Podcast Summary: Derms on Drugs

Episode: Network Meta-Analyses in Dermatology: Can You Trust the Data?

Host(s): Matt Zirwas, Laura Ferris, Tim Patton
Guest: Dr. Aaron Drucker (University of Toronto, NMA expert)
Release Date: October 10, 2025

Episode Overview

This episode tackles the often confusing but critical topic of network meta-analyses (NMAs) in dermatology. The hosts, joined by expert Dr. Aaron Drucker, break down what NMAs are, why their results sometimes differ (or conflict), how industry involvement might affect them, and how clinicians can interpret these analyses. Expect lively debate, practical advice, nerdy statistical deep-dives, and the group’s signature humor throughout.

Key Discussion Points & Insights

1. What Is a Network Meta-Analysis?

• Definition & Purpose:
  • NMAs combine multiple clinical trials (often without direct drug-to-drug comparisons) using advanced statistics to estimate which treatments are most effective.
  • They’re increasingly common in dermatology literature.
• Different from Regular Meta-Analyses:
  • Involves a “network” of comparisons (e.g., Drug A vs Placebo, Drug B vs Drug A, etc.).
  • Indirect comparisons are possible even when head-to-head trials are absent.
• Caution: Results can be inconsistent, heavily influenced by trial selection, statistical choices, and data quality.

Notable quote:

“A network meta-analysis basically takes a bunch of studies, kind of puts them all together with some fancy statistics, and then tells you which drug works the best ... but they don't always have consistent results.” — Matt Zirwas [00:36]

2. Recent NMA Examples: Key Findings and Red Flags

A. NMAs in Hidradenitis Suppurativa (HS)

• Presenter: Laura Ferris
• Study: Garg et al., JAMA Dermatology (25 trials, 39 treatments, ~6,000 patients)
• Findings:
  • Adalimumab remains the "standout" for efficacy and is hard to beat (“Adalimumab. It's pretty good and it's hard to beat.” — Laura Ferris [02:49])
  • Bimakizumab (anti-IL17AF) and Sonolocumab (nanobody, anti-IL17A/F) are contenders, though new and based on small studies.
  • Infliximab didn’t perform as well as expected, possibly due to trial variance.
  • Safety generally acceptable (low discontinuation rates).
• Nuances:
  • HS studies are “really hard to do”; scoring can vary significantly across time and raters.
  • Some confusion regarding endpoints and exact drugs because of trial nomenclature.

B. Atopic Dermatitis: Contradictory NMA Results

• Presenter: Matt Zirwas
• Two NMAs (2022) with differing conclusions:
  • Example: One NMA (JAMA) found abrocitinib 100mg inferior to dupilumab; another found them about equal.
• Industry Sponsorship:
  • At least one NMA was funded by AbbVie (“…if a network meta analysis is funded by a drug company, I generally pay no attention to it at this point.” — Matt Zirwas [09:50])
  • Comparative ranking outcomes often align suspiciously well with sponsor interests.

C. NMA Methods in Psoriasis

• Presenter: Tim Patton
• Study: Galimi et al., BMC, April 2025 (560 NMAs, ~20 drugs)
• Key Insight:
  • Even when analytic methods and study choices are altered, the “top tier” of drugs seldom change—but ranking within that group can.
  • Infliximab’s performance varied widely due to individual study design/dosing.
  • Drug companies can “cherry pick” favorable analyses (“…they could easily run 560, cherry pick the one that showed it to be the best.” — Tim Patton [13:50])

3. Expert Q&A with Dr. Aaron Drucker (Core Segment: 16:00-41:00)

A. How Robust Are NMAs with Few Head-to-Heads?

• Answer:
  • NMAs linking most drugs only via placebo are less robust but can still yield useful results.
  • True robustness is established once direct (head-to-head) trials emerge and agree with NMA findings. If head-to-head and NMA results diverge (statistical term: “incoherence”), trust the head-to-head study.

“If you have a well done randomized controlled trial of two drugs head to head, that does trump the results of indirect network meta analysis.” — Dr. Aaron Drucker [18:49]

B. Selection of Endpoints Matters

• The outcome measures chosen (e.g., PASI 75 vs PASI 100 in psoriasis) directly affect perceived efficacy.
• Recommendation: Check if outcome selection was pre-specified or post-hoc.

