Psych Vivas · foundations — advanced EBM and evidence synthesis
Advanced critical appraisal — structured clinical viva
Fellowship viva on advanced forest-plot reading, NMA assumptions, GRADE, and psychiatry-specific bias threats.
On this page & tools
Target exams
Interpretation
Reveal interpretation
Forest plot (panel 1). Open with design and outcome. Describe weights, CI whiskers, diamond, and — critically — the prediction interval if shown. Moderate–substantial I-squared means the average effect may not predict every setting; explore clinical heterogeneity (dose, chronicity, industry, scales) before formulary mandates.[1][9] Map key included trials to RoB 2 domains, emphasising concealment and outcome measurement for subjective scales.[2]
NMA rankings (panel 2). Cipriani-style antidepressant NMA integrates direct and indirect evidence for efficacy and acceptability.[3] Users’ guide priorities: network geometry, transitivity, consistency/coherence, and absolute benefits/harms — not SUCRA league tables as clinical gospel.[4][9] Small average differences can still be policy-relevant but should not force one drug for every patient.
GRADE language. Separate certainty (may be moderate/low after RoB, inconsistency, publication concerns) from recommendation strength (often conditional for agent choice among similarly effective options when values and side-effect profiles diverge).[5][6]
Follow-ups. Observational metabolic or suicide signals → ROBINS-I and confounding by indication.[7] 'Works only in severe depression' → Sun subgroup credibility criteria.[8] Non-inferiority press claims → demand pre-specified delta and CI-vs-margin.
Formulary bottom line. Do not change first-line policy on a ranking figure alone. Prefer: low RoB evidence, absolute effects, harm profiles, local acquisition/cost, monitoring capacity, and shared decision-making — with honest certainty language.[5][6][9]
Key points
References
- [1]Riley RD, Higgins JPT, Deeks JJ Interpretation of random effects meta-analyses BMJ, 2011.PMID 21310794
- [2]Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials BMJ, 2019.PMID 31462531
- [3]Cipriani A, Furukawa TA, Salanti G, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis Lancet, 2018.PMID 29477251
- [4]Mills EJ, Ioannidis JPA, Thorlund K, Schünemann HJ, Puhan MA, Guyatt GH How to use an article reporting a multiple treatment comparison meta-analysis JAMA, 2012.PMID 23011714
- [5]Guyatt GH, Oxman AD, Vist GE, et al; GRADE Working Group GRADE: an emerging consensus on rating quality of evidence and strength of recommendations BMJ, 2008.PMID 18436948
- [6]Guyatt GH, Oxman AD, Kunz R, et al. Going from evidence to recommendations BMJ, 2008.PMID 18467413
- [7]Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions BMJ, 2016.PMID 27733354
- [8]Sun X, Ioannidis JPA, Agoritsas T, Alba AC, Guyatt G How to use a subgroup analysis: users' guide to the medical literature JAMA, 2014.PMID 24449319
- [9]Chaimani A, Salanti G, Leucht S, Geddes JR, Cipriani A Common pitfalls and mistakes in the set-up, analysis and interpretation of results in network meta-analysis: what clinicians should look for in a published article Evid Based Ment Health, 2017.PMID 28739577