Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations

Conventional and approximate degrees of freedom procedures for testing multivariate interaction contrasts in groups by trials repeated measures designs were compared under assumption violation conditions. Procedures were based on either least-squares or robust estimators. Power generally favored test procedures based on robust estimators for non-normal distributions, but was influenced by the degree of departure from non-normality, definition of power, and magnitude of the multivariate effect size

A Systematic Review of the Quality of Reporting of Simulation Studies about Methods for the Analysis of Complex Longitudinal Patient-Reported Outcomes Data

International audiencePURPOSE: This study describes the characteristics and quality of reporting for published computer simulation studies about statistical methods to analyze complex longitudinal (i.e., repeated measures) patient-reported outcomes (PROs); we included methods for longitudinal latent variable measurement and growth models and response shift. METHODS: Scopus, PsycINFO, PubMed, EMBASE, and Social Science Citation Index were searched for English-language studies published between 1999 and 2016 using selected keywords. Extracted information included characteristics of the study purpose/objectives, simulation design, software, execution, performance, and results. The quality of reporting was evaluated using published best-practice guidelines. SYNTHESIS: A total of 1470 articles were reviewed and 42 articles met the inclusion criteria. The majority of the included studies (73.8%) investigated an existing statistical method, primarily a latent variable model (95.2%). Most studies specified the population model, including variable distributions, mean parameters, and correlation/covariances. The number of time points and sample size(s) were reported by all studies, but justification for the selected values was rarely provided. The majority of the studies (52.4%) did not report on model non-convergence. Bias, accuracy, and model fit were commonly reported performance metrics. All studies reported results descriptively, and 26.2% also used an inferential method. CONCLUSIONS: While methodological research on statistical analyses of complex longitudinal PRO data is informed by computer simulation studies, current reporting practices of these studies have not been consistent with best-practice guidelines. Comprehensive reporting of simulation methods and results ensures that the strengths and limitations of the investigated statistical methods are thoroughly explored

International Classification of Diseases (ICD)-coded obesity predicts risk of incident osteoporotic fracture

<div><p>International Classification of Diseases (ICD) codes have been used to ascertain individuals who are obese. There has been limited research about the predictive value of ICD-coded obesity for major chronic conditions at the population level. We tested the utility of ICD-coded obesity versus measured obesity for predicting incident major osteoporotic fracture (MOF), after adjusting for covariates (i.e., age and sex). In this historical cohort study (2001–2015), we selected 61,854 individuals aged 50 years and older from the Manitoba Bone Mineral Density Database, Canada. Body mass index (BMI) ≥30 kg/m² was used to define measured obesity. Hospital and physician ICD codes were used to ascertain ICD-coded obesity and incident MOF. Average cohort age was 66.3 years and 90.3% were female. The sensitivity, specificity and positive predictive value for ICD-coded obesity using measured obesity as the reference were 0.11 (95% confidence interval [CI]: 0.10, 0.11), 0.99 (95% CI: 0.99, 0.99) and 0.79 (95% CI: 0.77, 0.81), respectively. ICD-coded obesity (adjusted hazard ratio [HR] 0.83; 95% CI: 0.70, 0.99) and measured obesity (adjusted HR 0.83; 95% CI: 0.78, 0.88) were associated with decreased MOF risk. Although the area under the receiver operating characteristic curve (AUROC) estimates for incident MOF were not significantly different for ICD-coded obesity versus measured obesity (0.648 for ICD-coded obesity versus 0.650 for measured obesity; <i>P</i> = 0.056 for AUROC difference), the category-free net reclassification index for ICD-coded obesity versus measured obesity was -0.08 (95% CI: -0.11, -0.06) for predicting incident MOF. ICD-coded obesity predicted incident MOF, though it had low sensitivity and reclassified MOF risk slightly less well than measured obesity.</p></div

Net reclassification indices (NRIs) and 95% confidence intervals (95%CIs) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.

<p>Net reclassification indices (NRIs) and 95% confidence intervals (95%CIs) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.</p

Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.

<p>Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.</p

Akaike information criterion (AIC) and Bayesian information criterion (BIC) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.

<p>Akaike information criterion (AIC) and Bayesian information criterion (BIC) for incident major osteoporotic fracture (MOF) associated with ICD-coded obesity and measured obesity.</p

Cummulative incidence curves for major osteoporotic fracture (MOF) associated with ICD-coded obesity versus no ICD-coded obesity (left panel) and measured obesity versus no measured obesity (right panel).

<p>Cummulative incidence curves for major osteoporotic fracture (MOF) associated with ICD-coded obesity versus no ICD-coded obesity (left panel) and measured obesity versus no measured obesity (right panel).</p

Flow chart for cohort selection and exclusions.

<p>Flow chart for cohort selection and exclusions.</p

Residential mobility and severe mental illness: a population-based analysis

This research uses population-based administrative data linking health service use to longitudinal postal code information to describe the residential mobility of individuals with a severe mental illness (SMI), schizophrenia. This group is compared to two cohorts, one with no mental illness, and one with a severe physical illness of inflammatory bowel disease. The percentage of individuals with one or more changes in postal code in a 3-year period is examined, along with measures of rural-to-rural regional migration and rural-to-urban migration. Demographic, socioeconomic, and health service use characteristics are examined as determinants of mobility. The odds of moving were twice as high for the SMI cohort as for either of the other two cohorts. There were no statistically significant differences in rural-to-rural or rural-to-urban migration among the cohorts. Marital status, income quintile, and useof physicians are consistent determinants of mobility. The results are discussed from the perspectives of health services planning and access to housing