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Reconsidering some aspects of the two-trials paradigm.

Journal of biopharmaceutical statistics

Maca J, Gallo P, Branson M, Maurer W.
PMID: 12413234
J Biopharm Stat. 2002 May;12(2):107-19. doi: 10.1081/bip-120006450.

A common standard for the demonstration of efficacy in a clinical submission is a statistically significant outcome in at least two pivotal trials ("two-trials convention"). When the data structures in different trials are sufficiently similar to allow pooling of...

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Maca J, Gallo P, Branson M, et al. Reconsidering some aspects of the two-trials paradigm. J Biopharm Stat. 2002;12(2):107-19doi: 10.1081/bip-120006450.
Maca, J., Gallo, P., Branson, M., & Maurer, W. (2002). Reconsidering some aspects of the two-trials paradigm. Journal of biopharmaceutical statistics, 12(2), 107-19. https://doi.org/10.1081/bip-120006450
Maca, J, et al. "Reconsidering some aspects of the two-trials paradigm." Journal of biopharmaceutical statistics vol. 12,2 (2002): 107-19. doi: https://doi.org/10.1081/bip-120006450
Maca J, Gallo P, Branson M, Maurer W. Reconsidering some aspects of the two-trials paradigm. J Biopharm Stat. 2002 May;12(2):107-19. doi: 10.1081/bip-120006450. PMID: 12413234.
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Where is health-related quality-of-life assessment in oncology clinical study heading?.

Journal of biopharmaceutical statistics

Weng CS.
PMID: 15027496
J Biopharm Stat. 2004 Feb;14(1):1-3. doi: 10.1081/bip-120028502.

No abstract available.

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Weng CS. Where is health-related quality-of-life assessment in oncology clinical study heading?. J Biopharm Stat. 2004;14(1):1-3doi: 10.1081/bip-120028502.
Weng, C. S. (2004). Where is health-related quality-of-life assessment in oncology clinical study heading?. Journal of biopharmaceutical statistics, 14(1), 1-3. https://doi.org/10.1081/bip-120028502
Weng, C S Wayne. "Where is health-related quality-of-life assessment in oncology clinical study heading?." Journal of biopharmaceutical statistics vol. 14,1 (2004): 1-3. doi: https://doi.org/10.1081/bip-120028502
Weng CS. Where is health-related quality-of-life assessment in oncology clinical study heading?. J Biopharm Stat. 2004 Feb;14(1):1-3. doi: 10.1081/bip-120028502. PMID: 15027496.
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Hypotheses and type I error in active-control noninferiority trials.

Journal of biopharmaceutical statistics

Chen G, Wang YC, Chi GY.
PMID: 15206528
J Biopharm Stat. 2004 May;14(2):301-13. doi: 10.1081/BIP-120037181.

A fundamental assumption in the design and analysis of an active-control noninferiority trial is that the active control is truly effective. If this assumption does not hold, i.e., the active control is not effective, a harmful drug may be...

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Chen G, Wang YC, Chi GY. Hypotheses and type I error in active-control noninferiority trials. J Biopharm Stat. 2004;14(2):301-13doi: 10.1081/BIP-120037181.
Chen, G., Wang, Y. C., & Chi, G. Y. (2004). Hypotheses and type I error in active-control noninferiority trials. Journal of biopharmaceutical statistics, 14(2), 301-13. https://doi.org/10.1081/BIP-120037181
Chen, Gang, et al. "Hypotheses and type I error in active-control noninferiority trials." Journal of biopharmaceutical statistics vol. 14,2 (2004): 301-13. doi: https://doi.org/10.1081/BIP-120037181
Chen G, Wang YC, Chi GY. Hypotheses and type I error in active-control noninferiority trials. J Biopharm Stat. 2004 May;14(2):301-13. doi: 10.1081/BIP-120037181. PMID: 15206528.
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Sample size computation for two-sample noninferiority log-rank test.

