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Corrections to the article "Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches" that was published in a 2014 issue are presented.

Objective: Rheumatoid arthritis (RA) is an autoinflammatory disease caused by genetic susceptibility and environmental triggers, which include infectious agents. Helicobacter pylori, a bacterium that frequently colonizes the stomach, is associated with the development of certain autoinflammatory disorders. This study examined a possible association between H. pylori infection and RA.Method: This cohort study was performed in the Central Denmark Region. Patients were enrolled from primary healthcare centres after a urea breath test (UBT) for H. pylori and followed for a median of 8 years. Nationwide administrative registries provided information about the patients' diagnoses, country of birth, and gender. Comorbidity was determined using the Charlson Comorbidity Index. We compared the prevalence of RA via odds ratios (ORs) and incidences using Cox regression to calculate the hazard ratios (HRs) by comparing H. pylori-positive and H. pylori-negative individuals and adjusting for confounding variables.Results: A total of 56 000 people diagnosed as H. pylori positive or negative had similar rates of comorbidity. No link was found between H. pylori and RA. There was no difference in RA prevalence until time of UBT [OR = 0.91, 95% confidence interval (CI) 0.70-1.19)] or incidence of new RA cases after UBT (HR = 0.80, 95% CI 0.56-1.13) between H. pylori-positive and -negative subjects. Validation via four other RA definitions provided similar results.Conclusion: This study found no association between H. pylori infection and RA. This result does not support the involvement of H. pylori in a gut-joint axis of importance for RA development. [ABSTRACT FROM AUTHOR]

PharmML is an XML-based exchange format created with a focus on nonlinear mixed-effect (NLME) models used in pharmacometrics, but providing a very general framework that also allows describing mathematical and statistical models such as single-subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility. [ABSTRACT FROM AUTHOR]

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps. [ABSTRACT FROM AUTHOR]