Experienced Biostatistician for Bayesian Network Meta-Analysis (gemtc / R)
Budget: $250 – $750 USD
Paid on Delivery
We are seeking an experienced biostatistician or advanced data analyst to complete a Bayesian Network Meta-Analysis (NMA) using the gemtc R package framework. The project is estimated at approximately 10+ hours of work, depending on expertise level.
Project Background
The systematic review has been completed. Studies have already been identified and screened.
The analysis will be conducted using:
MetaInsight software
R package: gemtc (Generalized Evidence Synthesis for Network Meta-Analysis)
Bayesian random-effects model
Markov Chain Monte Carlo (MCMC) methods
Absolute Risk Reduction (ARR) as primary outcome
95% Credible Intervals (CrI)
SUCRA-based treatment rankings
Haldane–Anscombe continuity correction for zero-event trials
Default gemtc priors will be used unless otherwise justified.
Scope of Work
We need assistance with:
Structuring extracted study-level dichotomous data into an analysis-ready format
Applying Haldane–Anscombe correction where appropriate
Running Bayesian random-effects NMA using gemtc
Assessing convergence diagnostics (trace plots, Gelman–Rubin, ESS)
Generating:
Network plots
Forest plots
League tables
Treatment ranking plots
SUCRA values
Sensitivity analyses (if needed)
Brief written Methods & Results section suitable for manuscript inclusion
Fully reproducible and annotated R code
Required Qualifications
Strong experience in Bayesian Network Meta-Analysis
Hands-on experience with GEMTC
Understanding of MCMC diagnostics and model convergence
Experience with dichotomous outcomes and continuity corrections
Ability to interpret SUCRA and treatment rankings appropriately
Prior NMA publication experience strongly preferred
Deliverables
Clean structured dataset
Annotated R script
All model outputs and diagnostic plots
Publication-ready tables and figures
1–2 page written methods/results summary
Timeline - ASAP
To Apply
Please include:
Preferbly examples of prior NMA projects (especially Bayesian/gemtc-based)
Estimated timeline
NOTE: We are specifically looking for someone experienced, expert — not a beginner learning NMA.
We are seeking an experienced biostatistician or advanced data analyst to complete a Bayesian Network Meta-Analysis (NMA) using the gemtc R package framework. The project is estimated at approximately 10+ hours of work, depending on expertise level.
Project Background
The systematic review has been completed. Studies have already been identified and screened.
The analysis will be conducted using:
MetaInsight software
R package: gemtc (Generalized Evidence Synthesis for Network Meta-Analysis)
Bayesian random-effects model
Markov Chain Monte Carlo (MCMC) methods
Absolute Risk Reduction (ARR) as primary outcome
95% Credible Intervals (CrI)
SUCRA-based treatment rankings
Haldane–Anscombe continuity correction for zero-event trials
Default gemtc priors will be used unless otherwise justified.
Scope of Work
We need assistance with:
Structuring extracted study-level dichotomous data into an analysis-ready format
Applying Haldane–Anscombe correction where appropriate
Running Bayesian random-effects NMA using gemtc
Assessing convergence diagnostics (trace plots, Gelman–Rubin, ESS)
Generating:
Network plots
Forest plots
League tables
Treatment ranking plots
SUCRA values
Sensitivity analyses (if needed)
Brief written Methods & Results section suitable for manuscript inclusion
Fully reproducible and annotated R code
Required Qualifications
Strong experience in Bayesian Network Meta-Analysis
Hands-on experience with GEMTC
Understanding of MCMC diagnostics and model convergence
Experience with dichotomous outcomes and continuity corrections
Ability to interpret SUCRA and treatment rankings appropriately
Prior NMA publication experience strongly preferred
Deliverables
Clean structured dataset
Annotated R script
All model outputs and diagnostic plots
Publication-ready tables and figures
1–2 page written methods/results summary
Timeline - ASAP
To Apply
Please include:
Preferbly examples of prior NMA projects (especially Bayesian/gemtc-based)
Estimated timeline
NOTE: We are specifically looking for someone experienced, expert — not a beginner learning NMA.