DAS-28-based EULAR response and HAQ improvement in rheumatoid arthritis patients switching between TNF antagonists

INTRODUCTION No definitive data are available regarding the value of switching to an alternative TNF antagonist in rheumatoid arthritis patients who fail to respond to the first one. The aim of this study was to evaluate treatment response in a clinical setting based on HAQ improvement and EULAR response criteria in RA patients who were switched to a second or a third TNF antagonist due to failure with the first one. METHODS This was an observational, prospective study of a cohort of 417 RA patients treated with TNF antagonists in three university hospitals in Spain between January 1999 and December 2005. A database was created at the participating centres, with well-defined operational instructions. The main outcome variables were analyzed using parametric or non-parametric tests depending on the level of measurement and distribution of each variable. RESULTS Mean (+/- SD) DAS-28 on starting the first, second and third TNF antagonist was 5.9 (+/- 2.0), 5.1 (+/- 1.5) and 6.1 (+/- 1.1). At the end of follow-up, it decreased to 3.3 (+/- 1.6; Delta = -2.6; p > 0.0001), 4.2 (+/- 1.5; Delta = -1.1; p = 0.0001) and 5.4 (+/- 1.7; Delta = -0.7; p = 0.06). For the first TNF antagonist, DAS-28-based EULAR response level was good in 42% and moderate in 33% of patients. The second TNF antagonist yielded a good response in 20% and no response in 53% of patients, while the third one yielded a good response in 28% and no response in 72%. Mean baseline HAQ on starting the first, second and third TNF antagonist was 1.61, 1.52 and 1.87, respectively. At the end of follow-up, it decreased to 1.12 (Delta = -0.49; p < 0.0001), 1.31 (Delta = -0.21, p = 0.004) and 1.75 (Delta = -0.12; p = 0.1), respectively. Sixty four percent of patients had a clinically important improvement in HAQ (defined as > or = -0.22) with the first TNF antagonist and 46% with the second. CONCLUSION A clinically significant effect size was seen in less than half of RA patients cycling to a second TNF antagonist.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.