%0 Journal Article %1 Banerjee2006 %A Banerjee, Anindita %A Tsiatis, Anastasios A %D 2006 %J Statistics in medicine %K ClinicalTrials Humans PhaseIIasTopic PhaseIIasTopic:methods ResearchDesign SampleSize %N 19 %P 3382-95 %R 10.1002/sim.2501 %T Adaptive two-stage designs in phase II clinical trials. %U http://www.ncbi.nlm.nih.gov/pubmed/16479547 %V 25 %X Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective or not after the accumulation of the data at the end of each stage. Optimal fixed two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon when the primary outcome is a binary response. This paper proposes an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision-theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion. %@ 0277-6715

@article{Banerjee2006, abstract = {Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective or not after the accumulation of the data at the end of each stage. Optimal fixed two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon when the primary outcome is a binary response. This paper proposes an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision-theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Banerjee, Anindita and Tsiatis, Anastasios A}, biburl = {https://www.bibsonomy.org/bibtex/26ef5f2e92fe50d11cca695386064ebdf/jepcastel}, city = {Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. abanerj2@ncsu.edu}, doi = {10.1002/sim.2501}, interhash = {295da3d1fd08977f4e200ebab8d00d82}, intrahash = {6ef5f2e92fe50d11cca695386064ebdf}, isbn = {0277-6715}, issn = {0277-6715}, journal = {Statistics in medicine}, keywords = {ClinicalTrials Humans PhaseIIasTopic PhaseIIasTopic:methods ResearchDesign SampleSize}, month = {10}, note = {5259<m:linebreak></m:linebreak>LR: 20071115; CI: Copyright 2006; GR: R01-CA051962/CA/NCI NIH HHS/United States; GR: R37-AI031789/AI/NIAID NIH HHS/United States; JID: 8215016; ppublish;<m:linebreak></m:linebreak>Fase II}, number = 19, pages = {3382-95}, pmid = {16479547}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Adaptive two-stage designs in phase II clinical trials.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16479547}, volume = 25, year = 2006 }