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Keywords:

  • Physical disability;
  • Rheumatoid arthritis;
  • Disease-specific health measures;
  • Generic health measures;
  • Outcome assessment;
  • Factor analysis

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Objective

To measure physical disability in rheumatoid arthritis (RA) using a latent variable derived from a generic and a disease-specific self-reported disability instrument and an observer-assessed functional status scale.

Methods

Consecutive patients with RA completed the modified Health Assessment Questionnaire (M-HAQ) and the Short Form 36 (SF-36) physical function scale. An observer assigned a Steinbrocker functional classification. We used principal component factor analysis to extract a latent variable from the 3 scales. We used the Bayesian Information Criterion to compare how well the new latent variable and the 3 primary scales fit the criterion standards of current work status; vital status at 6 years; grip strength; walking velocity; the timed-button test; pain; and joint tenderness, swelling, and deformity.

Results

Complete data were available for 776 RA patients. The extracted latent variable explained 75% of the variance in the 3 primary scales. On a scale of 0–100, higher scores representing less disability, its mean ± SD was 56.4 ± 22.5. Correlation between the latent variable and the M-HAQ was −0.87; between the latent variable and SF-36 physical function scale was 0.89, and between the latent variable and Steinbrocker class was −0.85. Multivariate models that included the latent variable had superior fit than did models containing the primary scales for the criteria of current working; death by 6 years; pain; joint tenderness, swelling, or deformity; grip strength; walking velocity; and timed button test.

Conclusion

A latent variable derived from the M-HAQ, the SF-36 physical function scale, and the Steinbrocker functional class provides a parsimonious scale to measure physical disability in RA. The fit of the latent variable to comparison standards is equivalent or superior to that of the primary scales.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

The rheumatic disease process frequently leads to physical disability (1). Researchers aiming for a better understanding of rheumatoid arthritis (RA) outcome must first quantify it in a meaningful and reliable way. However, physical disability is a hypothetical construct, i.e., it was put together by scientists to explain the decline in the ability to perform physical activities that can occur in RA and other diseases (2). This implies that physical disability cannot be directly observed or measured, and as such, it can be considered a latent variable (3). Available measurement tools to assess physical disability in RA indirectly tap into the underlying construct (3).

To measure disability in RA, researchers have a variety of instruments and scales from which to choose (4). Some of these are considered arthritis-specific because they center on outcomes more immediately relevant to arthritis (5–7). Generic scales, on the other hand, measure more global outcomes and are suitable for studying a diversity of diseases (8). Each has its own set of advantages (9–12). Some empirical studies, however, have not found major differences in performance between the 2 types of scales (13, 14). The choice between one type over the other not being clear cut, some authorities reasonably advocate including both an arthritis-specific and a generic outcome measure in RA trials (9, 15). This recommendation has the added benefit that 2 or more measurement tools will provide a more reliable representation of the underlying construct (16).

However, not much attention has been given to how to report the results of studies that include 2 or more outcome measures of the same construct. The option of describing results on both scales separately in the same, or different, reports has certain disadvantages, including the need to conduct separate, parallel analyses; a greater potential for type I errors due to multiple comparisons; the added space needed to show results fully; enticement to duplicate publication; and problems of interpretation if results on the different scales diverge. When the last of these occurs, investigators may be lured into simply omitting results on the scale that do not fit their hypotheses. These potential problems, theoretical or real, can be averted by a data-reduction process aimed at estimating the underlying latent variable, conserving or enhancing information provided by the various scales.

We have confronted some of the above dilemmas during an ongoing study of the disablement process in RA. We selected 2 self-report scales, one generic and the other disease-specific, and an observer-derived classification system to assess the extent of physical disability in RA. In this article, we describe the data-reduction process we utilized to derive a parsimonious, single variable representing the construct of physical disability. We also show evidence of its equivalence, or superiority, to the 3 primary scales.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

Patients

From 1996 to 2000 we enrolled patients meeting the 1987 American College of Rheumatology (formerly American Rheumatism Association) RA criteria (17) into a study of the disablement process in RA (18). We have described our sample in previous publications (18–21). The study's acronym, ÓRALE (Outcome of Rheumatoid Arthritis Longitudinal Evaluation), matches a Mexican American idiom for “Lets go!” Here we will show cross-sectional results obtained during the recruitment evaluation of each participant.

