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Journal articles on the topic 'Randomization response'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Liu, Zhongqiang, Feifang Hu, and Li-Xin Zhang. "Nonparametric response-adaptive randomization for continuous responses." Pharmaceutical Statistics 17, no. 6 (2018): 781–96. http://dx.doi.org/10.1002/pst.1900.

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2

Cellamare, Matteo, Steffen Ventz, Elisabeth Baudin, Carole D. Mitnick, and Lorenzo Trippa. "A Bayesian response-adaptive trial in tuberculosis: The endTB trial." Clinical Trials 14, no. 1 (2016): 17–28. http://dx.doi.org/10.1177/1740774516665090.

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Purpose: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. Methods: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenar

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3

Hu, Feifang, William F. Rosenberger, and Li-Xin Zhang. "Asymptotically best response-adaptive randomization procedures." Journal of Statistical Planning and Inference 136, no. 6 (2006): 1911–22. http://dx.doi.org/10.1016/j.jspi.2005.08.011.

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4

Trueman, John W. H. "RANDOMIZATION CONFOUNDED: A RESPONSE TO CARPENTER." Cladistics 9, no. 1 (1993): 101–9. http://dx.doi.org/10.1111/j.1096-0031.1993.tb00211.x.

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5

Berger, Vance W. "A note on response-adaptive randomization." Contemporary Clinical Trials 40 (January 2015): 240. http://dx.doi.org/10.1016/j.cct.2014.12.013.

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6

Saville, Benjamin R., and Scott M. Berry. "Balanced covariates with response adaptive randomization." Pharmaceutical Statistics 16, no. 3 (2017): 210–17. http://dx.doi.org/10.1002/pst.1803.

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7

Viele, Kert, Kristine Broglio, Anna McGlothlin, and Benjamin R. Saville. "Comparison of methods for control allocation in multiple arm studies using response adaptive randomization." Clinical Trials 17, no. 1 (2019): 52–60. http://dx.doi.org/10.1177/1740774519877836.

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Background/Aims: Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting. Methods: We perform a simulation study to investigate

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8

Hung, Chou P., Benjamin M. Ramsden, and Anna Wang Roe. "Weakly Modulated Spike Trains: Significance, Precision, and Correction for Sample Size." Journal of Neurophysiology 87, no. 5 (2002): 2542–54. http://dx.doi.org/10.1152/jn.00420.2001.

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Many single-unit electrophysiological studies of visual cortex have investigated strong evoked responses to simple stimuli such as oriented gratings. Experiments involving other types of stimuli, such as natural scenes, higher-order features, and surface brightness, produce single-unit responses that are more difficult to interpret. Experiments with brightness, in particular, evoke single-unit responses that are typically weakly modulated. When the brightness is generated by a visual illusion such as the Cornsweet illusion, statistical tests are often necessary to distinguish true responses fr

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Biswas, Atanu, Rahul Bhattacharya, and Soumyadeep Das. "A multi-treatment response adaptive design for ordinal categorical responses." Statistical Methods in Medical Research 29, no. 3 (2019): 827–36. http://dx.doi.org/10.1177/0962280219846152.

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A multi-treatment response adaptive procedure is developed considering “comparison with the best” philosophy of multiple comparison procedures for clinical trials with ordinal categorical responses, when there is no shared control. For each response category, we arbitrarily create two outcome groups; one by combining the categories more favorable to it and the other by merging the categories at most as favorable to it and hence define the odds (i.e. cumulative odds). Pairwise ratios of such odds (i.e. cumulative odds ratios) based on pairs of treatments are combined conveniently to derive a me

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10

Proper, Jennifer, John Connett, and Thomas Murray. "Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes." Clinical Trials 18, no. 4 (2021): 417–26. http://dx.doi.org/10.1177/17407745211010139.

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Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasona

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11

Proschan, Michael, and Scott Evans. "Resist the Temptation of Response-Adaptive Randomization." Clinical Infectious Diseases 71, no. 11 (2020): 3002–4. http://dx.doi.org/10.1093/cid/ciaa334.

