The misleading concept of initial severity in depression clinical trials: development and results from a mathematical model

The impact of baseline glomerular filtration rate on subsequent changes of glomerular filtration rate in patients with chronic kidney disease

Higher baseline glomerular filtration rate (GFR) may yield subsequent steeper GFR decline, especially in patients with diabetes mellitus (DM). However, this correlation in patients with chronic kidney disease (CKD) and the presence or absence of DM remains controversial. We conducted a longitudinal cohort study in a single medical center between 2011 and 2018. Participants with CKD stage 1 to 3A were enrolled and divided into DM groups and non-DM groups, and then followed up at least every 6 months. We used a linear mixed regression model with centering time variable to overcome the problem of mathematical coupling in the analysis of the relation between baseline GFR and the changes, and compared the results from correct and incorrect specifications of the mixed models. A total number of 1002 patients with 285 diabetic and 717 non-diabetic persons was identified. The linear mixed regression model revealed a significantly negative correlation between baseline GFR and subsequent GFR change rate in both diabetic group and non-diabetic group (r =  - 0.44 [95% confidence interval [CI], - 0.69 to - 0.09]), but no statistical significance in non-diabetic group after within-subject mean centering of time variable (r =  - 0.09 [95% CI, - 0.41 to 0.25]). Our study showed that higher baseline GFR was associated with a subsequent steeper GFR decline in the DM group but not in the non-DM group among patients with early-stage CKD. Exact model specifications should be described in detail to prevent from a spurious conclusion.

Combining threshold analysis and GRADE to assess sensitivity to bias in antidepressant treatment recommendations adjusted for depression severity

Threshold analysis has recently been proposed to be used in combination with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) in order to assess the sensitivity to plausible bias of treatment recommendations derived from Bayesian network meta-analysis (NMA). Here, it was aimed to apply the combination of threshold analysis and GRADE to judge quantitative and qualitative information on risk of bias in antidepressant treatment recommendations. The analysis was based on the data set provided by Cipriani et al. (The Lancet 2018) comparing 21 antidepressants in adult major depressive disorder (MDD). Primary outcomes were efficacy (response rate) and acceptability (dropout rate) adjusted for the covariate depression severity. The combined approach suggested sensitivity to plausible bias to be largest for antidepressant recommendations top ranked by Cipriani et al., that is, amitriptyline, duloxetine, paroxetine, and venlafaxine in terms of efficacy and agomelatine, escitalopram, paroxetine, and venlafaxine in terms of acceptability. Covariate ranges within which recommendations were most sensitive to plausible bias were very severe depression in terms of efficacy (smallest threshold, ie, the largest sensitivity, around 39 Hamilton Depression Rating Scale [HDRS]) and moderate depression in terms of acceptability (smallest thresholds around 16 and 35 HDRS). This indicates that treatment recommendations within these ranges may likely change if plausible bias adjustments take place. The present findings may support decision makers in judging the sensitivity to plausible bias of current antidepressant treatment recommendations to accurately guide treatment decisions in MDD depending on depression severity.