I can help you conduct data analysis in BioStatistics using R/Python/Stata and write report
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Project Details
Why Hire Me?
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With over 7 years of experience in data analysis, I specialize in applying advanced biostatistical methods across medical, public health, and life sciences domains. I deliver precise statistical results and clear, publication-ready reports tailored to your research goals.
Why My Biostatistics Service Stands Out:
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Proficient in survival analysis, logistic regression, multivariate modeling, and time-to-event analysis
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Skilled in R, Python, and Stata for flexible tool selection based on project needs
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Strong record of working with clinical trial, epidemiological, and genomic datasets
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Capable of end-to-end delivery: cleaning, analysis, visualization, and report writing
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Confidentiality assured with strict adherence to research ethics and data security
What I Need From You to Start the Project
To begin your biostatistical analysis with accuracy and alignment, please provide the following:
1) Project Scope and Objectives
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Summary of your research problem or clinical study
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Specific hypotheses or research questions to be tested
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Preferred software (R, Python, or Stata)
2) Dataset and Structure
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Clean or raw data files (CSV, Excel, SPSS, etc.)
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Data dictionary or codebook (variable definitions and labels)
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Details on data collection method (e.g., survey, experiment, trial)
3) Statistical Methods Needed
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Type of analysis (e.g., survival, regression, repeated measures, ANOVA)
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Any specific modeling requirements or assumptions
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Literature references or methodology preferences (if any)
4) Reporting Expectations
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Format of final report (Word, PDF, Markdown)
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Whether you need visualizations (tables, graphs, Kaplan-Meier curves, etc.)
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Intended audience (academic, corporate, medical) to adapt language complexity
5) Data Privacy and Compliance
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Any consent, IRB, or GDPR-related requirements
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Anonymization expectations or masking sensitive identifiers
6) Timeline and Milestones
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Deadline for final submission
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Timeline for draft reviews and feedback incorporation
7) Communication Preferences
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Preferred method (Email, WhatsApp, Zoom)
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Availability for clarifying queries and providing feedback during analysis
8) Post-Delivery Support
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Whether you need help interpreting the report or answering reviewer comments
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Support period (e.g., one week after delivery) for clarification or edits
Portfolio
Survival Analysis of Vaccination Impact on Elderly Mortality
Explore how regional vaccination coverage affects mortality in elderly populations using Cox regression and Kaplan-Meier survival curves. A data-driven biostatistics case study using R for public health insights.
Longitudinal Study on Lifestyle Interventions and Blood Pressure
Analyze monthly blood pressure changes in hypertensive patients using mixed-effects models. This biostatistics case study demonstrates the impact of diet, exercise, and medication over time using Python.
ICU Admission Prediction Using EHR and Machine Learning
Build and compare logistic regression and random forest models to predict ICU admission using electronic health record data. A healthcare analytics case study demonstrating real-world applications of machine learning in Python.
Process
Customer Reviews
5 reviews for this Gig ★★★★☆ 4
The interpretation of complex epidemiological data was accurate and the report was detailed and easy to follow.
Very knowledgeable in biostatistics and patient with feedback, though initial clarification on methodology was needed.
The Python-based data cleaning and visualizations helped me present my thesis results more clearly.
Excellent handling of logistic regression in Stata, though I had to ask for some edits in the final formatting.
The survival analysis in R was done with precision and the report was publication-ready.