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Abstract Response-adaptive randomization (RAR) has recently gained popularity in clinical trials. The intent is noble: minimize the number of participants randomized to inferior treatments and increase the amount of information about better treatments. Unfortunately, RAR causes many problems, including (1) bias from temporal trends, (2) inefficiency in treatment effect estimation, (3) volatility in sample-size distributions that can cause a nontrivial proportion of trials to assign more patients to an inferior arm, (4) difficulty of validly analyzing results, and (5) the potential for selectio

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12

Li, Xuan, and Xikui Wang. "Variance-penalized response-adaptive randomization with mismeasurement." Journal of Statistical Planning and Inference 142, no. 7 (2012): 2128–35. http://dx.doi.org/10.1016/j.jspi.2012.02.016.

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13

Letsinger, Jennifer D., Raymond H. Myers, and Marvin Lentner. "Response Surface Methods for Bi-Randomization Structures." Journal of Quality Technology 28, no. 4 (1996): 381–97. http://dx.doi.org/10.1080/00224065.1996.11979697.

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14

Hatayama, Tomoyoshi, Satoshi Morita, and Kentaro Sakamaki. "Bayesian response adaptive randomization using longitudinal outcomes." Pharmaceutical Statistics 14, no. 5 (2015): 369–76. http://dx.doi.org/10.1002/pst.1695.

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15

Biswas, Atanu, and Rahul Bhattacharya. "Near efficient target allocations in response-adaptive randomization." Statistical Methods in Medical Research 25, no. 2 (2012): 807–20. http://dx.doi.org/10.1177/0962280212468378.

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16

Flournoy, Nancy, Linda M. Haines, and William F. Rosenberger. "A Graphical Comparison of Response-Adaptive Randomization Procedures." Statistics in Biopharmaceutical Research 5, no. 2 (2013): 126–41. http://dx.doi.org/10.1080/19466315.2013.782822.

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17

Batool, Fatima, and Javid Shabbir. "A generic randomization device for quantitative response variables." Communications in Statistics - Simulation and Computation 46, no. 8 (2016): 6399–413. http://dx.doi.org/10.1080/03610918.2016.1204456.

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18

Simon, Richard, and Noah Robin Simon. "Using randomization tests to preserve type I error with response adaptive and covariate adaptive randomization." Statistics & Probability Letters 81, no. 7 (2011): 767–72. http://dx.doi.org/10.1016/j.spl.2010.12.018.

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19

Vilakati, S., and G. Cortese. "Estimating survival distributions for time-varying SMART designs." South African Statistical Journal 53, no. 2 (2019): 115–31. http://dx.doi.org/10.37920/sasj.2019.53.2.4.

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Treatment of complex diseases such as cancer, HIV, leukemia and depression usually follows complex treatment sequences. In two-stage randomization designs, patients are randomized to first-stage treatments, and upon response, a second randomization to the second-stage treatments is done. The clinical goal in such trials is to achieve a response such as complete remission of leukemia, 50% shrinkage of solid tumor or increase in CD4 count in HIV patients. These responses are presumed to predict longer survival. The focus in two-stage randomization designs with survival endpoints is on estimating

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20

Zhang, Lanju, and William F. Rosenberger. "Response-adaptive randomization for survival trials: the parametric approach." Journal of the Royal Statistical Society: Series C (Applied Statistics) 56, no. 2 (2007): 153–65. http://dx.doi.org/10.1111/j.1467-9876.2007.00571.x.

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21

Liu, Zhongqiang, and Xuefeng Han. "Response-adaptive randomization using power function of hypothesis testing." Statistics and Its Interface 11, no. 3 (2018): 455–62. http://dx.doi.org/10.4310/sii.2018.v11.n3.a8.

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22

Assaid, Christopher. "The Theory of Response-Adaptive Randomization in Clinical Trials." Journal of the American Statistical Association 102, no. 480 (2007): 1479. http://dx.doi.org/10.1198/jasa.2007.s241.

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23

Smolen, HJ. "Comparison Of Adaptive Randomization Designs For Binary Response Trials." Value in Health 19, no. 3 (2016): A102. http://dx.doi.org/10.1016/j.jval.2016.03.1667.

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24

Zhang, Lanju, and William F. Rosenberger. "Response-Adaptive Randomization for Clinical Trials with Continuous Outcomes." Biometrics 62, no. 2 (2005): 562–69. http://dx.doi.org/10.1111/j.1541-0420.2005.00496.x.

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25

Carvalho, Carolina Muniz, Frank Wendt, Joel Gelernter, and Renato Polimanti. "155. Metabolome-Wide Mendelian Randomization Analysis of Trauma Response." Biological Psychiatry 85, no. 10 (2019): S64—S65. http://dx.doi.org/10.1016/j.biopsych.2019.03.169.