Journal of biopharmaceutical statistics

Jung SH, Kang SJ, McCall LM, Blumenstein B.
PMID: 16279355
J Biopharm Stat. 2005;15(6):969-79. doi: 10.1080/10543400500265736.

When an experimental therapy is less extensive, less toxic, or less expensive than a standard therapy, we may want to prove that the former is not worse than the latter through a noninferiority trial. In this article, we discuss...

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Jung SH, Kang SJ, McCall LM, et al. Sample size computation for two-sample noninferiority log-rank test. J Biopharm Stat. 2005;15(6):969-79doi: 10.1080/10543400500265736.
Jung, S. H., Kang, S. J., McCall, L. M., & Blumenstein, B. (2005). Sample size computation for two-sample noninferiority log-rank test. Journal of biopharmaceutical statistics, 15(6), 969-79. https://doi.org/10.1080/10543400500265736
Jung, Sin-Ho, et al. "Sample size computation for two-sample noninferiority log-rank test." Journal of biopharmaceutical statistics vol. 15,6 (2005): 969-79. doi: https://doi.org/10.1080/10543400500265736
Jung SH, Kang SJ, McCall LM, Blumenstein B. Sample size computation for two-sample noninferiority log-rank test. J Biopharm Stat. 2005;15(6):969-79. doi: 10.1080/10543400500265736. PMID: 16279355.
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Overview of agreement statistics for medical devices.

Journal of biopharmaceutical statistics

Lin L.
PMID: 18161545
J Biopharm Stat. 2008;18(1):126-44. doi: 10.1080/10543400701668290.

This paper is an overview that summarizes recently developed tools in assessing agreement for methods comparison and instrument/assay validation in medical devices. This paper emphasizes concept, sample sizes, and examples more than analytical formulas. We have considered a unified...

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Lin L. Overview of agreement statistics for medical devices. J Biopharm Stat. 2008;18(1):126-44doi: 10.1080/10543400701668290.
Lin, L. (2008). Overview of agreement statistics for medical devices. Journal of biopharmaceutical statistics, 18(1), 126-44. https://doi.org/10.1080/10543400701668290
Lin, Lawrence. "Overview of agreement statistics for medical devices." Journal of biopharmaceutical statistics vol. 18,1 (2008): 126-44. doi: https://doi.org/10.1080/10543400701668290
Lin L. Overview of agreement statistics for medical devices. J Biopharm Stat. 2008;18(1):126-44. doi: 10.1080/10543400701668290. PMID: 18161545.
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Power and sample size calculation for microarray studies.

Journal of biopharmaceutical statistics

Jung SH, Young SS.
PMID: 22204525
J Biopharm Stat. 2012;22(1):30-42. doi: 10.1080/10543406.2010.500066.

Microarray is a technology to screen a large number of genes to discover those differentially expressed between clinical subtypes or different conditions of human diseases. Gene discovery using microarray data requires adjustment for the large-scale multiplicity of candidate genes....

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Jung SH, Young SS. Power and sample size calculation for microarray studies. J Biopharm Stat. 2012;22(1):30-42doi: 10.1080/10543406.2010.500066.
Jung, S. H., & Young, S. S. (2012). Power and sample size calculation for microarray studies. Journal of biopharmaceutical statistics, 22(1), 30-42. https://doi.org/10.1080/10543406.2010.500066
Jung, Sin-Ho, and Young, S Stanley. "Power and sample size calculation for microarray studies." Journal of biopharmaceutical statistics vol. 22,1 (2012): 30-42. doi: https://doi.org/10.1080/10543406.2010.500066
Jung SH, Young SS. Power and sample size calculation for microarray studies. J Biopharm Stat. 2012;22(1):30-42. doi: 10.1080/10543406.2010.500066. PMID: 22204525; PMCID: PMC3324127.
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Resampling methods for model fitting and model selection.

