Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

Background and aimsDespite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.Methodology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use.Results188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1–10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19–1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79–2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66–0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58–0.95; p:0.017).ConclusionIn spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.