Simulation‐based sequential design
We review some simulation‐based methods to implement optimal decisions in sequential design problems as they naturally arise in clinical trial design. As a motivating example we use a stylized version of a dose‐ranging design in the ASTIN trial. The approach can be characterized as constrained backw...
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| Published in | Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 21; no. 4; pp. 729 - 739 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Inc
01.07.2022
Wiley Subscription Services, Inc |
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| Abstract | We review some simulation‐based methods to implement optimal decisions in sequential design problems as they naturally arise in clinical trial design. As a motivating example we use a stylized version of a dose‐ranging design in the ASTIN trial. The approach can be characterized as constrained backward induction. The nature of the constraint is a restriction of the decisions to a set of actions that are functions of the current history only implicitly through a low‐dimensional summary statistic. In addition, the action set is restricted to time‐invariant policies. Time‐dependence is only introduced indirectly through the change of the chosen summary statistic over time. This restriction allows computationally efficient solutions to the sequential decision problem. A further simplification is achieved by restricting optimal actions to be described by decision boundaries on the space of such summary statistics. |
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| AbstractList | Abstract
We review some simulation‐based methods to implement optimal decisions in sequential design problems as they naturally arise in clinical trial design. As a motivating example we use a stylized version of a dose‐ranging design in the ASTIN trial. The approach can be characterized as constrained backward induction. The nature of the constraint is a restriction of the decisions to a set of actions that are functions of the current history only implicitly through a low‐dimensional summary statistic. In addition, the action set is restricted to time‐invariant policies. Time‐dependence is only introduced indirectly through the change of the chosen summary statistic over time. This restriction allows computationally efficient solutions to the sequential decision problem. A further simplification is achieved by restricting optimal actions to be described by decision boundaries on the space of such summary statistics. We review some simulation‐based methods to implement optimal decisions in sequential design problems as they naturally arise in clinical trial design. As a motivating example we use a stylized version of a dose‐ranging design in the ASTIN trial. The approach can be characterized as constrained backward induction. The nature of the constraint is a restriction of the decisions to a set of actions that are functions of the current history only implicitly through a low‐dimensional summary statistic. In addition, the action set is restricted to time‐invariant policies. Time‐dependence is only introduced indirectly through the change of the chosen summary statistic over time. This restriction allows computationally efficient solutions to the sequential decision problem. A further simplification is achieved by restricting optimal actions to be described by decision boundaries on the space of such summary statistics. |
| Author | Duan, Yunshan Müller, Peter Garcia Tec, Mauricio |
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| References_xml | – year: 1985 – volume: V year: 2001 – volume: 17 start-page: 997 year: 2007 end-page: 1004 article-title: Discussion of the “white paper of the PhRMA working group on adaptive dose‐ranging designs publication-title: J Biopharm Stat – volume: 14 start-page: 75 year: 2013 end-page: 86 article-title: Sequential stopping for high‐throughput hypothesis testing experiments publication-title: Biostatistics – volume: 2 start-page: 487 issue: 4 year: 2010 end-page: 512 article-title: A simulation study to compare new adaptive dose–ranging designs publication-title: Stat Biopharm Res – volume: 90 start-page: 1322 issue: 432 year: 1995 end-page: 1330 article-title: Optimal design via curve fitting of Monte Carlo experiments publication-title: J Am Stat Assoc – volume: 55 start-page: 971 issue: 3 year: 1999 end-page: 977 article-title: Decision theoretic designs for phase II clinical trials with multiple outcomes publication-title: Biometrics – volume: 137 start-page: 3140 year: 2006 end-page: 3150 article-title: Simulation‐based sequential Bayesian design publication-title: J Stat Plan Inference – volume: 17 start-page: 965 issue: 6 year: 2007 end-page: 995 article-title: Innovative approaches for designing and analyzing adaptive dose‐ranging trials publication-title: J Biopharm Stat – volume: 54 start-page: 964 year: 1998 end-page: 975 article-title: Approaches for optimal sequential decision analysis in clinical trials publication-title: Biometrics – volume: 34 start-page: 2543 year: 2003 end-page: 2548 article-title: Acute stroke therapy by inhibition of neutrophils (ASTIN): an adaptive dose‐response study of UK‐279,276 in acute ischemic stroke publication-title: Stroke – volume: 8 start-page: 595 year: 2007 end-page: 608 article-title: Screening designs for drug development publication-title: Biostatistics – year: 2004 – year: 2003 – volume: 12 start-page: 566 year: 2003 end-page: 584 article-title: A gridding method for Bayesian sequential decision problems publication-title: J Comput Graph Stat – volume: 157 start-page: 357 year: 1994 end-page: 387 article-title: Bayesian approaches to randomized trials publication-title: J R Stat Soc, Ser A, Gen – volume: 33 start-page: 1646 issue: 10 year: 2014 end-page: 1661 article-title: Model‐based dose finding under model uncertainty using general parametric models publication-title: Stat Med – volume: 3 start-page: 197 year: 2006 end-page: 207 article-title: A Bayesian decision‐theoretic dose finding trial publication-title: Decis Anal – volume: 2 start-page: 340 issue: 4 year: 2005 end-page: 351 article-title: ASTIN: a Bayesian adaptive dose–response trial in acute stroke publication-title: Clin Trials – volume: 61 start-page: 738 issue: 3 year: 2005 end-page: 748 article-title: Combining multiple comparisons and modeling techniques in dose‐response studies publication-title: Biometrics – volume: 7 start-page: 279 issue: 1 year: 2020 end-page: 301 article-title: Q‐learning: theory and applications publication-title: Annu Rev Stat Appl – ident: e_1_2_9_3_1 doi: 10.1002/0471729000 – ident: e_1_2_9_15_1 doi: 10.1002/sim.6052 – ident: e_1_2_9_5_1 doi: 10.2307/2983527 – volume-title: Dynamic Programming year: 2003 ident: e_1_2_9_17_1 contributor: fullname: Bellman R – ident: e_1_2_9_22_1 doi: 10.1080/01621459.1995.10476636 – ident: e_1_2_9_23_1 doi: 10.1007/978-1-4757-4286-2 – ident: e_1_2_9_16_1 doi: 10.1080/10543400701643855 – ident: e_1_2_9_4_1 doi: 10.2307/2533849 – ident: e_1_2_9_6_1 doi: 10.1111/j.0006-341X.1999.00971.x – ident: e_1_2_9_7_1 – ident: e_1_2_9_8_1 doi: 10.1146/annurev-statistics-031219-041220 – ident: e_1_2_9_9_1 doi: 10.1198/1061860032274 – ident: e_1_2_9_14_1 doi: 10.1111/j.1541-0420.2005.00344.x – ident: e_1_2_9_20_1 doi: 10.1093/biostatistics/kxl031 – ident: e_1_2_9_10_1 doi: 10.1287/deca.1060.0079 – ident: e_1_2_9_21_1 doi: 10.1093/biostatistics/kxs026 – volume-title: Case Studies in Bayesian Statistics year: 2001 ident: e_1_2_9_2_1 contributor: fullname: Berry DA – ident: e_1_2_9_11_1 doi: 10.1016/j.jspi.2006.05.021 – ident: e_1_2_9_12_1 doi: 10.1080/10543400701643848 – ident: e_1_2_9_19_1 doi: 10.1191/1740774505cn094oa – ident: e_1_2_9_18_1 doi: 10.1161/01.STR.0000092527.33910.89 – ident: e_1_2_9_13_1 doi: 10.1198/sbr.2010.09045 |
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| Title | Simulation‐based sequential design |
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