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Journal articles on the topic 'Randomized design'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Green, S. B. "Design of Randomized Trials." Epidemiologic Reviews 24, no. 1 (2002): 4–11. http://dx.doi.org/10.1093/epirev/24.1.4.

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2

Blumenstein, Brent A., and John J. Crowley. "Randomized design and analysis of randomized clinical trials." Controlled Clinical Trials 7, no. 3 (1986): 235. http://dx.doi.org/10.1016/0197-2456(86)90074-7.

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3

Karaagaoglu, Ergun. "An Experimental Design Technique: Randomized Block Design." Turkish Journal of Biochemistry 38, no. 1 (2013): 1–4. http://dx.doi.org/10.5505/tjb.2013.04796.

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4

Freidlin, Boris, and Richard Simon. "Evaluation of Randomized Discontinuation Design." Journal of Clinical Oncology 23, no. 22 (2005): 5094–98. http://dx.doi.org/10.1200/jco.2005.02.520.

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Purpose Single-arm phase II trials may not be appropriate for testing cytostatic agents. We evaluate two kinds of randomized designs for the early development of target-based cytostatic agents. Methods We compared power of the randomized discontinuation and upfront randomization designs under two models for the treatment effect of targeted cytostatic agents. Results The randomized discontinuation design is not as efficient as upfront randomization if treatment has a fixed effect on tumor growth rate or if treatment benefit is restricted to slower-growing tumors. On the other hand, the randomiz

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5

Ndidiamaka, Ezra Precious, and Nwovu Sunday. "Relative Efficiency of Split-Plot Design To Randomized Designs." International Journal of Mathematics Trends and Technology 67, no. 6 (2021): 131–35. http://dx.doi.org/10.14445/22315373/ijmtt-v67i6p515.

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6

Stanley, Kenneth. "Design of Randomized Controlled Trials." Circulation 115, no. 9 (2007): 1164–69. http://dx.doi.org/10.1161/circulationaha.105.594945.

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7

Jones, Mark, Val Gebski, Mark Onslow, and Ann Packman. "Design of randomized controlled trials." Journal of Fluency Disorders 26, no. 4 (2001): 247–67. http://dx.doi.org/10.1016/s0094-730x(01)00108-5.

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8

Pocock, Stuart J., Tim C. Clayton, and Gregg W. Stone. "Design of Major Randomized Trials." Journal of the American College of Cardiology 66, no. 24 (2015): 2757–66. http://dx.doi.org/10.1016/j.jacc.2015.10.036.

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9

Egbewale, Bolaji Emmanuel. "Cluster Randomized Controlled Trials: A Quick Update on Concepts and Types." Clinical Trials and Practice – Open Journal 1, no. 1 (2017): 15–17. http://dx.doi.org/10.17140/ctpoj-1-103.

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Cluster randomized controlled trials have certain complexities both at design and statistical analysis stages of the experiment. Although, this experimental study approach offers solutions in situations where it would be inappropriate to randomize individuals into treatment arms its design and statistical analysis are not without certain unique challenges. Compared to individual randomized controlled trials, very limited understanding of the design concepts in cluster randomized controlled trials exists, this perhaps explain reasons it is not as popular as the former. This short note presents

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10

Badian, Reza A., Brendan McCormack, and Vibeke Sundling. "Person-Centered Research: A novel approach to Randomized Controlled Trials." European Journal for Person Centered Healthcare 6, no. 2 (2018): 209. http://dx.doi.org/10.5750/ejpch.v6i2.1435.

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Introduction: Integrating person-centered values with randomized controlled trials methodology is a novel idea. Person-centeredness is gaining steadily more prominence and attention in healthcare and health-related policy and research. Randomized controlled trials are considered as the gold standard in evidence-based medicine for evaluating the effects of treatment or determining the causal effect. A wide array of study designs is available, but there is a lack of designs with both strong person-centered principles and a strong position with respect to the level of evidence. In this paper we i

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11

Nishu, Lohmor, Khan Mujahid, Kapoor Kiran, and Kumar Tripathi Ramesh. "Studies on the Relative Efficiency of Different Experimental Designs for Sunflower (Helianthus annuus L.)." Annual Research & Review in Biology 12, no. 4 (2017): 1–7. https://doi.org/10.9734/ARRB/2017/31728.

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The best experimental design to use in any given condition is the one which estimates the desired effects and contrasts with maximum precision or efficiency. In uniformity trial data, the treatments being considered as dummy, the relative efficiencies of various experimental designs were determined using the yield data taken from the uniformly raised sunflower crop during February 2014 to June 2014 at CCSHAU research farm, Hisar. Randomized Block Design (RBD) was found to be more effective in reducing error variation over Completely Randomized Design (CRD). Latin Square Design (LSD) was found

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12

Rosner, Gary L., Walter Stadler, and Mark J. Ratain. "Randomized Discontinuation Design: Application to Cytostatic Antineoplastic Agents." Journal of Clinical Oncology 20, no. 22 (2002): 4478–84. http://dx.doi.org/10.1200/jco.2002.11.126.

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PURPOSE: Propose a phase II study design to evaluate the activity of a putative cytostatic agent, acknowledging heterogeneity of tumor growth rates in the population of patients. METHODS: In the setting of renal cell carcinoma, some patients’ tumors will grow slowly naturally. An appropriate design has to distinguish antiproliferative activity attributable to the novel agent from indolent disease. We propose a randomized discontinuation design that initially treats all patients with the study agent (stage 1) and then randomizes in a double-blind fashion to continuing therapy or placebo only th

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13

Pashley, Nicole E., Guillaume W. Basse, and Luke W. Miratrix. "Conditional as-if analyses in randomized experiments." Journal of Causal Inference 9, no. 1 (2021): 264–84. http://dx.doi.org/10.1515/jci-2021-0012.

