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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
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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.

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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; 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; 125(7), p. 605-13.
PMID: 8815760
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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; 14(18), p. 2009-21.
PMID: 8677401
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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
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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
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Analysis of longitudinal data with unmeasured confounders.
Authors: Palta M. , Yao T.J. .
Source: Biometrics, 1991 Dec; 47(4), p. 1355-69.
PMID: 1786323
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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
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Group sequential procedures: calendar versus information time.
Authors: Demets D.L. .
Source: Statistics in medicine, 1989 Oct; 8(10), p. 1191-8.
PMID: 2814068
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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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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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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
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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
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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
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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
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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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