Data collection procedures

Our study was approved by the Institutional Review Board of each of the recruitment centers and all patients gave written, informed consent. A physician or a research nurse, assisted by a trained research associate, conducted evaluations at the clinic where the patient was recruited. The evaluation lasted ∼90 minutes and consisted of a comprehensive interview, physical examination, review of available medical records, laboratory tests, and radiographs. Interviews were conducted in either English or Spanish, as preferred by patients.

Demographics

We ascertained age, sex, and race/ethnicity by self report, as described previously (20).

Musculoskeletal examination

A physician or research nurse, trained in joint examination techniques, assessed 48 joints in each patient for the presence or absence of tenderness or pain on motion, swelling, or deformity, as described elsewhere (22).

Pain

We asked patients to rate the amount of pain they experienced due to their arthritis during the past week on a graded, horizontal 10-point scale that has been validated in our patient population (23).

Performance-based functional measures

We measured grip strength with a hand-held JAMAR dynamometer (Sammons Preston, Bolingbrook, IL). In a sitting position, with the elbow held at 90° and the forearm supported on a flat horizontal surface, patients were asked to squeeze the handle with as much as strength as possible. Three repetitions from each hand were recorded in kilograms. The mean value of all repetitions for both hands is shown.

Walking velocity was measured with patients starting in a standing position. They were asked to walk at their usual pace for a distance of 50 feet, or 25 feet if they had difficulty covering the full distance. No effort was made to conceal the stopwatch used to time the patients. Results are expressed in feet per second. Patients unable to walk were assigned a velocity of 0 feet per second.

Patients were timed as they donned and fastened the front buttons in a standard 8-button shirt (Wal-Mart, San Antonio, TX). Results are expressed as buttons per second. Patient unable to don the shirt were assigned a value of 0 buttons per second.

Physical disability measures

We used 3 instruments to measure physical disability. The disability index of the modified Health Assessment Questionnaire (M-HAQ) is a self-administered, arthritis-specific instrument that asks respondents to rate the amount of difficulty they have performing 8 activities (dressing, getting out of bed, lifting a cup, walking, bathing, bending, turning faucets, and getting in and out of a car) on a scale ranging from 1 to 4 (without difficulty, with some, with much, and unable) (24). We used a cross-culturally equivalent Spanish version for our Spanish-speaking patients (23). The Short Form 36 (SF-36) physical functioning scale (SF-36PF) is an interviewer-administered generic instrument (8). The SF-36PF asks respondents to rate the amount of limitation caused by health on 10 physical activities (vigorous activities; moderate activities; carrying groceries; climbing several or 1 flight of stairs; bending; kneeling or stooping; walking more than a mile; walking several blocks or 1 block; bathing; and dressing). Respondents rate each activity on a 3-level scale (a lot of difficulty, a little, no difficulty). Individual responses were summed, and the sum was rescaled to range. The Steinbrocker functional classification was used by the physician or research nurse, who were trained in physical function assessment, to rate the extent of physical disability on a 4-level scale, ranging from class I, “complete functional capacity to carry out all usual duties without handicaps,” to class IV, “largely or wholly incapacitated with (the person) bedridden or confined to wheelchair” (25). We used each of these 3 scales as intended when they were originally developed, scoring them as recommended by their original authors.

Work status

We asked patients to describe their current work status from among the following answers: working full or part time, retired, student, housewife, unemployed/laid off, or disabled/unable to work. We used these responses for 2 sets of analyses: For the first, we classified patients as working (full or part time) versus not working (all others); for the second, we classified patients as disabled/unable to work versus all others.

Vital status

We have recontacted the patients at yearly intervals since their initial evaluation. For patients with whom we were not able to establish contact, even through family members, we searched publicly available death registries. We obtained a death certificate for all patients who died.

