Bayesian Meta-Regression for Clinical Side-Effects

@WycOj

As a seasoned Biostatistician and Data Analyst with over 7 years of experience, I can confidently assure you that your Bayesian Meta-Regression analysis will be in expert hands with me. My robust expertise in R, SPSS, and STATA, ideally position me for a thorough yet efficient project experience dedicated to providing clear, concise and reproducible results. In the context of your project, I specialize in data cleaning and manipulation for large datasets - making me an ideal candidate to import and prepare your Excel data. Additionally, my comprehensive understanding of statistical techniques such as Meta-Analysis combined with packages like brms, bayesmeta, etc., allows me to ensure the implementation of precise priors aligned with the Java code best suited for your project. One of my key strengths is in transforming patterns gleaned from large assortments of data sets into compelling insights. Combining this proficiency with my keen-eye for detail that uniquely fuels my love for generating informative data visualizations and dashboards using R and Python will significantly enhance your report output - including forest plots, funnel plots etc., as you've requested . In conclusion, the decision to entrust me with your project means selecting an exemplary and meticulously detailed professional who will provide consistent quality that complies with both stipulated requirements and established deadlines.

@ShaikhAneesa

Hi there, I understand you need a robust Bayesian framework to quantify treatment-related side effects, moving beyond frequentist meta-analysis to explore how study-level covariates drive the incidence of adverse events. My approach is built on a rigorous, reproducible R workflow. First, I will import and clean your Excel data, transforming it into a "tidy" format suitable for Bayesian modeling. Next, I will use the brms package to specify a random-effects model, implementing weakly informative priors to allow the data to speak while ensuring numerical stability. Finally, I will extend this into a meta-regression to quantify the impact of your covariates, providing full convergence diagnostics like R-hat and Effective Sample Size to ensure the posterior estimates are reliable. I am highly confident in delivering a "publication-grade" analysis. I prioritize transparency, so my code will include detailed comments justifying the choice of priors and model structures. I will also include sensitivity checks to see how different prior specifications affect your pooled estimates, ensuring the results are robust and ready for senior-level review. Do you have a specific link function in mind (such as Logit or Probit), and are the side-effect counts currently formatted as "events vs. total" for each study arm? I’m ready to provide a clean, Bayesian-driven insight into your trial data. Warm Regards, Aneesa.

@glanegonsalves

Hey there Glane here, hope you're doing well. I can help you in Bayesian meta analysis using Rmarkdown justifying each steps with comments and also providing explanation, visuals usign ggplot2,tables using kableExtra and then knitting it into word/html / pdf. Feel free to get in touch.

@GdevDataSceince

Hi there, I am thrilled to present for providing R programming solutions and services to meet your specific data analysis, statistical modeling, and visualization needs. I am of skilled R programmers and data analysts who are passionate about leveraging the power of R to deliver valuable insights and solutions for businesses across various domains. Utilizing R's extensive libraries, such as dplyr and ggplot2, for data cleaning, transformation, and visualization. Developing robust statistical models using R for forecasting, regression analysis, time series analysis, Implementing machine learning algorithms using R packages like caret, random Forest, xgboost, Building predictive models and classifiers to gain predictive insights from your data. Custom R Package Development: https://www.freelancer.com.bd/u/GdevDataSceince My commitment to client satisfaction is unwavering. Throughout the development process, I prioritize open communication, responsiveness, and collaboration. Welcome feedback and are always ready to address any concerns or modifications you may require. Regards @GdevDataSceince

@ahmedsadek111124

Hello, I can help with importing and preparing your Excel clinical trial data for a Bayesian meta-regression in R, using packages like brms or rstanarm. I can handle specifying sensible priors, fitting both base and meta-regression models, and generating diagnostics plus visualizations like forest and funnel plots. I’ll provide a clearly commented R script and a comprehensive reproducible report. I can also suggest sensitivity analyses if needed for robustness. What study-level covariates are you particularly interested in exploring in the meta-regression? Best regards,

@mzdesmag

Hello sir, Did go through your job description and glad to share that I have enormous experience in working with Bayesian Meta-Regression for Clinical Side-Effects I'm a seasoned programmer and Engineer with quality experience in Flutter, React, Node.JS, SpringBoot, Frontend and Backend Development, Python, Matlab, R studio, C, C++, C#, OpenCV, OpenGL, Tesseract OCR, google vision, Statistical programming/R progamming data analysis Computing for Data Analysis Time Series & Econometric, Machine learning, AI, Deep learning, Matlab and Mathematica, 3D modeling, CAD/CAM,AutoCAD, 2D, Architectural Engineering, SolidWorks, Unity 3D, PCB, Electronics, Arduino, Automation, Embedded and Firmware , IOT, Electrical/Mechanical Engineering I am a TOP Rated Freelancer, and you can check my reviews here as well: https://www.freelancer.com/u/mzdesmag. Looking forward to potentially working together on this project. Thanks and Best regards, Adekunle.

