Is there a role for expectation maximization imputation in addressing missing data in research using WOMAC questionnaire? Comparison to the standard mean approach and a tutorial

<p>Abstract</p> <p>Background</p> <p>Standard mean imputation for missing values in the Western Ontario and Mc Master (WOMAC) Osteoarthritis Index limits the use of collected data and may lead to bias. Probability model-based imputation methods overcome such limitations but were never before applied to the WOMAC. In this study, we compare imputation results for the Expectation Maximization method (EM) and the mean imputation method for WOMAC in a cohort of total hip replacement patients.</p> <p>Methods</p> <p>WOMAC data on a consecutive cohort of 2062 patients scheduled for surgery were analyzed. Rates of missing values in each of the WOMAC items from this large cohort were used to create missing patterns in the subset of patients with complete data. EM and the WOMAC's method of imputation are then applied to fill the missing values. Summary score statistics for both methods are then described through box-plot and contrasted with the complete case (CC) analysis and the true score (TS). This process is repeated using a smaller sample size of 200 randomly drawn patients with higher missing rate (5 times the rates of missing values observed in the 2062 patients capped at 45%).</p> <p>Results</p> <p>Rate of missing values per item ranged from 2.9% to 14.5% and 1339 patients had complete data. Probability model-based EM imputed a score for all subjects while WOMAC's imputation method did not. Mean subscale scores were very similar for both imputation methods and were similar to the true score; however, the EM method results were more consistent with the TS after simulation. This difference became more pronounced as the number of items in a subscale increased and the sample size decreased.</p> <p>Conclusions</p> <p>The EM method provides a better alternative to the WOMAC imputation method. The EM method is more accurate and imputes data to create a complete data set. These features are very valuable for patient-reported outcomes research in which resources are limited and the WOMAC score is used in a multivariate analysis.</p

Variation in symptoms of depression and anxiety in midlife women by menopausal status

Objectives To examine the association between menopausal status and the risk of symptoms of depression and anxiety in a community-based sample of Australian midlife women. Study design Female participants (mean age 50.6 ± 1.5) who were premenopausal (n = 237), perimenopausal (n = 249) or naturally postmenopausal (n = 225) were drawn from the Personality and Total Health (PATH) Through Life Project, a longitudinal study. Main outcome measures Symptoms of depression and anxiety were measured using the Goldberg Depression Scale and Goldberg Anxiety Scale. Generalised linear regression models with a negative binomial log link were used. Results Relative to premenopause and after adjusting for all relevant covariates, being perimenopausal was associated with increased risk of greater symptoms of depression (incidence rate ratio [IRR] = 1.29, p = 0.001), while being postmenopausal was associated with increased risk of greater symptoms of anxiety (IRR = 1.15, p = 0.041). Being perimenopausal or postmenopausal was associated with an increased risk of greater symptoms of depression (IRR = 1.35, p = 0.008; IRR = 1.31, p = 0.029) and anxiety (IRR = 1.22, p = 0.030; IRR = 1.32, p = 0.006) in women without a history of probable major depressive disorder or generalised anxiety disorder. Risk of symptoms did not differ with menopausal status in women with this history. Conclusions Menopausal status is associated with the risk of symptoms of depression and anxiety. There is a greater likelihood of increased symptoms of depression during perimenopause and symptoms of anxiety during postmenopause. In women without a history of depression or anxiety, the perimenopause and postmenopausal stages are associated with increased risk of greater symptoms of anxiety and depression relative to premenopause

Functional improvement after Total Knee Arthroplasty Revision: New observations on the dimensional nature of outcome

<p>Abstract</p> <p>Background</p> <p>Despite the numerous outcomes measures described it remains unclear what aspects of patient outcome are important in determining actual improvement following total knee arthroplasty revisions (TKAR). We performed a prospective cohort study of TKAR to determine the components of clinical improvement and how they are related and best measured.</p> <p>Methods</p> <p>An improvement scale was devised utilizing data from 186 consecutive TKAR patients on SF-36 physical (PCS) and mental (MCS) components, Western Ontario and McMaster Universities Osteoarthritis (WOMAC) Index, Knee Society Score (KSS), a novel Activity Scale (AS) and a physician derived severity assessment scale performed both preoperatively and at 6 month post-operative follow-up. The change in each of these scores was analyzed using factor analysis, deriving a composite improvement scale.</p> <p>Results</p> <p>All the instruments demonstrated statistically significantly better scores following TKAR (except the SF-36 MCS). Furthermore, all significant correlations between the scores were positive. Statistical factor analysis demonstrated that scores could be arranged into 4 related factor groupings with high internal consistency (Cronbach Alpha = 0.7). Factor 1 reflected patient perceived functional outcomes, Factor 2 activity levels, Factor 3 the MCS and Factor 4 the KSS.</p> <p>Conclusion</p> <p>This study demonstrates that improvement following TKAR has a multidimensional structure. The improvement scales represent a more coordinated method of the previously fragmented analysis of TKAR outcomes. This will improve assessment of the actual effectiveness of TKAR for patients and what aspects of improvement are most critical.</p