The aim of this unique volume is to help medical researchers design clinical trials to improve survival, remission duration, or time to recurrence of disease. Written in a user-friendly step-by-step format, this work enables the researcher-with no background in statistics-to determine sample size and write statistical considerations for their protocols. It provides critical language which can help with FDA submissions and/or research grants. It also provides the mathematical justification of the material at a level consistent with one year of undergraduate mathematical statistics. It presents survival analysis methods at a more elementary level than any known text. Filled with tables, figures, plus an extensive appendix, this one-of-a-kind reference is an absolute must for all clinical researchers and biostatisticians.
Table of Contents
1. How to use the sample size tables 2. Identification of parameters 3. References 4. Design and analysis of randomized clinical trials 5. Formulation of the therapeutic question 6. One sided vs. Two-sided question 7. Design of the clinical trial 8. Statistical considerations 9. Conduct of the trial 10. Analysis and reporting of the trial 11. Crucial elements 12. Binomial comparison 13. Kaplan-meier comparison (large sample) 14. Logrank test 15. References 16. Derivation of the statistical results 17. Derivation of the large sample distribution of the logrank statistic 18. Derivation of the large sample distribution of the kaplan-meier statistic 19. Difference between kaplan-meier curves 20. Exponential survival 21. Application of the logrank test when survival is exponential 22. Exponential survival with a poisson accrual process 23. Extension to the two-sample problem 24. Exponential survival with "up-front" accrual 25. Proportional hazard models and the exponential distribution 26. Consideration in planning a trial under proportional hazards: putting it all together 27. Losses to follow-up and sample size adjustment 28. Interpretation of the tables 29. Multi-treatment trials 30. Type a 31. Type b 32. Type c 33. Type d 34. Stratified logrank test 35. Advice on stratification 36. Intuitive justification why the logrank test and kaplan-meier estimation for actual accrual process behave in the limit in the same way as the fixed binomial assumption 37. Alternate standard error for the kaplan-meier estimator 38. Connection between kaplan-meier with binomial 39. References 40. Figures. Appendix i: a review of mathematical statistics. Appendix ii: tables. Index.
Jonathan Shuster is Research Professor and Biostatistician, General Clinical Research Center at the University of Florida.