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26

Jiang, Fei, J. Jack Lee, and Peter Müller. "A Bayesian decision-theoretic sequential response-adaptive randomization design." Statistics in Medicine 32, no. 12 (2013): 1975–94. http://dx.doi.org/10.1002/sim.5735.

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27

Lavori, Philip W., and Ree Dawson. "Dynamic treatment regimes: practical design considerations." Clinical Trials 1, no. 1 (2004): 9–20. http://dx.doi.org/10.1191/1740774504cn002oa.

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Background Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (“sequential ignorability”). In longitudinal observational studies, sequential ignorability must be assumed, while ran

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28

Liu, Hao, Xiao Lin, and Xuelin Huang. "An oncology clinical trial design with randomization adaptive to both short- and long-term responses." Statistical Methods in Medical Research 28, no. 7 (2017): 2015–31. http://dx.doi.org/10.1177/0962280217744816.

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In oncology clinical trials, both short-term response and long-term survival are important. We propose an urn-based adaptive randomization design to incorporate both of these two outcomes. While short-term response can update the randomization probability quickly to benefit the trial participants, long-term survival outcome can also change the randomization to favor the treatment arm with definitive therapeutic benefit. Using generalized Friedman’s urn, we derive an explicit formula for the limiting distribution of the number of subjects assigned to each arm. With prior or hypothetical knowled

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29

Shreyans, Gupta*1 &. hrushabh Bhadkamkar2. "SURVEY PAPER ON PRIVACY PRESERVING DATA MINING TECHNIQUES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 164–68. https://doi.org/10.5281/zenodo.1002689.

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The main aim of Data mining techniques are to try to find out helpful patterns from the data that is big in quantity. These ideas or patterns are useful to find out some useful information. The abilities learned through fully knowledge mining approaches may contain confidential information about persons or trade. Upkeep of secrecy is a gigantic aspect of information mining also as a result be taught of attaining some information mining ambitions without dropping the secrecy of the individuals .The assessment of privacy preserving data mining (PPDM) algorithms must don’t forget the penalties of

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30

Hiddemann, Wolfgang, Roswitha Forstpointner, Martin Dreyling, et al. "Rituximab Maintenance Prolongs Response Duration after Salvage Therapy with R-FCM in Patients with Relapsed Follicular Lymphomas and Mantle Cell Lymphomas: Results of a Prospective Randomized Trial of the German Low Grade Lymphoma Study Group (GLSG)." Blood 106, no. 11 (2005): 920. http://dx.doi.org/10.1182/blood.v106.11.920.920.

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Abstract Preceding studies have shown that Rituximab prolongs the time to treatment failure (TTF) and response duration (RD) in follicular lymphoma (FL) when given either together with chemotherapy or as maintenance after a no R containing therapy. In the current study the impact of R maintenance on RD was evaluated after remission induction by an R-chemo combination. Patients with advanced stage relapsed or refractory FL and mantle cell lymphoma (MCL) were eligible. The study design comprized a first randomization between 4 courses of chemotherapy with Fludarabine (25mg/m2/d days 1–3), Cyclop

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31

Tehranisa, Jason S., and William J. Meurer. "Can Response-Adaptive Randomization Increase Participation in Acute Stroke Trials?" Stroke 45, no. 7 (2014): 2131–33. http://dx.doi.org/10.1161/strokeaha.114.005418.

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32

Wang, Lin, and Lu Cui. "Seamless Phase II/III Combination Study Through Response Adaptive Randomization." Journal of Biopharmaceutical Statistics 17, no. 6 (2007): 1177–87. http://dx.doi.org/10.1080/10543400701645322.

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33

Xu, Jiajing, and Guosheng Yin. "Two-stage adaptive randomization for delayed response in clinical trials." Journal of the Royal Statistical Society: Series C (Applied Statistics) 63, no. 4 (2013): 559–78. http://dx.doi.org/10.1111/rssc.12048.

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34

Du, Yining, Xuan Wang, and J. Jack Lee. "Simulation study for evaluating the performance of response-adaptive randomization." Contemporary Clinical Trials 40 (January 2015): 15–25. http://dx.doi.org/10.1016/j.cct.2014.11.006.

