Background: This study aims to compare nature and frequency of adverse drug reactions (ADRs), time to first ADR, drug survival, and the share of ADRs in treatment discontinuation of first-time treatment with adalimumab (ADA) and etanercept (ETN) in real-world RA patients. Research design and methods: Retrospective, single-center cohort study including naïve patients treated between January 2003-April 2020. Time to first ADR and drug survival of first-time treatment were studied using Kaplan-Meier and Cox-regression models up to 10 years, with 2-and 5-year posthoc sensitivity analysis. Nature and frequencies of first-time ADRs and causes of treatment discontinuation were assessed. Results: In total, 416 patients (ADA: 255, ETN: 161, 4865 patient years) were included, of which 92 (22.1%) experienced ADR(s) (ADA: 59, 23.1%; ETN: 33, 20.4%). Adjusted for age, gender and concomitant conventional DMARD use, ADA was more likely to be discontinued than ETN up to 2-, 5-and 10-year follow-up (adjusted HRs 1.63; 1.62; 1.59 (all p<0.001)). ADRs were the second reason of treatment discontinuation (ADA 20.7%, ETN 21.4%). Conclusions: Despite seemingly different nature and frequencies, ADRs are the second reason of treatment discontinuation for both bDMARDs. Furthermore, 2-, 5-, and 10-year drug survival is longer for ETN compared to ADA.
The popularity of mobile food delivery apps (MFDAs) and the online food delivery industry surged during the COVID-19 epidemic. Despite the explosive growth in the use of these apps, relatively limited research has been done to determine what affects their continuous use. This study predicts the continuous use of MFDAs and explores the variables that influence this utilization using a novel machine learning (ML) based approach. The machine learning models included four distinct constructs (i.e., features): perceived compatibility, convenience, online reviews, and delivery experience. These features were measured using a survey instrument. Eight different machine learning (ML) models, ranging from basic decision trees to neural networks, were deployed. All eight models achieved high prediction accuracy of above 93%, with the CatBoost model having the highest accuracy among them at 98%. Feature importance analysis revealed perceived compatibility to be the most important factor impacting the continuous usage of MFDAs followed by convenience, online reviews, and delivery experience respectively. The study’s findings have ramifications for MFDA marketing and design. Given the significance of perceived compatibility, MFDA marketing campaigns should have a strong emphasis on highlighting how well these apps fit with the users’ lifestyles.
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