@anirudhat

Hi, I've worked extensively on Bayesian meta-analyses for clinical and epidemiological data, so your project on quantifying treatment-related adverse events is right in my wheelhouse. My approach would be to first clean and reshape your Excel workbook into an analysis-ready format, then fit a random-effects model using `brms` with weakly informative priors justified inline, followed by meta-regressions on your study-level covariates to explore heterogeneity in side-effect incidence. I'll deliver a fully commented R script alongside a rendered R Markdown report containing convergence diagnostics (R-hat, trace plots), posterior summaries, forest and funnel plots, and a plain-language interpretation. I'll also run leave-one-out sensitivity analyses and compare half-normal versus half-Cauchy priors on the between-study variance to check robustness. I can start immediately—happy to discuss your dataset structure before diving in.

@KirtikaKataria

Hi there, I understand you need a fully reproducible Bayesian meta-analysis workflow in R, starting from Excel-based RCT data, including random-effects modelling, meta-regression on study-level covariates, and clear diagnostic and interpretive outputs. My approach will begin with importing and cleaning your dataset in R (tidyverse), ensuring consistent effect size calculation (e.g., log OR/risk ratios) and variance structure. I will then specify a Bayesian random-effects model using brms (or bayesmeta where appropriate), with clearly justified priors (weakly informative or domain-informed) documented directly in the code. Next, I will fit the base pooled model followed by meta-regression models incorporating your study-level covariates, ensuring model convergence through diagnostics (R-hat, ESS, trace plots). I will generate posterior summaries, credible intervals, and key visualisations including forest plots, funnel plots, and posterior distributions. The entire workflow will be wrapped in a clean R Markdown report (HTML/PDF) combining code, outputs, and a concise interpretation of treatment-related side-effects and covariate influence. I can also include sensitivity analyses (alternative priors, model specifications) to strengthen robustness. QUESTION: What effect measure are your studies reporting (e.g., odds ratios, risk ratios, proportions), and are adverse events consistently defined across trials? Let’s get started. Regards, Shehwani.

@hoangnn90

✅Full Experience in Statistical Data Analysis and Visualization with R Programming✅. ✳️I am very confident that complete your project perfectly. ✳️I can guarantee the quality of the job and deliver the result on time. I hope we will discuss in more detail via chat. Best regards!

@Feriver

Hello, I understand you need a fully Bayesian meta-analysis and meta-regression in R using your Excel-based clinical trial dataset, focusing on treatment-related side-effects with transparent, reproducible code and clear interpretation. The workflow must include data preparation, model specification, diagnostics, and publication-ready outputs. I will clean and structure your Excel data for analysis, then implement a random-effects Bayesian model using brms or rstanarm, followed by meta-regressions incorporating study-level covariates. I will define and justify priors within commented code, run convergence diagnostics (R-hat, ESS, trace plots), and generate posterior summaries, forest plots, funnel plots, and sensitivity checks. All results will be delivered via a fully reproducible R script and an R Markdown report (HTML/PDF) with concise interpretation of findings and limitations. I can begin with a quick data audit to confirm structure and variable readiness, then proceed iteratively to ensure model clarity and robustness. The final output will be clean, reproducible, and ready for academic or clinical use. Are your outcomes binary (event rates) or continuous measures for side-effects across studies? Do you already have specific covariates selected for meta-regression, or should I help identify and encode them? Thanks, Asif

@Freelancersedo

Hello, I can do a fully reproducible Bayesian meta-analysis workflow in R, starting from cleaning your Excel dataset through to fitting random-effects models and meta-regressions using packages like brms or bayesmeta. I’ll specify and justify priors within the code, run convergence diagnostics, and generate clear outputs including forest plots, funnel plots, and posterior summaries. You’ll receive a well-commented R script, a rendered report (R Markdown to HTML or PDF), and a concise interpretation of results, along with optional sensitivity analyses to strengthen robustness and transparency.

@MZITSERVICES

Hi there, I am A.R.M. MASUD with a strong background in data science. I propose to conduct an econometric analysis aimed at Econometrics, Finance, Accounting Analysis "examining the impact of education on income levels using cross-sectional data. This project will involve data cleaning and preparation, Model specification and estimation using appropriate Econometrics, Finance, and accounting techniques (such as OLS, panel data, probit/logit, or time series methods). Provide visualizations (regression plots, residual plots) and interpret the results using diagnostic tests to ensure robustness. The analysis will be performed using statistical software such as SPSS, STATA, R, or Python, and findings will be summarized in a clear, concise report with actionable insights or policy recommendations. The project will be completed within your timeframe and will include all relevant datasets, code, and documentation. https://www.freelancer.com.bd/projects/excel/Finance-Excel-Sheet-Population/reviews https://www.freelancer.com.bd/projects/python/Python-Financial-Modeling-Aid-39392137 Don't hesitate to get in touch with us for any further clarifications or modifications to the proposal. Thanks A.R.M MASUD

