Data Suitability Assessment Guidance -- 2

@glanegonsalves

Hey there Glane here, I can help assess when it is methodologically appropriate to back-calculate TP, FP, FN, and TN from reported diagnostic accuracy measures such as sensitivity, specificity, ROC/AUC, logistic regression outputs, and other summary statistics for your medical diagnostic meta-analysis. Using R (including packages such as mada, meta, and metafor where appropriate), I'll provide a concise, evidence-based memo explaining when reverse calculations are statistically defensible, discuss implications for hsROC models, review your example studies, and support the recommendations with authoritative methodological references and reporting guidelines to ensure your approach is robust and suitable for publication.

@muratalper99

I have a PhD in statistics with vast experience in studying data and running various statistical analyses. I can provide information with whether derived results can be used bacak.

@qualityxenter

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@HanyasSalem

Hi there, I'm Dr. Hany, a seasoned medical writer and biostatistical analyst with a powerful fusion of clinical proficiency and statistical skills to offer. Your project warrants an exceptional blend of analytical abilities and medical domain insights, which my professional journey perfectly embodies. I believe my expertise in merging advanced data analysis with impactful medical content aligns flawlessly with your data suitability assessment needs for your medical diagnostic research. Let’s discuss your issues on true examples to sort it out and find out how reverse engineering can be achieved.

@assignsolver

Reverse-engineering diagnostic accuracy metrics from summary statistics requires careful consideration of mathematical validity and clinical defensibility—this memo addresses exactly that constraint. The project involves translating between different data representations (raw counts, logistic regression coefficients, ROC curves) and determining when back-calculations preserve statistical integrity. Deliverable structure: A technical memo systematizing the conditions under which sensitivity/specificity reversals, AUC decompositions, and regression-based reconstructions are mathematically sound. Each case will include threshold analysis, information loss assessment, and confidence interval propagation concerns. Examples from your provided data will be evaluated against established biostatistical literature. The memo will cite authoritative guidelines from sources including Pepe's ROC curve methodology papers, DeLong et al. on AUC estimation, and relevant sections from Fawcett's technical framework. Where back-calculations introduce unacceptable assumptions or data loss, alternative analytical approaches will be flagged. Delivery includes the written memo plus one structured call to walk through methodological reasoning for your specific hsROC context and clarify edge cases in your reference excerpts.

@warrick1718

This project immediately caught my attention because it is exactly the type of work I do best. Your need for guidance on "reverse-engineering" true-positive and false-negative counts from summary measures like sensitivity and specificity aligns perfectly with my expertise. I understand the importance of ensuring that your meta-analyses are built on clean, professional, and user-friendly methodologies. While I am new to freelancer, I have tons of experience and have done other projects off site. My background includes extensive work in medical statistics and data interpretation, enabling me to deliver clear memos and provide authoritative citations for your case studies. If this sounds like what you're looking for I'd love to hear more about your project. Regards, Warrick Van Eeden

@gustavoadolfog1

My background combines laboratory science, analytical method validation, and statistical analysis, giving me extensive experience interpreting diagnostic performance metrics and scientific research. I routinely work with sensitivity, specificity, predictive values, ROC analysis, logistic regression, and validation of analytical methods, always with an emphasis on scientific rigor and reproducibility. For this project, I can: * Review published studies and determine whether reconstruction of TP, FP, FN, and TN is methodologically justified. * Explain the assumptions required when deriving confusion matrices from reported sensitivity, specificity, prevalence, sample size, logistic regression outputs, or ROC-derived metrics. * Prepare a concise, evidence-based memo summarizing which approaches are appropriate, which should be avoided, and why. * Support recommendations with references from peer-reviewed methodological literature and established guidance on diagnostic test accuracy meta-analysis, including HSROC and related approaches.