Sage Journals: Discover world-class research

Abstract

Clinical trials (CTs) are often monitored for efficacy or futility. Several methods for interim monitoring of CTs have been developed. Although informative, few of these methods convey information regarding effect sizes (eg, treatment differences), and none use prediction to convey information regarding potential effect size estimates and associated precision, with trial continuation. We propose use of prediction and specifically “predicted intervals” (PIs) as a flexible and practical tool for quantitative monitoring of CTs. PIs provide information regarding effect sizes, are invariant to study design, and provide flexibility in the decision-making process. We outline construction of PIs for binary, continuous, and time-to-event endpoints and present examples of their use. PIs provide a valuable tool for Data Monitoring Committees.

Keywords

Data monitoring Prediction Predicted intervals Interim analyses Clinical trials

Get full access to this article

View all access options for this article.

References

O'Brien

Fleming

. A multiple testing procedure for clinical trials. Biometrics. 1979;35:549–556.

Pocock

. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64:191–199.

Peto

Pike

Armitrage

Design and analysis of randomized clinical trials requiring prolonged observations of each patient. I. Introduction and design. Br J Cancer. 1976;34:585–612.

Lan

KKG

DeMets

. Discrete sequential boundaries for clinical trials. Biometrika. 1983; 70(3):659–663.

Wang

Tsiatis

. Approximately optimal one-parameter boundaries for group sequential trials. Biometrics. 1987;43:193–199.

Whitehead

. The Design and Analysis of Sequential Clinical Trials. New York, NY: Halstead Press; 1983.

Whitehead

. Sequential methods based on boundaries approach for the clinical comparison of survival times. Stat Med. 1994;13:1357–1368.

Jennison

Turnbull

. Group Sequential Methods With Applications to Clinical Trials. Boca Raton, FL: CRC Press; 2000:171–189.

Chang

Chuang-Stein

. Type I error and power in trials with one interim futility analysis. Pharm Stat. 2004;3:51–59.

10.

Berntsen

Rasmussen

. Monitoring a randomized clinical trial for futility: the North-Norwegian Lidocaine Intervention Trial. Stat Med. 1991;10:405–412.

11.

EAST Users Manual, 2003.

12.

Ware

Muller

Braunwald

. The futility index: an approach to the cost-effective termination of randomized clinical trials. Am J Med. 1985;78:635–643.

13.

Pampallona

Tsiatis

. Group sequential designs for one-sided and two-sided hypothesis testing with provision for early stopping in favor of the null hypothesis. J Stat Plann Infer. 1994;42:19–35.

14.

Pampallona

Tsiatis

Kim

. Interim monitoring of group sequential trials using spending functions for the type I and type II error probabilities. Drug Inf J. 2001;35:1113–1121.

Data Monitoring in Clinical Trials Using Prediction

Abstract

Keywords

Get full access to this article

References