C. Influence of Industry & Protocols

• Having a published protocol is crucial for transparency.
• Industry-funded NMAs often have more “spin” and should be read extra critically.
• Academic or Cochrane-group NMAs are generally more trustworthy.

“For network meta analysis, there's no good reason for an industry group to do a network meta analysis. ... Industry is great at doing clinical trials and they have the money to do clinical trials, but they don't need to be the ones doing network meta analysis necessarily.” — Dr. Aaron Drucker [28:39]

D. Practical Tips for Reading and Using NMAs

• Assess these three things:
  1. Was there a published protocol?
  2. Is the sponsorship academic or industry?
  3. Is there nuance in the rankings (confidence intervals, how close drugs are ranked)?
• Avoid overemphasis on “first place” rankings unless clinical differences are substantial.

E. Complexity and Accessibility

• It's possible to run “simple” NMAs with software, but more complex analyses require expertise.
• NMAs are a good research area for trainees interested in clinical research, especially if targeting unaddressed questions.

F. Where to Find Trustworthy NMAs

• Dr. Drucker’s group maintains living NMAs at eczematherapies.com.
• The Cochrane group is the go-to for psoriasis NMAs.

4. Key Challenges and Misconceptions

• Industry Sponsorship:
  • NMAs can be subtly or overtly biased in design, methodology, endpoint selection, and result spin.
• Trial Population Differences (“Transitivity”):
  • Trial demographics and inclusion criteria change over time, making cross-trial comparisons less reliable for older drugs (e.g., infliximab vs newer biologics).
• False Certainty:
  • Rankings without reported uncertainty are misleading.
• Insufficient Data (“Hub and Spoke”):
  • If every drug is only connected to placebo (“spokes” from a single hub), the results are much less robust.

5. Fun and Trivia Segment (Statistics & Medical History; 41:00–45:17)

• Kaplan & Meier: Creators of the Kaplan-Meier curve, foundational for survival analysis.
• Florence Nightingale: Pioneered use of statistical graphics with her “Coxcomb” graph to improve hospital sanitation.
• Student’s T-test: So named because William Gossett, employed at Guinness, published under the pseudonym “Student”.

Notable Quotes & Moments

• “Adalimumab. It's pretty good and it's hard to beat.”
  — Laura Ferris [02:49]

• “I always thought that’d be a big ... you know, one would have a tiny standard deviation and one would have a huge standard deviation. But ... statistics people don’t talk about it.”
  — Matt Zirwas [41:12]

• “Rankings are really oversimplified. There’s no confidence interval ... There’s going to be some spin.”
  — Dr. Aaron Drucker [26:21]

• “For our listeners ... how would you recommend, like, trying to decide, should I listen to ... [an NMA]?”
  — Matt Zirwas [28:00]

• “Find a disease state that doesn’t have a network meta analysis, or it has one, but it’s old ... then that’s where you can make a real contribution.”
  — Dr. Aaron Drucker [32:25]

Recommended Resources & Takeaways

• Check for protocols and academic sponsorship when evaluating NMAs.
• Don’t trust vendor-funded NMAs at face value. Scrutinize the methodology and spin.
• NMAs are useful but direct head-to-head trials remain the most reliable source if disagreements emerge.
• For up-to-date eczema NMA data: eczematherapies.com
• For psoriasis: Use the Cochrane review page.

Timestamps for Major Segments

• Introduction to NMAs, Hidradenitis Suppurativa example: 00:10–07:12
• Contradictory Atopic Dermatitis NMAs & Industry Bias: 07:12–10:23
• Psoriasis NMA Methods, Cherry Picking: 10:23–15:28
• Dr. Drucker Joins – NMA Deep Dive: 16:00–41:00
• Statistics Trivia and Fun Facts: 41:00–45:17

Final Thought

Despite their statistical complexity, NMAs are here to stay—and critical for drug comparisons in dermatology. But trust is built on transparency, methodology, and independence. As Dr. Drucker notes, “...it ought to be more nuance in the interpretation.” Dermatologists should arm themselves with a healthy skepticism—and always look for the protocol before buying into the rankings.