Please obtain Lang and Secic's book Reporting Statistics to complete this topic. You need to work through the book, and do the quiz at the end. This book is the textbook used in the USP course, they charge you more than $4,000 to do what I am giving you in this topic. I organized the USP course and it is their most popular course. It is entirely online and asynchronous; they go through the textbook and answer questions on an online forum, and in the end, prepare a PowerPoint presentation on a disease or drug that has a lot of statistics associated with it (that is why the CDC is there).
What we are trying with this topic is a 2-week intensive program: each day you read 2 chapters and we have an hour interaction going through the chapter and making sure everyone understands what iis there.
Some medical writers find this topic the easiest thing they have ever done, some find it impossibly difficult.
When you have finished this topic and passed the quizzes, you will get a separate MiMW Certificate on Reporting Statistics.
You may skip over this topic and do it later, but you do need to complete it to get your MiMW Certificate in Medical Writing.
Why statistics? Starting from the beginning, statistical analysis was invented because the entire population could not be sampled. Look at the costs and difficulty of asking the entire population of the United States a few simple questions in the Census. Supposing you want to know how many young women in the Bronx color their hair mahogany. If you don't know how many young women are in the Bronx, the question becomes absurd.
The clinical study report is the gold standard for reporting results of a clinical trial. The clinical study report has 3 data sections: demographics, efficacy and safety. Each data section can be huge, and may involve 300 subjects. A drug can only been seen as having the right balance of safety and efficacy if the data has seen to be statistically significant. You want to see how important statistical analysis is, look at this huge GSK study report. Download here.
1. The demographics section: the population taking active drug needs to be seen to be coming from the same population as the population taking placebo. Or, the population taking effective marketed drug needs to be seen to be coming from the same population as effective marketed drug plus active drug.
2. The efficacy section: the population taking the active drug needs to be seen to be responding differently from the population taking the active drug; or the population taking active drug needs to be seen to be identical to the population taking active drug plus an effective marketed drug.
4. The safety section: the population taking a drug will be having side effects that are not seen in the placebo population. The statistical analysis needs to make sure that the side effects from the drug are not killing or injuring the patients worse than the disease.
Read Lang and Secic textbook from start to finish and do the exercises.
Table of Contents of Lang and Secic
A Note to the Reader Foreword Preface to the First Edition Preface to the Second Edition Acknowledgments Introduction Differences Between Clinical and Statistical Significance Part 1: Guidelines for Reporting Statistics in Medicine 1. Summarizing Data: Reporting Numbers and Descriptive Statistics 2. Comparing Probabilities of Events: Reporting Measures of Risk 3. Generalizing from a Sample to a Population: Reporting Estimates and Confidence Intervals 4. Comparing Groups with P Values: Reporting Hypothesis Tests 5. Adjusting for Many P Values: The Multiple Testing Problem 6. Testing for Relationships: Reporting Association and Correlation Analyses 7. Predicting Values from One or More Variables: Reporting Regression Analyses 8. Analyzing Groups with Multiple Variables: Reporting Analysis of Variance (ANOVA) 9. Assessing Time-to-Event as an Endpoint: Reporting Survival Analyses 10. Determining the Presence or Absence of Disease: Reporting the Performance Characteristics of Diagnostic Tests 11. Considering "Prior Probabilities:" Reporting Bayesian Statistical Analyses 12. Describing Patterns of Disease and Disability in Populations: Reporting Epidemiological Measures Part 2: Guidelines for Reporting Research Designs and Activities 13. Testing Interventions in Experimental Studies: Reporting Randomized Controlled Trials 14. Observing Prospectively, from Exposure to Outcome: Reporting Cohort or Longitudinal Studies 15. Observing Retrospectively, from Outcome to Exposure: Reporting Case-Control Studies 16. Observing Exposures and Outcomes Concurrently: Reporting Surveys or Cross-Sectional Studies Part 3: Guidelines for Reporting Integrated Research Methods 17. Synthesizing the Results of Related Studies: Reporting Systematic Reviews and Meta-Analyses 18. Weighing the Costs and Consequences of Therapy: Reporting Economic Evaluations 19. Informing Treatment Choices: Reporting Decision Analyses and Clinical Practice Guidelines Part 4: Guidelines for Presenting Data and Statistics in Tables and Figures 20. Tabular Displays of Data and Statistics: Reporting Values, Groups, and Comparisons in Tables 21. Visual Displays of Data and Statistics: Reporting Values, Groups, and Comparisons in Figures Part 5: A Guide to Statistical Terms and Tests Part 6: Appendices Appendix 1: Rules for Presenting Numbers in Text Appendix 2: Mathematical Symbols and Notations Appendix 3: Spellings of Statistical Terms and Tests Appendix 4: Links to other Sets of Reporting Guidelines Appendix 5: Sources of Error, Confounding, and Bias in Biomedical Research About the Authors References Index
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