Statistical analysis

We performed a principal component factor analysis using the composite summary scores of the M-HAQ, SF-36PF, and the Steinbrocker functional class, and then extracted the first principal component from the unrotated factor loadings using the least squares regression method (26). We rescaled the extracted factor to range from 0 to 100 with a positive valence, higher values representing less disability. To evaluate the degree of bivariate association between the new latent variable and other study variables with interval or ratio distributions, we used Pearson product moment correlation coefficients (27). For the Steinbrocker functional class, a 4-level ordinal scale, we used the square root of the multiple R2 from a regression model that included dummy variables for each Steinbrocker level instead of the Pearson coefficient. Differences between the coefficients were tested after Fisher z-transformation (28) using the procedure provided by Goldstein (29). Because this required us to perform a total of 21 correlation coefficient comparisons, we only considered coefficients to be significantly different if the comparison P value was ≤ 0.002, adjusted according to the Bonferroni technique (the conventional α = 0.05 ÷ number of comparisons = 21). To evaluate the latent variable's association with categorical criterion variables, we divided the latent variable into ordinal categories and used chi-square to test the strength of association (27). We then evaluated the fit of multivariate models that included the new latent variable compared with models that included the primary variables. We asked the question: Does a multivariate model that includes the new latent variable fit the criterion standards better than models that include any of the primary variables? We included age and sex as covariates in all these multivariate models because they can have a strong influence on any of the criterion measures we used. The general form of the models we compared was

  • equation image

where y could be any of the criterion standards (working status, vital status, grip strength, etc.), a was age, b was sex, and pd was 1 of the 4 physical disability scales (M-HAQ, SF-36PF, Steinbrocker class, or the new latent variable). When y was a categorical variable, the model was a logistic regression; and when y was an interval or ratio variable, the model was ordinary least squares regression. We expected that the fit of a multivariate model including the new latent variable on any of the criterion standards would be equivalent or superior to the fit of models that include any of the 3 primary variables. We used the Bayesian Information Criterion (BIC) to confirm this expectation (30). The BIC varies inversely with a model's fit, and given 2 models, the one with the smaller or more negative BIC has better fit (30). We used Raftery's guidelines to interpret BIC differences between 2 models: A BIC difference >10 is considered “very strong” evidence in favor to the model with the smaller BIC; a difference of 6–10 is “strong;” 2–6 is “positive,” and 0–2 is “weak” evidence (30). We performed all analyses on a desktop personal computer, using the Stata 7.0 software package (College Station, TX).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

As expected for a group of people with established RA visiting a rheumatologist, most were women, median disease duration was 8 years, and rheumatoid factor was present in the majority (Table 1). The median number of 8 deformed joints indicates a substantial amount of joint damage (22). In accord with this finding, only 21% of the patients were working full or part time, and 27% stated they were unable to work. Of the 756 patients on whom we had followup information up to 6 years later, 71 were known to have died (9%).

Table 1. Clinical characteristics of the 776 RA patients studied*
CharacteristicNo. with data availableDistribution
  • *

    RA = rheumatoid arthritis; MHAQ = Modified Health Assessment Questionnaire; SF-36 = Short Form 36.

Age, median (range), years77657 (19–90)
Male, no. (%)776229 (30)
Ethnic group, no. (%)776 
 White 272 (35)
 Black 53 (7)
 Asian 14 (2)
 Hispanic 431 (56)
 Other 6 (1)
Education, median (range), years77212 (0–17)
Currently working, no. (%)776166 (21)
Disabled for work, no. (%)776213 (27)
Time from disease onset, median (range), years7768 (0–52)
Tender joint count, no. (%)77615 (13)
Swollen joint count, no. (%)7767 (7)
Deformed joint count, no. (%)77610 (11)
Nodules, no. (%)776233 (30)
Rheumatoid factor positive, no. (%)770682 (89)
Walking velocity, mean ± SD, meters/minute77559 ± 25
Grip strength, mean ± SD, lbs77614 ± 10
Button test, mean ± SD, buttons/minute7697.1 ± 3.8
MHAQ, mean ± SD7761.89 ± 0.70
SF-36, mean ± SD77635.6 ± 27.87
Steinbrocker functional class, mean ± SD776 
 I 163 ± 21
 II 383 ± 49
 III 190 ± 24
 IV 40 ± 5
Latent Disability Scale, mean ± SD, lbs77656 ± 23
Deaths as of March 2002, no. (%)75671 (9)

