Brand-Name Antidepressants Outperform Their Generic Counterparts in Preventing Hospitalization for Depression: The Real-World Evidence from Taiwan

BackgroundGeneric antidepressants are approved on the market based on evidence of bioequivalence to their brand-name versions. We aimed to assess whether generic antidepressants exert equal effectiveness as their brand-name counterparts for treating patients with depressive disorders.MethodsIn a nationwide, population-based cohort in Taiwan from 1997 through 2013, patients with a diagnosis of a depressive disorder aged between 18 and 65 years who were new users of antidepressant drugs were classified into either the brand-name group or the generic group. All patients were followed up until medication discontinuation or the end of the study period. We assessed the risk for hospitalization as a primary outcome and augmentation therapy, daily dose, medication discontinuation, or switching to another antidepressant as secondary outcomes.ResultsA total of 277 651 brand-name users (35.8% male; mean age: 41.2 years) and 270 583 generic users (35.8% male; mean age: 41.0 years) were divided into 10 different antidepressant groups (fluoxetine, sertraline, paroxetine, escitalopram, citalopram, venlafaxine, mirtazapine, moclobemide, imipramine, and bupropion). We found that patients treated with the generic form of sertraline, paroxetine, escitalopram, venlafaxine, mirtazapine, and bupropion demonstrated significantly higher risks of psychiatric hospitalization (adjusted hazard ratios ranged from 1.20–2.34), compared to their brand-name counterparts. The differences between brand-name antidepressants and their generic counterparts in secondary outcomes varied across different drugs.ConclusionsCompared to most generic antidepressants, brand-name drugs exhibited more protective effects on psychiatric hospitalization for depressive patients. These findings could serve as an important reference for clinicians when encountering patients with depressive disorder.     

Lower body mass index and higher height are correlated with increased varicocele risk

To evaluate the anthropometric indexes in subjects with varicocele compared to controls and the incidence of varicocele in different body mass index (BMI) groups for the purpose of exploring the association between varicocele and anthropometric indexes. A comprehensive literature search was conducted by using PubMed, MEDLINE, EMBASE databases and Cochrane Library up to February 2019. A systematic review and meta‐analysis was conducted by STATA, and Newcastle–Ottawa Scale was utilised for assessing risk of bias. Ultimately, 13 articles containing seven case–control studies and six cross‐sectional studies with 1,385,630 subjects were involved in our study. Pooled results demonstrated that varicocele patients had a lower BMI (WMD = ?0.77, 95% CI = ?1.03 to ?0.51) and a higher height than nonvaricocele participants, especially in grade 3 varicocele patients. Subgroup analyses showed that normal BMI individuals had a higher risk of varicocele than obese or overweight individuals and a lower risk than underweight individuals. In conclusion, this study indicates that varicocele patients have a lower BMI and a higher height than nonvaricocele participants. Moreover, men with excess bodyweight have a lower incidence of varicocele compared to normal weight or underweight people. That is to say, high BMI and adiposity protect against varicocele and high BMI is associated with a decreased risk of varicocele.     

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.