Biostatistician — Independent Data Validation for Digital Health Publications
Independent Biostatistician
Real-World Evidence Validation for Peer-Reviewed Publication
Project Overview
The Tapping Solution is the world’s leading Emotional Freedom Techniques (EFT) app with 32 million+ completed sessions. We are preparing peer-reviewed publications using real-world evidence (RWE) from our app’s session data.
We need an independent biostatistician to validate our data cleaning pipeline, verify our statistical models, and co-sign a Data Quality Attestation Letter that will be shared with academic collaborators at Yale, Harvard/MGH, UCLA, and other institutions.
This is not a traditional analysis-from-scratch engagement. We have already completed preliminary analyses. Your role is to independently verify and certify our work to publication standards.
Scope of Work
You will validate multiple datasets sliced from a single master data export. Datasets cover conditions including anxiety, sleep, chronic pain, depression, rumination, and others — each with hundreds of thousands to millions of sessions. We’ll provide datasets on a rolling basis as they’re prepared.
Deliverables Per Dataset
1.Data Cleaning Verification — Review our Python cleaning scripts. Confirm inclusion/exclusion criteria match our CONSORT flow diagram. Run your own independent checks on the master export.
2. Statistical Model Review — Verify our Linear Mixed Effects (LME) models, effect size calculations, and sensitivity analyses. Confirm model selection is appropriate for repeated-measures app data.
3. Descriptive Statistics & Summaries — Generate publication-ready descriptive tables (demographics, session counts, completion rates, distributions).
4. Formal Data Dictionary — Review and certify the data dictionary for each cleaned dataset.
5. Data Quality Attestation Letter — A signed letter confirming the datasets meet standards for peer-reviewed publication. This letter will be shared with academic collaborators.
6. Methods Section Draft Review — Verify that our statistical methods sections (pre-drafted) accurately describe the analyses performed.
What We Provide to You
We’ve done significant prep work to minimize your hours:
• Master data export with SHA-256 hash and chain of custody documentation
• Complete Python cleaning scripts (fully commented, reproducible pipeline)
• Draft data dictionaries for each dataset
• Preliminary descriptive statistics and effect size calculations
• Pre-drafted methods sections for your review
• Data Provenance & Validation Protocol documenting our entire process
• CONSORT-style flow diagrams showing session inclusion/exclusion
AI Transparency Note: Some of our draft materials were generated with AI assistance (Claude Opus 4.6). All analytical decisions were made by humans. Your independent verification is what makes these materials publication-ready. We will fully disclose AI involvement in all publications per our AI Transparency Protocol.
Required Qualifications
• PhD or Master’s in Biostatistics, Epidemiology, or related quantitative field
• Experience with real-world evidence (RWE) or observational health data
• Proficiency in R or Python for statistical analysis
• Familiarity with LME/GEE models for repeated measures
• Experience contributing to peer-reviewed publications (named in methods or acknowledgments)
• Understanding of STROBE/RECORD reporting guidelines
Preferred (Not Required)
• Experience with digital health / mHealth app data
• Familiarity with pre/post outcome measures (Likert scales, PROs)
• Previous work with mental health or pain outcomes research
• Willingness to be acknowledged in publications as independent statistician
Apply tot his job
Apply To this Job
Real-World Evidence Validation for Peer-Reviewed Publication
Project Overview
The Tapping Solution is the world’s leading Emotional Freedom Techniques (EFT) app with 32 million+ completed sessions. We are preparing peer-reviewed publications using real-world evidence (RWE) from our app’s session data.
We need an independent biostatistician to validate our data cleaning pipeline, verify our statistical models, and co-sign a Data Quality Attestation Letter that will be shared with academic collaborators at Yale, Harvard/MGH, UCLA, and other institutions.
This is not a traditional analysis-from-scratch engagement. We have already completed preliminary analyses. Your role is to independently verify and certify our work to publication standards.
Scope of Work
You will validate multiple datasets sliced from a single master data export. Datasets cover conditions including anxiety, sleep, chronic pain, depression, rumination, and others — each with hundreds of thousands to millions of sessions. We’ll provide datasets on a rolling basis as they’re prepared.
Deliverables Per Dataset
1.Data Cleaning Verification — Review our Python cleaning scripts. Confirm inclusion/exclusion criteria match our CONSORT flow diagram. Run your own independent checks on the master export.
2. Statistical Model Review — Verify our Linear Mixed Effects (LME) models, effect size calculations, and sensitivity analyses. Confirm model selection is appropriate for repeated-measures app data.
3. Descriptive Statistics & Summaries — Generate publication-ready descriptive tables (demographics, session counts, completion rates, distributions).
4. Formal Data Dictionary — Review and certify the data dictionary for each cleaned dataset.
5. Data Quality Attestation Letter — A signed letter confirming the datasets meet standards for peer-reviewed publication. This letter will be shared with academic collaborators.
6. Methods Section Draft Review — Verify that our statistical methods sections (pre-drafted) accurately describe the analyses performed.
What We Provide to You
We’ve done significant prep work to minimize your hours:
• Master data export with SHA-256 hash and chain of custody documentation
• Complete Python cleaning scripts (fully commented, reproducible pipeline)
• Draft data dictionaries for each dataset
• Preliminary descriptive statistics and effect size calculations
• Pre-drafted methods sections for your review
• Data Provenance & Validation Protocol documenting our entire process
• CONSORT-style flow diagrams showing session inclusion/exclusion
AI Transparency Note: Some of our draft materials were generated with AI assistance (Claude Opus 4.6). All analytical decisions were made by humans. Your independent verification is what makes these materials publication-ready. We will fully disclose AI involvement in all publications per our AI Transparency Protocol.
Required Qualifications
• PhD or Master’s in Biostatistics, Epidemiology, or related quantitative field
• Experience with real-world evidence (RWE) or observational health data
• Proficiency in R or Python for statistical analysis
• Familiarity with LME/GEE models for repeated measures
• Experience contributing to peer-reviewed publications (named in methods or acknowledgments)
• Understanding of STROBE/RECORD reporting guidelines
Preferred (Not Required)
• Experience with digital health / mHealth app data
• Familiarity with pre/post outcome measures (Likert scales, PROs)
• Previous work with mental health or pain outcomes research
• Willingness to be acknowledged in publications as independent statistician
Apply tot his job
Apply To this Job