Figure 1 is a diagram of the factor analysis we used to derive the physical disability latent variable. The 3 primary variables, M-HAQ, SF-36PF, and Steinbrocker class, loaded strongly on a single factor, with loadings ≥0.8. This factor explained ≥75% of the primary variables' combined variance. Uniqueness values were <0.3 for each of the primary variables, indicating that these share more than two-thirds of their combined variance. We extracted the single factor without rotation, using linear regression scoring. Figure 2 shows probability distributions for the 3 primary scales and the latent variable.

thumbnail image

Figure 1. Diagram of the factor analysis we conducted to extract the latent variable measuring physical disability. The 3 primary variables are represented by squares and the circles represent information outside the latent variable. M.H.A.Q. = modified Health Assessment Questionnaire; SF-36 = Short Form 36.

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thumbnail image

Figure 2. Frequency distributions of the disability scales employed. Disability level decreases from left to right. A large proportion of patients had a score of 1 on the modified Health Assessment Questionnaire (M.H.A.Q.), indicating low disability levels on this scale (top left). However, the opposite is true for the Short Form 36 physical function (SF36PF) scale, in which the largest category is made up of patients with low scores, indicating high disability levels (top right). The Steinbrocker functional class provides only 4 levels to classify physical disability (lower left). The distribution of scores on the latent disability scale approached normality (lower right).

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The Pearson correlation coefficients between the extracted latent variable and the primary variables, as expected, was also strong, with r values ≥0.8. Figure 3 shows scatterplots of the bivariate distribution of these variables. The correlations between the latent variable and the criterion variables (pain, joint tenderness, swelling or deformity, grip strength, walking velocity, and the timed button test) are shown in Table 2, contrasted with the correlation coefficients between the primary scales and the same criterion standards. The latent variable had a significantly stronger correlation with most of the criterion standards than did the primary variables M-HAQ, SF-36PF, and Steinbrocker class. Notable exceptions were the correlation with the pain and articular examination variables, for which there was no significant difference between the M-HAQ and the latent variable. Also interestingly, the number of deformed joints correlated more strongly with Steinbrocker class that with any of the other physical disability scales.

thumbnail image

Figure 3. Matrix plot showing the bivariate distribution of the 3 primary variables and the latent variable. The Pearson correlation coefficient between the latent variable and the modified Health Assessment Questionnaire was −0.87; between the latent variable and Short Form 36 physical function scale (SF-36PF) was 0.89; and between the latent variable and the Steinbrocker class was −0.85. All coefficients were significant at P ≤ 0.0001.

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Table 2. Correlation between physical disability scales and variables measured as criterion standards*
 MHAQSF-36PFSteinbrocker classLatent variable
  • *

    Pearson correlation coefficients were compared after Fischer z-transformation, after equalizing coefficient signs (28, 29). MHAQ = Modified Health Assessment Questionnaire; SF-36PF = short form 36 physical functioning scale.

  • Significance of comparisons versus latent variable was set at P ≤ 0.002.

Pain0.59−0.530.41−0.59
Tender0.49−0.430.33−0.47
Swollen0.24−0.220.22−0.24
Deformity0.20−0.250.52−0.35
Grip−0.540.520.480.59
Velocity−0.610.650.670.72
Button−0.540.550.600.64

Figure 4 shows the relationship between the latent variable and selected comparison criteria. These graphs show the association between higher values in the latent variable and graded decreases in the number of deformed joints, the proportion of disabled patients, and the proportion of those who died within 6 years. Conversely, performance-based functional measures (grip strength, timed button test, and walking velocity) displayed a proportional rise with increasing values on the latent scale, as did the probability of working full or part time.

thumbnail image

Figure 4. Relationship between the latent variable measuring physical disability and the criterion measures deformed joint count (top left; trend P ≤ 0.001); walking velocity, grip strength, and timed button test (top right; trend P ≤ 0.001 for each variable); work disability and death within 6 years (bottom left; trend P ≤ 0.001 for each); and currently working (bottom right; trend P ≤ 0.001). Error bars represent standard error.

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Table 3 shows the BICs of models that contained age, sex, and each of the 4 disability scales (the M-HAQ, SF-36PF, Steinbrocker class, and the latent variable) as independent variables for each of the criterion standards. For most of the criterion standards, the BIC was smaller, indicating better fit, in the models that included the latent variable (Table 3). Notable exceptions, again, included the Steinbrocker class, whose model had a better fit versus the deformed joint count than did any of the other physical disability scales. Likewise, there was positive evidence that the SF-36PF fit better in a model for disabled work status, than did any of the other physical disability scales.

