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Grant Details

Grant Number: 5R01CA018332-27 Interpret this number
Primary Investigator: Demets, David
Organization: University Of Wisconsin Madison
Project Title: Statistical Problems in Cancer Research
Fiscal Year: 2001


Abstract

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.



Publications

Increasing The Sample Size When The Unblinded Interim Result Is Promising
Authors: Chen Y.H. , DeMets D.L. , Lan K.K. .
Source: Statistics In Medicine, 2004-04-15 00:00:00.0; 23(7), p. 1023-38.
PMID: 15057876
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Surrogate End Points In Clinical Trials: Are We Being Misled?
Authors: Fleming T.R. , DeMets D.L. .
Source: Annals Of Internal Medicine, 1996-10-01 00:00:00.0; 125(7), p. 605-13.
PMID: 8815760
Related Citations

Self-modelling With Random Shift And Scale Parameters And A Free-knot Spline Shape Function
Authors: Lindstrom M.J. .
Source: Statistics In Medicine, 1995-09-30 00:00:00.0; 14(18), p. 2009-21.
PMID: 8677401
Related Citations

Group Sequential Comparison Of Changes: Ad-hoc Versus More Exact Method
Authors: Lee J.W. , DeMets D.L. .
Source: Biometrics, 1995 Mar; 51(1), p. 21-30.
PMID: 7766776
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Global Comparison Of Radiation And Chemotherapy Dose-response Curves With A Test For Interaction
Authors: Lindstrom M.J. , Kunugi K.A. , Kinsella T.J. .
Source: Radiation Research, 1993 Aug; 135(2), p. 269-77.
PMID: 7690150
Related Citations

Sample Size Determination For Group Sequential Clinical Trials With Immediate Response
Authors: Kim K. , Demets D.L. .
Source: Statistics In Medicine, 1992 Jul; 11(10), p. 1391-9.
PMID: 1518999
Related Citations

Analysis Of Longitudinal Data With Unmeasured Confounders
Authors: Palta M. , Yao T.J. .
Source: Biometrics, 1991 Dec; 47(4), p. 1355-69.
PMID: 1786323
Related Citations

Nonlinear Mixed Effects Models For Repeated Measures Data
Authors: Lindstrom M.L. , Bates D.M. .
Source: Biometrics, 1990 Sep; 46(3), p. 673-87.
PMID: 2242409
Related Citations

Group Sequential Procedures: Calendar Versus Information Time
Authors: Demets D.L. .
Source: Statistics In Medicine, 1989 Oct; 8(10), p. 1191-8.
PMID: 2814068
Related Citations

Changing Frequency Of Interim Analysis In Sequential Monitoring
Authors: Lan K.K. , DeMets D.L. .
Source: Biometrics, 1989 Sep; 45(3), p. 1017-20.
PMID: 2790114
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Design And Analysis Of Phase I Clinical Trials
Authors: Storer B.E. .
Source: Biometrics, 1989 Sep; 45(3), p. 925-37.
PMID: 2790129
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Confidence Intervals Following Group Sequential Tests In Clinical Trials
Authors: Kim K. , DeMets D.L. .
Source: Biometrics, 1987 Dec; 43(4), p. 857-64.
PMID: 3427170
Related Citations

Practical Aspects In Data Monitoring: A Brief Review
Authors: Demets D.L. .
Source: Statistics In Medicine, 1987 Oct-Nov; 6(7), p. 753-60.
PMID: 3321314
Related Citations

Some Considerations In The Analysis Of Rates Of Change In Longitudinal Studies
Authors: Palta M. , Cook T. .
Source: Statistics In Medicine, 1987 Jul-Aug; 6(5), p. 599-611.
PMID: 3659670
Related Citations

Methods For Combining Randomized Clinical Trials: Strengths And Limitations
Authors: Demets D.L. .
Source: Statistics In Medicine, 1987 Apr-May; 6(3), p. 341-50.
PMID: 3616287
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Use Of Logrank Tests And Group Sequential Methods At Fixed Calendar Times
Authors: DeMets D.L. , Gail M.H. .
Source: Biometrics, 1985 Dec; 41(4), p. 1039-44.
PMID: 4096915
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Investigating Maximum Power Losses In Survival Studies With Nonstratified Randomization
Authors: Palta M. .
Source: Biometrics, 1985 Jun; 41(2), p. 497-504.
PMID: 4027324
Related Citations

A Biological Marker Model For Predicting Disease Transitions
Authors: Klein J.P. , Klotz J.H. , Grever M.R. .
Source: Biometrics, 1984 Dec; 40(4), p. 927-36.
PMID: 6598390
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