Centre effects and case-mix in early rheumatoid arthritis observational cohorts: a narrative review

Objectives Observational cohort studies in early RA are a key source of evidence, despite inconsistencies in methodological approaches. This narrative review assesses the spectrum of methodologies used in addressing centre-level effect and case-mix adjustment in early RA observational cohort studies. Methods An electronic search was undertaken to identify observational prospective cohorts of >100 patients recruited from two or more centres, within 2 years of an RA or early inflammatory arthritis diagnosis. References and author publication lists of all studies from eligible cohorts were assessed for additional cohorts. Results Thirty-four unique cohorts were identified from 204 studies. Seven percent of studies considered centre in their analyses, most commonly as a fixed effect in regression modelling. Reporting of case-mix variables in analyses varied widely. The number of variables considered in case-mix adjustment was higher following publication of the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement in 2007. Conclusion Centre effect is unreported or inadequately accounted for in the majority of RA observational cohorts, potentially leading to spurious inferences and obstructing comparisons between studies. Inadequate case-mix adjustment precludes meaningful comparisons between centres. Appropriate methodology to account for centre and case-mix adjustment should be considered at the outset of analyses.

Investigating covariate-by-centre interaction in survival data

In survival analysis, assessing the existence of potential centre effects on the baseline hazard or on the effect of fixed covariates on the baseline hazard, such as treatment-by-centre interaction, is a frequent clinical concern in multicentre studies. Survival models with random effects on the baseline hazard and/or on the effect of the covariates of interest have been largely applied, for instance, to investigate potential centre effects. We aimed to develop a procedure to routinely test for multiple random effects in survival analyses. We propose a statistic and a permutation approach to test whether all or a subset of components of the variance-covariance matrix of random effects are non-zero in a mixed-effects Cox model framework. Performances of the proposed permutation tests are examined under different null hypotheses corresponding to the different components of the variance-covariance matrix, i.e ., to the different random effects considered on the baseline hazard and/or on the covariates effects. Several alternative hypotheses are evaluated using simulations. The results indicate that the permutation tests have valid type I error rates under the null and achieve satisfactory power under all alternatives. The procedure is applied to two European cohorts of haematological stem cell transplants in acute leukaemia to investigate the heterogeneity across centres in leukaemia-free survival and the potential heterogeneity in prognostic factors effects across centres.