Table 3. Bayesian information criterion of multivariate models, according to physical disability scale used as independent variable*
Dependent variablePhysical disability scale included as independent variable in multivariate model
MHAQSF-36PFSteinbrocker classLatent variable
  • *

    Values shown are Bayesian information criteria. MHAQ = Modified Health Assessment Questionnaire; SF-36PF = Short Form 36 physical functioning scale.

  • Model's form was y = age + sex + physical disability scale, where y = dependent variable. For current working, currently disabled, and death by 6 years, the model was logistic; for other variables, model was ordinary least squares.

  • Extracted from a principal component factor analysis of MHAQ, SF-36PF and Steinbrocker class (Figure 1).

  • §

    Very strong support for model that includes the latent variable.

  • Positive support for model that includes the latent variable.

  • #

    Strong support for model that includes the latent variable.

Currently working−4,429§−4,447−4,432§−4,451
Currently disabled−4,288§−4,308−4,266§−4,303
Death within 6 years−4,782#−4,780§−4,775§−4,791
Pain−1,588§−1,531§−1,414§−1,602
Tender joint count809#855§933§818
Swollen joint count−8−57§−14
Deformed joint count662§661§519607
Grip strength248§261§306§170
Walking velocity1,654§1,609§1,660§1,466
Timed button test−7,501§−7,465§−7,478§−7,597

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

One desirable characteristic of research data is parsimony, or simplicity of explanation (31). Under this principle, one variable is preferable to 2 or more, providing that the single variable is as informative as the 2 or more. We have shown evidence that a single latent variable derived from principal component factor analysis of 3 scales, the M-HAQ, the SF-36PF, and the Steinbrocker functional class, has equal or superior performance to the primary scales, as manifested by an equal or stronger degree of association with the criterion standards we selected. We used the disablement process as a theoretical framework to inform our selection of criterion standards (18, 32, 33), aiming to test the underlying physical disability construct from as many perspectives as possible. Thus, our comparison criteria included key RA impairments, such as the amount of pain and the number of tender, swollen, and deformed joints (33). We also used measures of functional limitation, occupational status, and death within 6 years as criteria.

The correlation between the joint impairments and the latent variable was nearly always stronger than that between the same impairments and the primary disability scales (Table 2). This likely is due to the superior reliability of the latent variable, which is a composite of the 3 primary disability scales. This approach has been referred to as incomplete principal component regression because the variable of interest is provided by the first principal component in a factor analysis (34). The composite measure's stronger correlation with most criterion standards conforms to a fundamental theorem of measurement theory

  • equation image

according to which the correlation between 2 variables, x and y is limited by the square root of the product of each variable's reliability (16). However, there were 2 comparisons that did not follow this rule: The M-HAQ correlated equally strongly as the latent variable with the impairments; and the Steinbrocker class correlated more strongly with the number of deformities than did any of the other disability scales, including the latent variable. The reason for this may be that examiners may have incorporated findings from the joint exam into their judgment of the Steinbrocker class. In contrast, the M-HAQ and the SF-36 are self-reported scales that patients answer according their own perceived condition.

We also used 3 performance-based measures of functional limitation: grip strength, walking velocity, and timed button test. Within the disablement process framework, these measurements are closer to the physical disability construct than are the joint impairments (18, 32, 33) and, consequently, their degree of correlation with the disability scales was stronger. Here, even more so than with the impairments, the latent variable's association with the performance-based measures was stronger than that of any of the 3 primary physical disability scales considered individually.

Work loss is one of the main adverse consequences of RA (35). We found that work status was strongly associated with the 4 physical disability scales with a tendency for the association to be stronger for the latent variable. Likewise, death displayed a similar pattern of association. One of the main uses of these comparison standards, occupational and vital status, is as anchors that researchers or clinicians can use to interpret the values along the latent variable scale. As shown in Figure 4, there are strong adverse outcomes associated with lower values for the latent variable.

