Statistical Methods for Dose-Finding Experiments

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ISBN: 9780470861233 | 0470861231
Cover: Hardcover
Copyright: 5/26/2006

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Hopital Saint-Louis, Paris, France. Reviews main statistical approaches for dose-finding in phase I/II clinical trials and presents practical guidance on correct use. Topics include basic concepts, philosophy and methodology, algorithm-based approaches, and more. For researchers. Expanded-outline format.

Contributors.

Preface.

Introduction (Sylvie Chevret).

Part I. General Principles and Controversial Issues in Dose-Findings.

1. Basic concepts in dose-finding (Sylvie Chevret).

1.1 Main concepts.

1.2 Main issues from a pharmaceutical point of view.

1.3 Statistical issues of dose-finding phase I trials.

1.3.1 Ethical concerns.

1.3.2 Design.

1.3.3 Inference.

1.4 Conclusion.

References.

2. Philosophy and methodology of dose-finding – a regulatory perspective (Robert Hemmings).

2.1 Introduction.

2.2 In search of the optimal dose.

2.2.1 What is an optimal dose?

2.2.2 When to determine the dose and duration of treatment?

2.2.3 Limitations of clinical trials.

2.2.4 Identification of the therapeutic window.

2.2.5 How many dose levels should be licensed?

2.2.6 Determining the duration of treatment.

2.3 Regulatory requirements for drug licensure.

2.3.1 Introduction.

2.3.2 The product licence.

2.3.3 ICH E4.

2.4 Benefits to the sponsor and the patient of providing data on dose-response over and above those required for licensing.

2.4.1 Increasing the likelihood that the product is a success.

2.4.2 Achieving consistent posology worldwide.

2.5 Trial designs for determining dose-response.

2.5.1 Introduction.

2.5.2 Issues relating to the design of a dose-response programme.

2.5.3 Designing a dose-finding trial for monotherapy.

2.5.4 Designing a dose-finding trial for a combination product.

2.5.5 Use of ‘adaptive designs’ in dose-response.

2.6 Discussion.

References.

Part II. Algorithm-Based Approaches.

3. Traditional and modified algorithm-based designs for phase I cancer clinical trials (Weichung Joe Shih and Yong Lin).

3.1 Introduction.

3.2 Notation and convention.

3.3 Traditional algorithm-based designs.

3.3.1 Traditional A + B design without dose de-escalation.

3.3.2 Traditional A + B design with dose de-escalation.

3.4 Modified algorithm-based designs.

3.4.1 M1 A + B design.

3.4.2 M2 A + B design.

3.4.3 M3 A + B design.

3.5 Probability of a dose being chosen as the MTD.

3.5.1 M1 A + B design.

3.5.2 M2 A + B design.

3.5.3 M3 A + B design.

3.6 Expected number of patients treated at each dose level.

3.6.1 M1 A + B design.

3.6.2 M2 A + B design.

3.6.3 M3 A + B design.

3.7 Other statistical properties.

3.8 Examples.

3.8.1 Example 1: M1 3 + 2 design without dose de-escalation.

3.8.2 Example 2: M2 3 + 3 design with dose de-escalation.

3.8.3 Example 3: M3 3 + 3 design without dose de-escalation.

3.9 Discussion.

Acknowledgments.

References.

4. Accelerated titration designs (Janet Dancey, Boris Freidlin and Larry Rubinstein)

4.1 Introduction.

4.2 Design.

4.2.1 Intrapatient dose-escalation.

4.3 Evaluation of performance.

4.4 Model-based analysis.

4.5 Clinical applications.

4.6 Conclusions.

References.

5. Group up-and-down designs in toxicity studies (Anna Ivanova and Nancy Flournoy).

5.1 Introduction.

5.2 Group up-and-down designs for phase I clinical trials.

5.3 Designs for acute toxicity studies.

5.4 Fully sequential designs for phase I clinical trials.

5.5 Start-up rules.

5.6 Estimation.

5.6.1 The empirical mean estimator.

5.6.2 The mode of the treatment distribution.

5.6.3 The maximum likelihood estimator (MLE).

5.6.4 The isotonic regression-based estimator.

5.7 Up-and-down designs to find the dose with maximum success probability.

5.7.1 An optimizing up-and-down design.

5.7.2 A balancing up-and-down design.

5.8 Discussion.

References.

Part III. Model-Based Approaches.

6. The continual reassessment method (Sylvie Chevret and Sarah Zohar).

6.1 Introduction.

6.2 The original continual reassessment method (CRM).

6.2.1 Trial planification.

6.2.2 Design and inference.

6.2.3 Inference.

6.2.4 Practical considerations.

6.2.5 CRM for phase II dose-finding studies.

6.2.6 Example.

6.3 Likelihood CRM (CRML).

6.4 Modified continual reassessment method (MCRM).

6.4.1 Modifications in design.

6.4.2 Modifications in modelling the dose-response relationship.

6.4.3 Example.

6.5 Concluding remarks.

References.

