Adaptive increase in sample size when interim results are promising: A practical guide with examples

This paper discusses the benefits and limitations of adaptive sample size re-estimation for phase 3 confirmatory clinical trials. Comparisons are made with more traditional fixed sample and group sequential designs. It is seen that the real benefit of the adaptive approach arises through the ability to invest sample size resources into the trial in stages. The trial starts with a small up-front sample size commitment. Additional sample size resources are committed to the trial only if promising results are obtained at an interim analysis. This strategy is shown through examples of actual trials, one in neurology and one in cardiology, to be more advantageous than the fixed sample or group sequential approaches in certain settings. A major factor that has generated controversy and inhibited more widespread use of these methods has been their reliance on non-standard tests and p-values for preserving the type-1 error. If, however, the sample size is only increased when interim results are promising, one can dispense with these non-standard methods of inference. Therefore, in the spirit of making adaptive increases in trial size more widely appealing and readily implementable we here define those promising circumstances in which a conventional final inference can be performed while preserving the overall type-1 error. Methodological, regulatory and operational issues are examined.

[1]  M A Proschan,et al.  Designed extension of studies based on conditional power. , 1995, Biometrics.

[2]  W. Brannath,et al.  Exact Confidence Bounds Following Adaptive Group Sequential Tests , 2009, Biometrics.

[3]  P. Bauer,et al.  The reassessment of trial perspectives from interim data—a critical view , 2006, Statistics in medicine.

[4]  P. Bauer,et al.  Evaluation of experiments with adaptive interim analyses. , 1994, Biometrics.

[5]  Anastasios A. Tsiatis,et al.  Flexible Sample Size Considerations Using Information-Based Interim Monitoring , 2001 .

[6]  P. Armitage,et al.  Repeated Significance Tests on Accumulating Data , 1969 .

[7]  T. Fleming,et al.  Adaptive Methods: Telling “The Rest of the Story” , 2010, Journal of biopharmaceutical statistics.

[8]  S. Pocock When (not) to stop a clinical trial for benefit. , 2005, JAMA.

[9]  Sue-Jane Wang,et al.  Modification of Sample Size in Group Sequential Clinical Trials , 1999, Biometrics.

[10]  HansâHelge Müller,et al.  Adaptive Group Sequential Designs for Clinical Trials: Combining the Advantages of Adaptive and of Classical Group Sequential Approaches , 2001 .

[11]  Christopher Jennison,et al.  Mid‐course sample size modification in clinical trials based on the observed treatment effect , 2002, Statistics in medicine.

[12]  K. K. Lan,et al.  Discrete sequential boundaries for clinical trials , 1983 .

[13]  Anastasios A. Tsiatis,et al.  Interim Monitoring of Group Sequential Trials Using Spending Functions for the Type I and Type II Error Probabilities , 2001 .

[14]  J. S. D. Cani,et al.  Group sequential designs using a family of type I error probability spending functions. , 1990, Statistics in medicine.

[15]  W. Lehmacher,et al.  Adaptive Sample Size Calculations in Group Sequential Trials , 1999, Biometrics.

[16]  Cyrus Mehta,et al.  Optimizing Trial Design: Sequential, Adaptive, and Enrichment Strategies , 2009, Circulation.

[17]  J. Ware,et al.  Sample Size Re-Estimation for Adaptive Sequential Design in Clinical Trials , 2008, Journal of biopharmaceutical statistics.

[18]  Martin Posch,et al.  Repeated confidence intervals for adaptive group sequential trials , 2007, Statistics in medicine.

[19]  H. Schäfer,et al.  Adaptive Group Sequential Designs for Clinical Trials: Combining the Advantages of Adaptive and of Classical Group Sequential Approaches , 2001, Biometrics.

[20]  A. Tsiatis,et al.  On the inefficiency of the adaptive design for monitoring clinical trials , 2003 .

[21]  D. DeMets,et al.  Increasing the sample size when the unblinded interim result is promising , 2004, Statistics in medicine.

[22]  P. O'Brien,et al.  A multiple testing procedure for clinical trials. , 1979, Biometrics.

[23]  Martin Posch,et al.  Issues in designing flexible trials , 2003, Statistics in medicine.

[24]  B. Turnbull,et al.  Group Sequential Methods with Applications to Clinical Trials , 1999 .

[25]  Werner Brannath,et al.  Multiplicity and flexibility in clinical trials , 2007, Pharmaceutical statistics.