The physical disability scales we used in the present analyses, including the latent variable we developed, often find use in multivariate models, either as outcomes or predictors. We thus compared the fit of models that included the different physical disability scales as independent variables and each of the different criterion variables as outcomes. Because each of the criteria we used can be heavily influenced by age and sex, we included these 2 variables as covariates in all of the multivariate models. We chose the BIC as a comparative measure because it is a tool used often for model selection (30, 36). We expected that the models that included the new latent variable would have smaller BICs, indicating better fit. Indeed this was the case with nearly all of the criterion variables.

The latent disability variable has distributional advantages over the 3 primary scales. Both the M-HAQ and the SF-36PF display skewed distributions (Figure 2), the former displaying a ceiling effect, the latter, a floor effect (37). The latent scale lacks skewness in either direction, more closely approximating normality than any of the primary scales. Moreover, the latent variable displays an interval or near-interval distribution, as suggested by the monotonic rise in criterion variables as the scale increases (Figure 4).

The latent variable has theoretical advantages as well: Physical disability is a hypothetical construct and claims that any one disability measurement scale is superior to others are debatable. Using more than one measurement tool may be a more accurate way to get at the underlying construct because it enables the unmeasured construct to be assessed from a variety of angles. For this same reason, the idea of using both a scale intended specifically for arthritis and one intended for unselected populations (9, 15) is quite attractive, because the arthritis-specific scale, the M-HAQ in our study, will capture the arthritis-relevant outcomes whereas the generic scale, here provided by the SF-36PF, will capture an overall nonspecific disease impact.

We acknowledge some limitations of our analysis. Factor analysis assumes that data are distributed on interval, multivariate normal scales, an assumption that may not be stringently met by the 3 disability scales we entered into the factor analysis. However, this assumption is a strict requirement only if statistical inference is used to determine the number of factors and can be relaxed when factor analysis is used descriptively (26, 38). The least squares factor extraction method we used is also robust to deviations from normality (39). The M-HAQ and SF-36PF scales we used were developed using sound psychometric theory to produce results on interval or near-interval scales, and they have each been used as such in numerous studies over many years. We used the composite scores of both these scales, scored as originally intended. It is possible, however, to select items from each of these scales and calibrate their weights so that they more closely approximate a true interval or ratio scale by using item response theory or Rasch analysis (40, 41). This may represent an alternative method to accomplish the aims we pursued here.

Data parsimony is a desirable feature in a research study; among other reasons, because it avoids the problems we mentioned in the beginning of this article. In the present analysis, we have reduced the original 3 scales into 1 single variable that in many respects outperforms the individual primary scales. A similar data reduction strategy could be used for other RA processes, such as inflammatory disease activity, disease damage, joint impairment, and functional limitation (32, 33). For example, a latent variable extracted from the disease activity measures recommended for RA clinical trials (42) could potentially lead to more efficient trials if the latent variable outperforms the primary scales, as was the case for disability measures in the present analysis.

It is important to point out that ours is a data-driven approach, and that the latent variable cannot be fully specified as an outcome measure in advance of a study. We do not advise investigators to attempt to directly apply the factor loadings we estimated here to develop a latent disability variable for use in their own studies, because data from another patient sample could be quite different. Moreover, investigators may have reasons to choose a different set of primary disability scales from those used here. We do believe, however, that researchers can apply a principal component factor analysis, similar to that shown here to their own data, to extract a latent variable that will likely exceed the primary scales in reliability.

In conclusion, we have used factor analysis to derive a latent variable that measures physical disability in RA. The new variable outperforms the primary scales in a number of tests of association with comparison criterion standards. This approach may be used to develop latent variables measuring other RA disease components, such as disease activity, damage, and functional limitation.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES

We acknowledge the invaluable assistance of Florencia Salazar and Samvel Pogosian, MD, in the conduct of the ÓRALE study. We also thank Drs. Ramon Arroyo, Daniel Battafarano, Rita Cuevas, Alex de Jesus, Michael Fischbach, John Huff, Rodolfo Molina, Mathew Mosbacker, Frederick Murphy, Carlos Orces, Christopher Parker, Thomas Rennie, Jon Russell, Joel Rutstein, and James Wild for giving us permission to study their patients and for contributing to this study.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. REFERENCES