7. Using Bayesian decision theory in dose-escalation studies (John Whitehead).

7.1 Introduction.

7.2 Example of a dose-escalation study.

7.3 A statistical model for the study.

7.4 Prior information.

7.5 Gain functions.

7.6 Safety constraints and stopping rules.

7.7 Evaluation of the Bayesian approach.

7.8 Discussion.

References.

8. Dose-escalation with overdose control (Mourad Tighiouart and Andr´e Rogatko).

8.1 Introduction.

8.2 Escalation with overdose control design.

8.2.1 EWOC design.

8.2.2 Example.

8.3 Adjusting for covariates.

8.3.1 Model.

8.3.2 Example.

8.4 Choice of prior distributions.

8.4.1 Independent priors.

8.4.2 Correlated priors.

8.4.3 Simulations.

8.5 Concluding remarks.

References.

9. Dose-escalation methods for phase I healthy volunteer studies (Y. Zhou).

9.1 Introduction.

9.2 Frequentist analysis.

9.3 A Bayesian analysis.

9.4 Conducting dose-escalation using a Bayesian decision–theoretic approach.

9.5 Multiple simulations.

9.6 Conclusions.

Acknowledgements.

References.

Part IV. Future Trends for Past Issues.

10. Defining stopping rules (Sarah Zohar).

10.1 Introduction.

10.2 Background.

10.3 Dose-finding specificities.

10.3.1 Algorithm-based stopping rules.

10.3.2 Model-based stopping rules: frequentist approaches.

10.3.3 Model-based stopping rules: Bayesian approaches.

10.4 Examples.

10.4.1 The Nalmefene trial.

10.4.2 The Prantal trial.

10.4.3 The nitroglycerin trial.

10.5 Conclusions.

Acknowledgment.

References.

11. Dose-finding with delayed binary outcomes in cancer trials (Ying Kuen Cheung).

11.1 Introduction.

11.2 Review of current practice.

11.2.1 Group CRM.

11.2.2 Look-ahead methods.

11.2.3 Time-to-event methods.

11.3 Basic methods.

11.3.1 The CRM.

11.3.2 The TITE-CRM.

11.3.3 A dose-adjusted weight.

11.4 An example.

11.4.1 Intensity modulated radiation therapy (IMRT).

11.4.2 A single trial.

11.4.3 Weight calculation.

11.5 Simulation results.

11.5.1 The IMRT scenarios.

11.5.2 The late-onset scenarios.

11.5.3 Practical guidelines.

11.6 Chronic toxicities.

11.7 Discussion.

11.8 Bibliographic notes.

References.

12. Dose-finding based on multiple ordinal toxicities in phase I oncology trials (B. Nebiyou Bekele and Peter F. Thall).

12.1 Introduction.

12.2 Probability model.

12.3 Dose-finding algorithm.

12.4 Elicitation process.

12.5 Application to the sarcoma trial.

12.6 Simulation study and sensitivity analyses.

12.6.1 Simulation study design.

12.6.2 Simulation results.

12.6.3 Sensitivity analyses.

12.7 Concluding remarks.

References.

13. A two-stage design for dose-finding with two agents (Peter F. Thall)

13.1 Introduction.

13.2 Dose-toxicity model.

13.2.1 Toxicity probabilities.

13.2.2 Establishing priors.

13.3 A two-stage dose-finding algorithm.

13.3.1 Geometry of L1 and L2.

13.3.2 Dose-finding algorithm.

13.3.3 Criteria for stage 2.

13.3.4 Computing.

13.4 Application.

13.5 Discussion.

References.

14. Using both efficacy and toxicity for dose-finding (Peter F. Thall and John D. Cook).

14.1 Introduction.

14.2 Illustrative trial.

14.3 Dose-outcome models.

14.3.1 Bivariate binary probability distributions.

14.3.2 Priors and posteriors.

14.4 The dose-finding algorithm.

14.4.1 Acceptability limits.

14.4.2 Trade-off contours.

14.4.3 The algorithm.

14.5 Simulation studies.

14.6 Discussion.

References.

Part V. Conclusions.

15. Websites and software (Sarah Zohar).

15.1 Introduction.

15.2 Computation methods using statistical software.

15.3 Phase I or phase II dose-finding software.

15.3.1 Software for the continual reassessment method and its modifications.

15.3.2 Bayesian ADEPT: assisted decision making in early phase trials.

15.3.3 ATDPH1: accelerated titration designs for phase I clinical trials.

15.3.4 PMTD: traditional algorithm-based designs for phase I.

15.3.5 EWOC: escalation with overdose control.

15.4 Phase I/II dose-finding software.

15.4.1 EFFTOX2.

15.5 Conclusions.

References.

Appendix: random numbers generation.

Index.

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