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35

Sverdlov, Oleksandr, William F. Rosenberger, and Yevgen Ryeznik. "Utility of Covariate-Adjusted Response-Adaptive Randomization in Survival Trials." Statistics in Biopharmaceutical Research 5, no. 1 (2013): 38–53. http://dx.doi.org/10.1080/19466315.2012.754376.

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36

Magirr, Dominic. "Block response-adaptive randomization in clinical trials with binary endpoints." Pharmaceutical Statistics 10, no. 4 (2010): 341–46. http://dx.doi.org/10.1002/pst.471.

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37

Ren, Zhenggang, Zhendong Chen, Weijia Fang, et al. "Landmark analysis of OS by response status in previously treated patients (pts) with advanced hepatocellular carcinoma (aHCC): Post hoc analysis of the KEYNOTE-394 study." Journal of Clinical Oncology 41, no. 16_suppl (2023): 4074. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.4074.

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4074 Background: KEYNOTE-224 and KEYNOTE-240 landmark analyses showed that objective response (OR) in pembrolizumab (pembro)-treated pts was prognostic of longer OS after the landmark. Landmark analysis was performed using the KEYNOTE-394 study to determine whether response assessment before landmark is prognostic of longer OS after landmark time in pembro-treated pts. Methods: Adults with confirmed HCC, radiographic progression during or after treatment with or intolerance to sorafenib or oxaliplatin-based chemotherapy were eligible for KEYNOTE-394. Landmark analyses of OS for responders, pts

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38

Maki, Robert G., J. Kyle Wathen, Shreyaskumar R. Patel, et al. "Randomized Phase II Study of Gemcitabine and Docetaxel Compared With Gemcitabine Alone in Patients With Metastatic Soft Tissue Sarcomas: Results of Sarcoma Alliance for Research Through Collaboration Study 002." Journal of Clinical Oncology 25, no. 19 (2007): 2755–63. http://dx.doi.org/10.1200/jco.2006.10.4117.

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Purpose Gemcitabine as a single agent and the combination of gemcitabine and docetaxel have activity in patients with metastatic soft tissue sarcoma. To determine if the addition of docetaxel to gemcitabine improved clinical outcome of patients with metastatic soft tissue sarcomas, we compared a fixed dose rate infusion of gemcitabine versus a lower dose of gemcitabine with docetaxel. Patients and Methods In this open-label phase II clinical trial, the primary end point was tumor response, defined as complete or partial response or stable disease lasting at least 24 weeks. A Bayesian adaptive

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39

Boonstra, Philip S., Alex Tabarrok, and Garth W. Strohbehn. "Targeted randomization dose optimization trials enable fractional dosing of scarce drugs." PLOS ONE 18, no. 10 (2023): e0287511. http://dx.doi.org/10.1371/journal.pone.0287511.

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Administering drug at a dose lower than that used in pivotal clinical trials, known as fractional dosing, can stretch scarce resources. Implementing fractional dosing with confidence requires understanding a drug’s dose-response relationship. Clinical trials aimed at describing dose-response in scarce, efficacious drugs risk underdosing, leading dose-finding trials to not be pursued despite their obvious potential benefit. We developed a new set of response-adaptive randomized dose-finding trials and demonstrate, in a series of simulated trials across diverse dose-response curves, these design

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40

Antognini, Alessandro Baldi, Alessandro Vagheggini, and Maroussa Zagoraiou. "Is the classical Wald test always suitable under response-adaptive randomization?" Statistical Methods in Medical Research 27, no. 8 (2016): 2294–311. http://dx.doi.org/10.1177/0962280216680241.

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The aim of this paper is to analyze the impact of response-adaptive randomization rules for normal response trials intended to test the superiority of one of two available treatments. Taking into account the classical Wald test, we show how response-adaptive methodology could induce a consistent loss of inferential precision. Then, we suggest a modified version of the Wald test which, by using the current allocation proportion to the treatments as a consistent estimator of the target, avoids some degenerate scenarios and so it should be preferable to the classical test. Furthermore, we show bo

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41

Wendler, David. "Response to Open Peer Commentaries on “Must Research Participants Understand Randomization?”." American Journal of Bioethics 9, no. 2 (2009): W1—W2. http://dx.doi.org/10.1080/15265160802715656.