Journal of biopharmaceutical statistics

Babu GJ.
PMID: 22023685
J Biopharm Stat. 2011 Nov;21(6):1177-86. doi: 10.1080/10543406.2011.607749.

Resampling procedures for fitting models and model selection are considered in this article. Nonparametric goodness-of-fit statistics are generally based on the empirical distribution function. The distribution-free property of these statistics does not hold in the multivariate case or when...

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Babu GJ. Resampling methods for model fitting and model selection. J Biopharm Stat. 2011;21(6):1177-86doi: 10.1080/10543406.2011.607749.
Babu, G. J. (2011). Resampling methods for model fitting and model selection. Journal of biopharmaceutical statistics, 21(6), 1177-86. https://doi.org/10.1080/10543406.2011.607749
Babu, G Jogesh. "Resampling methods for model fitting and model selection." Journal of biopharmaceutical statistics vol. 21,6 (2011): 1177-86. doi: https://doi.org/10.1080/10543406.2011.607749
Babu GJ. Resampling methods for model fitting and model selection. J Biopharm Stat. 2011 Nov;21(6):1177-86. doi: 10.1080/10543406.2011.607749. PMID: 22023685.
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Guest editors' note: Special issue on subgroup analysis in clinical trials.

Journal of biopharmaceutical statistics

Wang SJ, Dmitrienko A.
PMID: 24392974
J Biopharm Stat. 2014;24(1):1-3. doi: 10.1080/10543406.2014.858958.

No abstract available.

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Wang SJ, Dmitrienko A. Guest editors' note: Special issue on subgroup analysis in clinical trials. J Biopharm Stat. 2014;24(1):1-3doi: 10.1080/10543406.2014.858958.
Wang, S. J., & Dmitrienko, A. (2014). Guest editors' note: Special issue on subgroup analysis in clinical trials. Journal of biopharmaceutical statistics, 24(1), 1-3. https://doi.org/10.1080/10543406.2014.858958
Wang, Sue-Jane, and Dmitrienko, Alex. "Guest editors' note: Special issue on subgroup analysis in clinical trials." Journal of biopharmaceutical statistics vol. 24,1 (2014): 1-3. doi: https://doi.org/10.1080/10543406.2014.858958
Wang SJ, Dmitrienko A. Guest editors' note: Special issue on subgroup analysis in clinical trials. J Biopharm Stat. 2014;24(1):1-3. doi: 10.1080/10543406.2014.858958. PMID: 24392974.
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Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies.

Journal of biopharmaceutical statistics

Wang X, Bai F, Pang H, George SL.
PMID: 31286838
J Biopharm Stat. 2019;29(4):592-605. doi: 10.1080/10543406.2019.1633659. Epub 2019 Jul 09.

For time-to-event outcomes, the Kaplan-Meier estimator is commonly used to estimate survival functions of treatment groups and to compute marginal treatment effects, such as the difference in survival rates between treatments at a landmark time. The derived estimates of...

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Wang X, Bai F, Pang H, et al. Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies. J Biopharm Stat. 2019;29(4):592-605doi: 10.1080/10543406.2019.1633659.
Wang, X., Bai, F., Pang, H., & George, S. L. (2019). Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies. Journal of biopharmaceutical statistics, 29(4), 592-605. https://doi.org/10.1080/10543406.2019.1633659
Wang, Xiaofei, et al. "Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies." Journal of biopharmaceutical statistics vol. 29,4 (2019): 592-605. doi: https://doi.org/10.1080/10543406.2019.1633659
Wang X, Bai F, Pang H, George SL. Bias-adjusted Kaplan-Meier survival curves for marginal treatment effect in observational studies. J Biopharm Stat. 2019;29(4):592-605. doi: 10.1080/10543406.2019.1633659. Epub 2019 Jul 09. PMID: 31286838; PMCID: PMC6684083.
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Methods for the analysis of multiple endpoints in small populations: A review.

