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Clinical Atlas Prestige · Evidence-first

Psych Vivasfoundations — advanced EBM and evidence synthesis

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.

clinical
On this page & tools

Target exams

FRANZCPMRCPsychABPNMD-DNB

Target exams

FRANZCPMRCPsychABPNMD-DNB
Prompt
You are in a FRANZCP/MRCPsych advanced EBM viva. The examiner places two figures on the table: (1) a forest plot of a random-effects meta-analysis of second-generation antipsychotics versus placebo for acute schizophrenia (I-squared 58%, prediction interval wide and touching the null for some outcomes); (2) a network plot ranking 21 antidepressants by efficacy and acceptability (Cipriani-style). Follow-ups will cover RoB 2 domains, funnel plots, GRADE certainty versus recommendation strength, non-inferiority margins, ROBINS-I for observational safety, and subgroup credibility. Defend whether you would change local first-line formulary choices.

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

PI ≠ CI

Prediction intervals speak to new-setting effects; diamond CIs speak to average effects under the model.[1]

NMA needs transitivity

Indirect comparisons fail silently if effect modifiers differ across the network paths.[4]

Certainty vs strength

GRADE recommendation strength also depends on values and resource trade-offs.[5][6]

References

  1. [1]Riley RD, Higgins JPT, Deeks JJ Interpretation of random effects meta-analyses BMJ, 2011.PMID 21310794
  2. [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. [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. [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. [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. [6]Guyatt GH, Oxman AD, Kunz R, et al. Going from evidence to recommendations BMJ, 2008.PMID 18467413
  7. [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. [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. [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