BGPT: Create Graphs: Forest plots MenACWY-TT vs CRM and D with study-level data

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Long Response Copied

Forest plots request (MenACWY-TT vs CRM and D)

To create study-level forest plots (typically effect sizes with CIs per study), I need the actual study-level numeric data for:

Comparator group: CRM and D (as you wrote “CRM and D” — I need to confirm what “D” refers to: a different product? a day/timepoint? a dose group?).
Effect metric (e.g., risk ratio, risk difference, odds ratio, GMT ratio, seroresponse proportion difference). Forest plots require a consistent metric across studies.
For each study arm: the numerator/denominator needed to compute that effect size (or the effect size + SE/CI if already reported).
Per-study confidence intervals or enough to compute them (sample sizes + event counts, or log(effect) + SE, etc.).

Right now, no study-level dataset was provided in your message, so I cannot truthfully compute or plot anything without inventing data (which I will not do).

What I need from you (copy/paste table)

Paste your study-level data in one of these formats. If you already have it in a CSV, upload it and tell me the column names.

Format A (recommended): event counts

study_id,study_name,outcome,metric,arm,events,total,ci_low,ci_high
S1,...,Outcome1,RR,MenACWY-TT,12,100,, 
S1,...,Outcome1,RR,CRM,10,98,,
S1,...,Outcome1,RR,D,9,95,,

If you provide events/total, I will compute effect sizes + CIs and generate forest plots.

Format B: effect already computed

study_id,outcome,metric,arm,effect,ci_low,ci_high
S1,Outcome1,RR,MenACWY-TT vs CRM,1.23,1.05,1.44
S2,Outcome1,RR,MenACWY-TT vs D,0.98,0.80,1.20

If effect sizes are already provided, I will plot them directly.

Once you provide the data, I will produce (at minimum)

Forest plot #1: MenACWY-TT vs CRM (study-level effects + 95% CIs, with optional random-effects summary).
Forest plot #2: MenACWY-TT vs D (same outcome metric, same conventions).
Data table used to generate each plot (so it’s auditable, reproducible, and science-grade).
Heterogeneity indicators (e.g., I²/τ²) if the requested model is feasible from your provided metric/SE/CI.

Clarifying question (critical)

When you say "CRM and D", does that mean:

CRM is one comparator product, and D is a second comparator product (two separate forest plots),
or is CRM and D a combined label in a single arm?

Reply with which one, and confirm the outcome(s> you want plotted (e.g., seroresponse rate, GMT ratio, safety event rate).

If you upload the MenACWY-TT vs CRM and D study-level data (or specify sources/primary studies), the agent will compute effect sizes and generate the forest plots.

Scientific skepticism note: I cannot generate forest plots without the per-study numeric inputs (or per-study effect sizes + uncertainty). Any attempt without those numbers would be data fabrication.

Feedback:

Updated: March 19, 2026

Keep Exploring

Which outcome metric (RR/OR/GMT/seroresponse/safety incidence) should be plotted for MenACWY-TT vs CRM and D, and how is the denominator defined per study?

Do all included studies report the same endpoint definition and assay/cutoff (e.g., seroresponse threshold), or will subgrouping/mapping be required before pooling?

Analysis Wizard

Reads your uploaded study-level MenACWY-TT/CRM/D dataset, computes consistent effect sizes and CIs per study for your chosen metric, then outputs tidy tables and arrays for Plotly forest-plot rendering.

Science Art

Science Movie

Make a narrated HD Science movie for this answer ($32 per minute)

Discussion

BGPT Bias

I’m biased toward refusing to fabricate numeric results when study-level data aren’t provided, prioritizing auditability and correctness over producing a “plot” without inputs.

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