Therapeutic and prevention clinical trials in cancer and other major diseases with mortality or irreversible morbidity are typically monitored to detect early evidence of benefit or harm, for ethical and scientific reasons. However, repeated interim analyses using conventional statistical methods will increase the likelihood of false positive claims of treatment effect. In 1983, Lan and DeMets (Biometrika) extended earlier work of Pocock, O'Brien and Fleming and others by proposing a flexible group sequential plan using an alpha spending function. However, trials terminated early may exaggerate treatment benefits or harm. We previously evaluated the degree of bias in the estimate of treatment effect and proposed bias correction estimators for the linear mixed effects model and the proportional hazards model in survival analysis. In this proposal, we continue our exploration of bias and examine a conditioned estimate of treatment effect and its properties. The condition is on the actual time of early termination. We also compare this to estimators previously evaluated by Whitehead and others. In addition, we consider the problem of allocating the total alpha level to two outcomes, one simple and the other a composite, in a sequential design. In a particular case, the simple outcome (e.g. death) is a component of the composite outcome (e.g. death plus disease recurrence or hospitalization). This issue is of particular interest to regulatory agencies where a trial is designed mainly to find a treatment effect on the composite outcome but the monitoring focuses heavily on the simple outcome. We also develop a method for dropping inferior aims in a randomized Phase II/III design, selecting the best dose for comparision based on the primary clinical outcome. For many trials such as in prevention, a best dose cannot be selected using a surrogate but requires some followup using a clinical outcome. However, following multiple arms for a clinical outcome may not be a feasible or affordable. Our proposed design allows for starting with multiple dosages, following each arm for a period of time and dropping inferior arms. Ultimately, a loading dose will be selected sequentially and composed to the control arm. This design results in efficiency in allowing patients in the leading arm to be used in the Phase III comparison and also saves time. These design issues were motivated by trials encountered in our collaboration with cancer center investigators.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA018332-26
Application #
6172231
Study Section
Special Emphasis Panel (ZRG1-STA (01))
Program Officer
Erickson, Burdette (BUD) W
Project Start
1978-07-01
Project End
2002-06-30
Budget Start
2000-07-01
Budget End
2001-06-30
Support Year
26
Fiscal Year
2000
Total Cost
$103,647
Indirect Cost
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Chen, Y H Joshua; DeMets, David L; Lan, K K Gordon (2004) Increasing the sample size when the unblinded interim result is promising. Stat Med 23:1023-38
Fleming, T R; DeMets, D L (1996) Surrogate end points in clinical trials: are we being misled? Ann Intern Med 125:605-13
Lindstrom, M J (1995) Self-modelling with random shift and scale parameters and a free-knot spline shape function. Stat Med 14:2009-21
Lee, J W; DeMets, D L (1995) Group sequential comparison of changes: ad-hoc versus more exact method. Biometrics 51:21-30
Lindstrom, M J; Kunugi, K A; Kinsella, T J (1993) Global comparison of radiation and chemotherapy dose-response curves with a test for interaction. Radiat Res 135:269-77
Kim, K; Demets, D L (1992) Sample size determination for group sequential clinical trials with immediate response. Stat Med 11:1391-9
Palta, M; Yao, T J (1991) Analysis of longitudinal data with unmeasured confounders. Biometrics 47:1355-69
Lindstrom, M L; Bates, D M (1990) Nonlinear mixed effects models for repeated measures data. Biometrics 46:673-87
Lan, K K; DeMets, D L (1989) Changing frequency of interim analysis in sequential monitoring. Biometrics 45:1017-20
Storer, B E (1989) Design and analysis of phase I clinical trials. Biometrics 45:925-37

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