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42

Zhu, Hongjian, Jin Piao, J. Jack Lee, Feifang Hu, and Lixin Zhang. "Response adaptive randomization procedures in seamless phase II/III clinical trials." Journal of Biopharmaceutical Statistics 30, no. 1 (2019): 3–17. http://dx.doi.org/10.1080/10543406.2019.1657439.

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43

Noordam, Raymond, Roelof AJ Smit, Iris Postmus, Stella Trompet, and Diana van Heemst. "Lessons from Mendelian randomization studies on liver biomarkers: response to Abbasi." International Journal of Epidemiology 46, no. 5 (2017): 1713–14. http://dx.doi.org/10.1093/ije/dyx064.

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44

Galbete, Arkaitz, José A. Moler, and Fernando Plo. "Asymptotic Behavior of a Randomization Test for a Response-Adaptive Design." Sequential Analysis 34, no. 1 (2015): 39–56. http://dx.doi.org/10.1080/07474946.2015.995979.

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45

Baldi Antognini, Alessandro, Alessandro Vagheggini, Maroussa Zagoraiou, and Marco Novelli. "A new design strategy for hypothesis testing under response adaptive randomization." Electronic Journal of Statistics 12, no. 2 (2018): 2454–81. http://dx.doi.org/10.1214/18-ejs1458.

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46

Ning, Jing, and Xuelin Huang. "Response-adaptive randomization for clinical trials with adjustment for covariate imbalance." Statistics in Medicine 29, no. 17 (2010): 1761–68. http://dx.doi.org/10.1002/sim.3978.

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47

Haines, Linda M., and Hassan Sadiq. "Start-up designs for response-adaptive randomization procedures with sequential estimation." Statistics in Medicine 34, no. 21 (2015): 2958–70. http://dx.doi.org/10.1002/sim.6528.

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48

Thall, Peter F. "Practical Bayesian Guidelines for Small Randomized Oncology Trials." Cancers 17, no. 12 (2025): 1902. https://doi.org/10.3390/cancers17121902.

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Randomization is a well-established statistical tool for obtaining fair treatment comparisons in clinical trials. Despite this, most investigators conducting small early-phase oncology trials of different experimental treatments or doses of a single agent do not randomize patients. This may be due to convention, physicians’ desire to choose personalized treatments for their patients, or the belief that randomization is of little value in small trials. We argue that, when it is feasible and ethical, randomization is very desirable in early-phase trials because it gives fair treatment comparison

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49

Freedman, Arnold S., Paul A. Hamlin, Sattva Neelapu, et al. "Phase III Trial of Active Immunotherapy (FavId®, Id/KLH) Following Rituximab Induction Therapy: Clinical Responses in Patients (pts) with Follicular Non-Hodgkin’s Lymphoma (fNHL)." Blood 108, no. 11 (2006): 2756. http://dx.doi.org/10.1182/blood.v108.11.2756.2756.

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Abstract Background: FavId is a personalized immunotherapy comprised of a recombinantly produced idiotype protein (Id) present on a pt’s lymphoma chemically conjugated with keyhole limpet hemocyanin (KLH). A single arm PhII study (Koc et al. Blood2005, 106:11 abstr #772) showed that FavId may improve the clinical response and TTP in both treatment naïve (TN) and relapsed/refractory (R/R) fNHL pts previously treated with rituximab. The results of that study formed the basis for this randomized, double-blind, placebo-controlled PhIII trial of FavId following rituximab (wkly x 4) in TN and R/R p

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50

Antoniucci, Diana M., Eric Vittinghoff, Terri Blackwell, Dennis M. Black, and Deborah E. Sellmeyer. "Vitamin D Insufficiency Does Not Affect Bone Mineral Density Response to Raloxifene." Journal of Clinical Endocrinology & Metabolism 90, no. 8 (2005): 4566–72. http://dx.doi.org/10.1210/jc.2005-0290.

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Abstract Context: Vitamin D insufficiency and osteoporosis are common and often coexist in postmenopausal women. Objective: The objective of this study was to test whether the presence of vitamin D insufficiency at the initiation of raloxifene therapy affected the subsequent response of bone mineral density (BMD). Design, Setting, and Participants: We studied 7522 postmenopausal participants of the Multiple Outcomes of Raloxifene Evaluation, a placebo-controlled trial of the effects of raloxifene on BMD and fracture. Intervention: After enrollment, all participants began daily supplements of 5

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