@kaimenyijohn5

AM READY TO START ASAP: BAYESIAN META-ANALYSIS – CLINICAL TRIAL SIDE EFFECTS (R, BRMS, RANDOM-EFFECTS, META-REGRESSION) Hello, I am John K., an MSc Economist & Statistician with over fifteen years of experience as a full-time freelancer. I have successfully delivered 1,000+ projects on Freelancer.com, maintaining a 4.9-star rating. My expertise includes Bayesian methods (brms, rstanarm), meta-analysis, and R programming. My understanding is: You have randomized clinical trial data in Excel. You need a Bayesian random-effects meta-analysis with meta-regression to explore study-level covariates affecting adverse events. Workflow must be in R (brms, rstanarm, or bayesmeta). Deliverables: cleaned data, R script, convergence diagnostics, posterior summaries, forest & funnel plots, and a rendered report (R Markdown/HTML/PDF). I will deliver: ✅ Cleaned dataset and fully reproducible R script. ✅ Base meta-analysis model + meta-regression models. ✅ Prior justification, convergence checks, visualizations, and written interpretation. I am ready to begin. Let's connect via chat to receive your Excel dataset and study-level covariates. Respectfully, John K.

@Abuzar00

I am Abuza, a seasoned full-stack developer with over 5 years of professional experience. While my core programming skills consist of Java, PHP, and Python, I am also well versed in utilizing essential data analysis tools such as Excel and its automation with VBA macros. I have a strong background in RESTful API design, which will be useful for connecting and integrating your project needs. My proficiency with R is equally solid, ensuring that I can handle your Bayesian meta-regression project with ease. In terms of database management, another crucial aspect for this project, I have worked extensively with MySQL, PostgreSQL, and MongoDB, honing my skills not only in database optimization and architecture but also in ensuring data integrity. This translates to a reliable framework for handling your consolidated Excel dataset—cleaning it up, preparing it for analyses, and maintaining its quality throughout the workflow process. Moreover, I have ample experience in delivering accurate reports within timelines in a readable format using R Markdowns/HTML or PDFs. This ensures transparent communication of convergence diagnostics, posterior summaries; presentation of the findings in forest and funnel plots; as well as a step-by-step account of how the conclusions were reached.

@Andyloredo

Successfully implementing Bayesian meta-regression requires careful consideration of prior specification to ensure robust results. I have experience building and interpreting Bayesian models in R, particularly using `brms` and `rstanarm`, for similar epidemiological analyses. I’ll focus on importing your Excel data, fitting the random-effects model, and then extending it with the meta-regressions to explore the influence of study-level covariates, ensuring clear code comments and convergence diagnostics throughout. I can deliver the commented R script, the rendered report (R Markdown/HTML), and all necessary auxiliary files within the 7-day timeframe, prioritizing accuracy and clarity.

@Danzy262

Hi, I’ll build a fully reproducible Bayesian meta-analysis workflow in R for your clinical-trial adverse-event dataset. What you’ll get: Cleaned and prepared Excel dataset for analysis Random-effects Bayesian meta-analysis + meta-regression models Clearly justified priors with commented R code Convergence diagnostics, posterior summaries, forest/funnel plots R Markdown report with concise interpretation of findings Fully reproducible R scripts and supporting files Optional sensitivity analyses for robustness checks I’ll use transparent workflows with packages like brms/rstanarm/bayesmeta depending on model fit and performance. I’ve completed similar statistical analysis projects with 5⭐ feedback for accuracy and delivery. Timeline: 3–5 days Budget: $70 total Please send me a message so we can get started immediately!

@waleeds05

Hi! I see you're looking to analyze clinical trial data to get a grip on treatment-related side effects using a Bayesian approach. I can help you out with that by importing your Excel data and running the necessary random-effects Bayesian meta-analysis in R. I’ve got solid experience in this area, especially with packages like brms and bayesmeta, so I can ensure the code is clean and reproducible. I’ll make sure to prepare your data, fit both the base and meta-regression models, and provide all the diagnostics you need, along with visualizations and a concise report on the findings. If it helps, I can suggest some sensitivity checks as we go along. You'll have the initial deliverable in about 10 days or faster. Let me know what you think! Best regards, Walled Saleem

@rishi9343

I will import the Excel data, run a random-effects Bayesian meta-analysis using R packages like brms or bayesmeta, and extend it with meta-regressions to explore study-level covariates, providing convergence diagnostics, posterior summaries, and forest plots, and deliver a commented R script and rendered report, happy to discuss further over DM.

@bv413

⭐⭐⭐⭐⭐ I have strong experience conducting Bayesian meta-analyses in R using packages like brms and rstanarm, including random-effects models and meta-regressions for clinical datasets with full reproducibility. I will clean and structure your Excel data, specify and document appropriate priors, fit the pooled and covariate-adjusted models, and provide full diagnostics (convergence, posterior summaries, forest/funnel plots) along with a clear interpretation. The deliverable will include a fully commented R script, an R Markdown report (HTML/PDF), and all supporting files so the analysis runs end-to-end without issues. I can also include sensitivity analyses and alternative specifications to strengthen robustness and confidence in the findings.