Adjusted treatment COMPArisons between guSelkumab and uStekinumab for treatment of moderate-to-severe plaque psoriasis: the COMPASS analysis.

BACKGROUND: Guselkumab is an interleukin-23 inhibitor indicated for the treatment of moderate-to-severe plaque psoriasis in adults. Guselkumab has demonstrated additional benefit in patients with early inadequate response to ustekinumab. Long-term efficacy comparisons of guselkumab and ustekinumab are currently lacking among ustekinumab-naive patients. OBJECTIVES: To assess the relative efficacy of guselkumab and ustekinumab for maintenance therapy of moderate-to-severe plaque psoriasis, using individual patient data (IPD) from randomized controlled trials. METHODS: IPD for guselkumab from the VOYAGE 1 and 2 trials were pooled and compared with IPD for ustekinumab from the NAVIGATE trial. Multivariable logistic regression analyses compared guselkumab 100 mg and ustekinumab 45 mg or 90 mg for the achievement and maintenance of Psoriasis Area and Severity Index (PASI) 90, 75 and 100 responses up to 40 weeks. The regression models accounted for a range of clinically relevant covariates (e.g. age, sex, psoriasis duration). Relative efficacy was expressed using odds ratios (ORs) and predicted probability of treatment response associated with each intervention. RESULTS: Patients receiving guselkumab had significantly higher probabilities of achieving a PASI 90 response than patients receiving ustekinumab, at both week 16 [70·4% vs. 46·0%, OR 2·79, 95% confidence interval (CI) 2·22-3·45] and week 40 (74·2% vs. 54·5%, OR 2·40, 95% CI 1·89-3·13]. Guselkumab was also associated with a significantly increased likelihood of achieving both PASI 75 and PASI 100 responses at weeks 16 and 40, compared with ustekinumab. CONCLUSIONS: Adjusted analyses leveraging IPD demonstrate that guselkumab has a significantly higher probability of achieving and maintaining PASI treatment responses through week 40 than ustekinumab does.

Real-time parameter estimation of Zika outbreaks using model averaging.

Early prediction of the final size of any epidemic and in particular for Zika disease outbreaks can be useful for health authorities in order to plan the response to the outbreak. The Richards model is often been used to estimate epidemiological parameters for arboviral diseases based on the reported cumulative cases in single- and multi-wave outbreaks. However, other non-linear models can also fit the data as well. Typically, one follows the so called post selection estimation procedure, i.e., selects the best fitting model out of the set of candidate models and ignores the model uncertainty in both estimation and inference since these procedures are based on a single model. In this paper we focus on the estimation of the final size and the turning point of the epidemic and conduct a real-time prediction for the final size of the outbreak using several non-linear models in which these parameters are estimated via model averaging. The proposed method is applied to Zika outbreak data in four cities from Colombia, during the outbreak ocurred in 2015-2016.