Conditional power of antidepressant network meta-analysis

Abstract

Background: Conditional power of network meta-analysis (NMA) can support the planning of randomized controlled trials (RCTs) assessing medical interventions. Conditional power is the probability that updating existing inconclusive evidence in NMA with additional trial(s) will result in conclusive evidence, given assumptions regarding trial design, anticipated effect sizes, or event probabilities.

Methods: The present work aimed to estimate conditional power for potential future trials on antidepressant treatments. Existing evidence was based on a published network of 502 RCTs conducted between 1979-2018 assessing acute antidepressant treatment in major depressive disorder (MDD). Primary outcomes were efficacy in terms of the symptom change on the Hamilton Depression Scale (HAMD) and tolerability in terms of the dropout rate due to adverse events. The network compares 21 antidepressants consisting of 231 relative treatment comparisons, 164 (efficacy) and 127 (tolerability) of which are currently assumed to have inconclusive evidence.

Results: Required sample sizes to achieve new conclusive evidence with at least 80% conditional power were estimated to range between N = 894 - 4190 (efficacy) and N = 521 - 1246 (tolerability). Otherwise, sample sizes ranging between N = 49 - 485 (efficacy) and N = 40 - 320 (tolerability) may require stopping for futility based on a boundary at 20% conditional power. Optimizing trial designs by considering multiple trials that contribute both direct and indirect evidence, anticipating alternative effect sizes or alternative event probabilities, may increase conditional power but required sample sizes remain high. Antidepressants having the greatest conditional power associated with smallest required sample sizes were identified as those on which current evidence is low, i.e., clomipramine, levomilnacipran, milnacipran, nefazodone, and vilazodone, with respect to both outcomes.

Conclusions: The present results suggest that conditional power to achieve new conclusive evidence in ongoing or future trials on antidepressant treatments is low. Limiting the use of the presented conditional power analysis are primarily due to the estimated large sample sizes which would be required in future trials as well as due to the well-known small effect sizes in antidepressant treatments. These findings may inform researchers and decision-makers regarding the clinical relevance and justification of research in ongoing or future antidepressant RCTs in MDD.

Keywords: Antidepressants; Conclusive evidence; Conditional power; Network meta-analysis; Sample size.

Fig. 1

Evidence across study year. Bar plots illustrating the cumulative sum of comparisons with conclusive versus inconclusive evidence across study year with respect to the two outcomes efficacy and tolerability. The total number of treatment comparisons is 231

Fig. 2

Conditional power. Box plots illustrating conditional power (CP) across all comparisons with inconclusive evidence as a function of sample size with respect to the two outcomes efficacy and tolerability. Whiskers of the box plots extend to the most extreme data values. Horizontal red dashed lines indicate 20% and 80% conditional power at which sample sizes (N_CP=20%, N_CP=80%) were estimated. Results are shown based on the main analysis considering a trial design ratio of r = 1/0, anticipated alternative effect sizes equal to the network estimates (f_xyN), and anticipated event probabilities equal to the average network event probability (pc_N)

Fig. 3

Sample size. Heat map illustrating sample size at 20% (N_CP=20%) (lower triangles) versus 80% conditional power (N_CP=80%) (upper triangles) for individual comparisons with respect to the two outcomes efficacy and tolerability. Colormap is log scaled for better visibility. Comparisons with conclusive evidence are marked (white). Results are shown based on the main analysis considering a trial design ratio of r = 1/0, anticipated alternative effect sizes equal to the network estimates (f_xyN), and anticipated event probabilities equal to the average network event probability (pc_N)

Fig. 4

Network graphs. Network graphs illustrating treatment comparisons with inconclusive evidence with respect to the two outcomes efficacy and tolerability. Circle size is proportionate to actual sample size. Line width is inverse proportionate to the sample size at 80% conditional power (N_CP=80%), such that thicker connections indicate smaller sample sizes and thus greater conditional power. Thickness is log scaled for better visibility. Results are shown based on the main analysis considering a trial design ratio of r = 1/0, anticipated alternative effect sizes equal to the network estimates (f_xyN), and anticipated event probabilities equal to the average network event probability (pc_N). See the supplementary appendix for graphs of the original network (Supplement 1, Fig. S2)