still useful lessons here for the clinician. Pain extent (widespread pain) and fatigue, particularly in combination as in the SI scale, are very important
clinical variables that should be assessed in the clinical
interview. Although most medical records contain information on comorbidity, actual physician familiarity with
the extent of an individual patient’s comorbidities will
provide important insights into the patient’s global health
and associated perceptions.
Depression Prediction in RA
AUTHOR CONTRIBUTIONS
Dr. Wolfe had full access to all of the data in the study and takes
responsibility for the integrity of the data and the accuracy of the
data analysis.
Study design. Wolfe, Michaud.
Acquisition of data. Wolfe, Michaud.
Analysis and interpretation of data. Wolfe, Michaud.
Manuscript preparation. Wolfe.
Statistical analysis. Wolfe.
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