Interval-Censored Time-to-Event Data

Interval-Censored Time-to-Event Data

by Ding-Geng Chen, Jianguo Sun, Karl E. Peace
Released July 2012
Publisher(s): Chapman and Hall/CRC
ISBN: 9781466504288

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Book description

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research.Divid

Table of contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Dedication
  4. Contents (1/2)
  5. Contents (2/2)
  6. List of Figures
  7. List of Tables
  8. Preface
  9. List of Contributors
  10. I. Introduction and Overview
    1. 1. Overview of Recent Developments for Interval-Censored Data (1/6)
    2. 1. Overview of Recent Developments for Interval-Censored Data (2/6)
    3. 1. Overview of Recent Developments for Interval-Censored Data (3/6)
    4. 1. Overview of Recent Developments for Interval-Censored Data (4/6)
    5. 1. Overview of Recent Developments for Interval-Censored Data (5/6)
    6. 1. Overview of Recent Developments for Interval-Censored Data (6/6)
    7. 2. A Review of Various Models for Interval-Censored Data (1/3)
    8. 2. A Review of Various Models for Interval-Censored Data (2/3)
    9. 2. A Review of Various Models for Interval-Censored Data (3/3)
  11. II. Methodology
    1. 3. Current Status Data in the Twenty-First Century (1/10)
    2. 3. Current Status Data in the Twenty-First Century (2/10)
    3. 3. Current Status Data in the Twenty-First Century (3/10)
    4. 3. Current Status Data in the Twenty-First Century (4/10)
    5. 3. Current Status Data in the Twenty-First Century (5/10)
    6. 3. Current Status Data in the Twenty-First Century (6/10)
    7. 3. Current Status Data in the Twenty-First Century (7/10)
    8. 3. Current Status Data in the Twenty-First Century (8/10)
    9. 3. Current Status Data in the Twenty-First Century (9/10)
    10. 3. Current Status Data in the Twenty-First Century (10/10)
    11. 4. Regression Analysis for Current Status Data (1/5)
    12. 4. Regression Analysis for Current Status Data (2/5)
    13. 4. Regression Analysis for Current Status Data (3/5)
    14. 4. Regression Analysis for Current Status Data (4/5)
    15. 4. Regression Analysis for Current Status Data (5/5)
    16. 5. Statistical Analysis of Dependent Current Status Data (1/8)
    17. 5. Statistical Analysis of Dependent Current Status Data (2/8)
    18. 5. Statistical Analysis of Dependent Current Status Data (3/8)
    19. 5. Statistical Analysis of Dependent Current Status Data (4/8)
    20. 5. Statistical Analysis of Dependent Current Status Data (5/8)
    21. 5. Statistical Analysis of Dependent Current Status Data (6/8)
    22. 5. Statistical Analysis of Dependent Current Status Data (7/8)
    23. 5. Statistical Analysis of Dependent Current Status Data (8/8)
    24. 6. Bayesian Semiparametric Regression Analysis of Interval- Censored Data with Monotone Splines (1/4)
    25. 6. Bayesian Semiparametric Regression Analysis of Interval- Censored Data with Monotone Splines (2/4)
    26. 6. Bayesian Semiparametric Regression Analysis of Interval- Censored Data with Monotone Splines (3/4)
    27. 6. Bayesian Semiparametric Regression Analysis of Interval- Censored Data with Monotone Splines (4/4)
    28. 7. Bayesian Inference of Interval-Censored Survival Data (1/6)
    29. 7. Bayesian Inference of Interval-Censored Survival Data (2/6)
    30. 7. Bayesian Inference of Interval-Censored Survival Data (3/6)
    31. 7. Bayesian Inference of Interval-Censored Survival Data (4/6)
    32. 7. Bayesian Inference of Interval-Censored Survival Data (5/6)
    33. 7. Bayesian Inference of Interval-Censored Survival Data (6/6)
    34. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (1/8)
    35. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (2/8)
    36. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (3/8)
    37. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (4/8)
    38. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (5/8)
    39. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (6/8)
    40. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (7/8)
    41. 8. Targeted Minimum Loss–Based Estimation of a Causal Effect Using Interval-Censored Time-to-Event Data (8/8)
    42. 9. Consistent Variance Estimation in Interval-Censored Data (1/8)
    43. 9. Consistent Variance Estimation in Interval-Censored Data (2/8)
    44. 9. Consistent Variance Estimation in Interval-Censored Data (3/8)
    45. 9. Consistent Variance Estimation in Interval-Censored Data (4/8)
    46. 9. Consistent Variance Estimation in Interval-Censored Data (5/8)
    47. 9. Consistent Variance Estimation in Interval-Censored Data (6/8)
    48. 9. Consistent Variance Estimation in Interval-Censored Data (7/8)
    49. 9. Consistent Variance Estimation in Interval-Censored Data (8/8)
  12. III. Applications and Related Software
    1. 10. Bias Assessment in Progression-Free Survival Analysis (1/8)
    2. 10. Bias Assessment in Progression-Free Survival Analysis (2/8)
    3. 10. Bias Assessment in Progression-Free Survival Analysis (3/8)
    4. 10. Bias Assessment in Progression-Free Survival Analysis (4/8)
    5. 10. Bias Assessment in Progression-Free Survival Analysis (5/8)
    6. 10. Bias Assessment in Progression-Free Survival Analysis (6/8)
    7. 10. Bias Assessment in Progression-Free Survival Analysis (7/8)
    8. 10. Bias Assessment in Progression-Free Survival Analysis (8/8)
    9. 11. Bias and Its Remedy in Interval-Censored Time-to-Event Applications (1/4)
    10. 11. Bias and Its Remedy in Interval-Censored Time-to-Event Applications (2/4)
    11. 11. Bias and Its Remedy in Interval-Censored Time-to-Event Applications (3/4)
    12. 11. Bias and Its Remedy in Interval-Censored Time-to-Event Applications (4/4)
    13. 12. Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke (1/4)
    14. 12. Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke (2/4)
    15. 12. Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke (3/4)
    16. 12. Adaptive Decision Making Based on Interval-Censored Data in a Clinical Trial to Optimize Rapid Treatment of Stroke (4/4)
    17. 13. Practical Issues on Using Weighted Logrank Tests (1/7)
    18. 13. Practical Issues on Using Weighted Logrank Tests (2/7)
    19. 13. Practical Issues on Using Weighted Logrank Tests (3/7)
    20. 13. Practical Issues on Using Weighted Logrank Tests (4/7)
    21. 13. Practical Issues on Using Weighted Logrank Tests (5/7)
    22. 13. Practical Issues on Using Weighted Logrank Tests (6/7)
    23. 13. Practical Issues on Using Weighted Logrank Tests (7/7)
    24. 14. glrt – New R Package for Analyzing Interval-Censored Survival Data (1/5)
    25. 14. glrt – New R Package for Analyzing Interval-Censored Survival Data (2/5)
    26. 14. glrt – New R Package for Analyzing Interval-Censored Survival Data (3/5)
    27. 14. glrt – New R Package for Analyzing Interval-Censored Survival Data (4/5)
    28. 14. glrt – New R Package for Analyzing Interval-Censored Survival Data (5/5)

Product information

  • Title: Interval-Censored Time-to-Event Data
  • Author(s): Ding-Geng Chen, Jianguo Sun, Karl E. Peace
  • Release date: July 2012
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781466504288