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Pharmacy China 2015 Paper 11 Why Statistical Analysis Plan (SAP) should be comprehensive? Ridden Merchant, PPD (I) Pvt. Ltd., Bengaluru, India Faith Prayankotveettil, PPD (I) Pvt. Ltd., Bengaluru,

How to fill out why statistical analysis plan

How to fill out why statistical analysis plan

1. Start by understanding the purpose of the statistical analysis plan (SAP).
2. Familiarize yourself with the study protocol, objectives, and data collection methods.
3. Identify the statistical methods and techniques needed to analyze the collected data.
4. Determine the specific variables and populations of interest.
5. Create an outline or template for the SAP, including sections such as introduction, objectives, methods, analysis plan, and data interpretation.
6. Begin with the introduction, providing an overview of the study and its goals.
7. Clearly state the objectives of the analysis and the research questions to be answered.
8. Describe the statistical methods to be used, including any assumptions or limitations.
9. Provide detailed information on the data management and data cleaning procedures.
10. Outline the analysis plan, including the statistical tests or models to be employed.
11. Discuss any sensitivity analyses or subgroup analyses planned.
12. Describe how missing data will be handled.
13. Include a section on data interpretation, presenting the main findings and their implications.
14. Consider seeking feedback or input from statisticians or other experts to improve the SAP.
15. Once completed, review and revise the SAP as needed before finalizing.

Who needs why statistical analysis plan?

1. Researchers and scientists conducting clinical trials or research studies.
2. Pharmaceutical and biotech companies developing new drugs or therapies.
3. Regulatory bodies and agencies reviewing study protocols and statistical analysis plans.
4. Grant funding organizations assessing research proposals.
5. Data analysts and statisticians involved in data analysis and reporting.
6. Medical journal editors and reviewers evaluating study protocols and statistical analyses.
7. Academic institutions and universities conducting research projects.
8. Healthcare professionals and physicians involved in evidence-based medicine.
9. Public health organizations and policymakers analyzing data for decision-making.
10. Quality control and assurance teams ensuring data accuracy and validity.

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