Sample size re‐estimation in clinical trials

Adaptive clinical trials are becoming very popular because of their flexibility in allowing mid-stream changes of sample size, endpoints, populations, etc. At the same time, they have been regarded with mistrust because they can produce bizarre results in very extreme settings. Understanding the advantages and disadvantages of these rapidly developing methods is a must. This paper reviews flexible methods for sample size re-estimation when the outcome is continuous.

[1]  T. Friede,et al.  A comparison of methods for adaptive sample size adjustment , 2001, Statistics in medicine.

[2]  A. Gould,et al.  Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance , 1992 .

[3]  C. Stein.  A Two-Sample Test for a Linear Hypothesis Whose Power is Independent of the Variance , 1945 .

[4]  C. Burman,et al.  Are Flexible Designs Sound? , 2006, Biometrics.

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

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

[7]  M A Proschan,et al.  An improved double sampling procedure based on the variance. , 2000, Biometrics.

[8]  T. Friede,et al.  Re-calculating the sample size in internal pilot study designs with control of the type I error rate. , 2000, Statistics in medicine.

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

[10]  K E Muller,et al.  Exact test size and power of a Gaussian error linear model for an internal pilot study. , 1999, Statistics in medicine.

[11]  M. Proschan,et al.  Internal pilot studies I: type I error rate of the naive t-test. , 1999, Statistics in medicine.

[12]  W. Brannath,et al.  Recursive Combination Tests , 2002 .

[13]  Christian Sonesson,et al.  Surveillance in Longitudinal Models: Detection of Intrauterine Growth Restriction , 2004, Biometrics.

[14]  K E Muller,et al.  Controlling test size while gaining the benefits of an internal pilot design. , 2001, Biometrics.

[15]  K K Lan,et al.  The B-value: a tool for monitoring data. , 1988, Biometrics.

[16]  Biao Xing,et al.  A method to estimate the variance of an endpoint from an on-going blinded trial. , 2005, Statistics in medicine.

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

[18]  J. Wittes,et al.  The role of internal pilot studies in increasing the efficiency of clinical trials. , 1990, Statistics in medicine.

[19]  G. Bray,et al.  Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet , 2001 .

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

[21]  D. Zucker,et al.  Sequential monitoring of clinical trials: the role of information and Brownian motion. , 1993, Statistics in medicine.

[22]  L Fisher,et al.  Statistical Inference for Self‐Designing Clinical Trials with a One‐Sided Hypothesis , 1999, Biometrics.

[23]  Frank Miller,et al.  Variance Estimation in Clinical Studies with Interim Sample Size Reestimation , 2005, Biometrics.

[24]  D. Zucker,et al.  Internal pilot studies II: comparison of various procedures. , 1999, Statistics in medicine.

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