Journal of biopharmaceutical statistics

Ristl R, Urach S, Rosenkranz G, Posch M.
PMID: 29985752
J Biopharm Stat. 2019;29(1):1-29. doi: 10.1080/10543406.2018.1489402. Epub 2018 Jul 09.

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome...

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Ristl R, Urach S, Rosenkranz G, et al. Methods for the analysis of multiple endpoints in small populations: A review. J Biopharm Stat. 2018;29(1):1-29doi: 10.1080/10543406.2018.1489402.
Ristl, R., Urach, S., Rosenkranz, G., & Posch, M. (2019). Methods for the analysis of multiple endpoints in small populations: A review. Journal of biopharmaceutical statistics, 29(1), 1-29. https://doi.org/10.1080/10543406.2018.1489402
Ristl, Robin, et al. "Methods for the analysis of multiple endpoints in small populations: A review." Journal of biopharmaceutical statistics vol. 29,1 (2019): 1-29. doi: https://doi.org/10.1080/10543406.2018.1489402
Ristl R, Urach S, Rosenkranz G, Posch M. Methods for the analysis of multiple endpoints in small populations: A review. J Biopharm Stat. 2019;29(1):1-29. doi: 10.1080/10543406.2018.1489402. Epub 2018 Jul 09. PMID: 29985752.
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On sample size determination in multi-armed confirmatory adaptive designs.

Journal of biopharmaceutical statistics

Wassmer G.
PMID: 21516570
J Biopharm Stat. 2011 Jul;21(4):802-17. doi: 10.1080/10543406.2011.551336.

An important application of confirmatory adaptive designs is the data-driven selection of treatment arms in multi-armed trials. A general methodology for adaptive designs is based on the combination testing principle. Using this principle, selection of treatment arms in multi-armed...

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Wassmer G. On sample size determination in multi-armed confirmatory adaptive designs. J Biopharm Stat. 2011;21(4):802-17doi: 10.1080/10543406.2011.551336.
Wassmer, G. (2011). On sample size determination in multi-armed confirmatory adaptive designs. Journal of biopharmaceutical statistics, 21(4), 802-17. https://doi.org/10.1080/10543406.2011.551336
Wassmer, Gernot. "On sample size determination in multi-armed confirmatory adaptive designs." Journal of biopharmaceutical statistics vol. 21,4 (2011): 802-17. doi: https://doi.org/10.1080/10543406.2011.551336
Wassmer G. On sample size determination in multi-armed confirmatory adaptive designs. J Biopharm Stat. 2011 Jul;21(4):802-17. doi: 10.1080/10543406.2011.551336. PMID: 21516570.
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Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients.

Journal of biopharmaceutical statistics

Chen J, Girard M, Wang S, Kisfalvi K, Lirio R.
PMID: 34882518
J Biopharm Stat. 2021 Dec 09;1-16. doi: 10.1080/10543406.2021.2009500. Epub 2021 Dec 09.

With recent advances in machine learning, we demonstrated the use of supervised machine learning to optimize the prediction of treatment outcomes of vedolizumab through iterative optimization using VARSITY and VISIBLE 1 data in patients with moderate-to-severe ulcerative colitis. The...

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Chen J, Girard M, Wang S, et al. Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients. J Biopharm Stat. 2021;1-16doi: 10.1080/10543406.2021.2009500.
Chen, J., Girard, M., Wang, S., Kisfalvi, K., & Lirio, R. (2021). Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients. Journal of biopharmaceutical statistics, 1-16. https://doi.org/10.1080/10543406.2021.2009500
Chen, Jingjing, et al. "Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients." Journal of biopharmaceutical statistics vol. (2021): 1-16. doi: https://doi.org/10.1080/10543406.2021.2009500
Chen J, Girard M, Wang S, Kisfalvi K, Lirio R. Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients. J Biopharm Stat. 2021 Dec 09;1-16. doi: 10.1080/10543406.2021.2009500. Epub 2021 Dec 09. PMID: 34882518.
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Showing 1 to 12 of 99 entries