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Abstract The injunction to “analyze the way you randomize” is well known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the merits of randomization-based inference seldom follow this injunction to the letter. Bernoulli randomized experiments are often analyzed as completely randomized experiments, and completely randomized experiments are analyzed as if they had been stratified; more generally, it is not uncommon to analyze an experiment as if it had been randomized differently. This article examines the theoretical foundation b

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14

Rosenberger, William F. "RANDOMIZED URN MODELS AND SEQUENTIAL DESIGN." Sequential Analysis 21, no. 1-2 (2002): 1–28. http://dx.doi.org/10.1081/sqa-120004166.

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15

Bria, E., M. Di Maio, C. Nisticò, et al. "Factorial design for randomized clinical trials." Annals of Oncology 17, no. 10 (2006): 1607–8. http://dx.doi.org/10.1093/annonc/mdl106.

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16

Gabler, Siegfried, and Horst Stenger. "Design effect of randomized systematic sampling." Statistics 46, no. 1 (2012): 131–48. http://dx.doi.org/10.1080/02331888.2010.491252.

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17

Walker, Dylan, and Lev Muchnik. "Design of Randomized Experiments in Networks." Proceedings of the IEEE 102, no. 12 (2014): 1940–51. http://dx.doi.org/10.1109/jproc.2014.2363674.

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18

Purgato, M., C. Barbui, S. Stroup, and C. Adams. "Pragmatic design in randomized controlled trials." Psychological Medicine 45, no. 2 (2014): 225–30. http://dx.doi.org/10.1017/s0033291714001275.

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At more than 10 years after the paper by Hotopf and colleagues regarding pragmatic trials in psychiatry, the field has evolved and is evolving further. There have been many developments in our understanding of what pragmatism really means, and excellent examples of truly pragmatic trials in psychiatry are currently available. Funders have helped encourage more emphasis on the need for such studies, but ‘local’ and trans-national regulations could help more. Consumers of the evidence should have a greater voice in generating the research agenda and, as this happens, the questions generated are

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19

Kean, Yin M., Ronald G. Thomas, and Leon J. Thal. "Power calculation for randomized start design." Controlled Clinical Trials 19, no. 3 (1998): S38. http://dx.doi.org/10.1016/s0197-2456(98)80118-9.

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20

Yunjiang Lou, Guanfeng Liu, and Zexiang Li. "Randomized Optimal Design of Parallel Manipulators." IEEE Transactions on Automation Science and Engineering 5, no. 2 (2008): 223–33. http://dx.doi.org/10.1109/tase.2007.909446.

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21

Sonpavde, Guru, Thomas E. Hutson, Matthew D. Galsky, and William R. Berry. "Problems With the Randomized Discontinuation Design." Journal of Clinical Oncology 24, no. 28 (2006): 4669a—4670. http://dx.doi.org/10.1200/jco.2006.08.1026.

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22

Capili, Bernadette, and Joyce K. Anastasi. "The Cluster Randomized Trial Study Design." AJN, American Journal of Nursing 123, no. 9 (2023): 57–60. http://dx.doi.org/10.1097/01.naj.0000978168.48569.72.

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Editor's note: This is the 17th article in a series on clinical research by nurses. The series is designed to be used as a resource for nurses to understand the concepts and principles essential to research. Each column will present the concepts that underpin evidence-based practice—from research design to data interpretation. To see all the articles in the series, go to https://links.lww.com/AJN/A204.

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23

Raaijmakers, Maartje, Hendrik Koffijberg, Jocelyne Posthumus, Ben van Hout, Herman van Engeland, and Walter Matthys. "Assessing performance of a randomized versus a non-randomized study design." Contemporary Clinical Trials 29, no. 2 (2008): 293–303. http://dx.doi.org/10.1016/j.cct.2007.07.006.

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24

Korn, Edward L., Boris Freidlin, Jeffrey S. Abrams, and Susan Halabi. "Design Issues in Randomized Phase II/III Trials." Journal of Clinical Oncology 30, no. 6 (2012): 667–71. http://dx.doi.org/10.1200/jco.2011.38.5732.

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Phase II trials are used to show sufficient preliminary activity of a new treatment (in single-arm designs or randomized screening designs) or to select among treatments with demonstrated activity (in randomized selection designs). The treatments prioritized in a phase II trial are then tested definitively against a control treatment in a randomized phase III trial. Randomized phase II/III trials use an adaptive trial design that combines these two types of trials in one, with potential gains in time and reduced numbers of patients required to be treated. Two key considerations in designing a

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25

Kumari, Srishti, and Azarudheen S. "Synergizing Neutrosophy and Randomized Blocks Design: Development and Analytical Insights." International Journal of Neutrosophic Science 23, no. 1 (2024): 125–33. http://dx.doi.org/10.54216/ijns.230111.

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The design of the experiment is a strategy for effectively examining the relationship between input design parameters and process output and developing a greater understanding. A randomized block design is an experimental design that has two primary factors and is widely used in agriculture, environment, biological, animal, and food sciences, where experimental material is heterogeneous and precise. In a randomised block design, one or more observations may lose their true significance due to an accident, poor handling, pest infestations in agricultural trials, or other factors. It is prudent

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26

Hu, Yirui, and Donald R. Hoover. "Non-randomized and randomized stepped-wedge designs using an orthogonalized least squares framework." Statistical Methods in Medical Research 27, no. 4 (2016): 1202–18. http://dx.doi.org/10.1177/0962280216657852.

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Randomized stepped-wedge (R-SW) designs are increasingly used to evaluate interventions targeting continuous longitudinal outcomes measured at T-fixed time points. Typically, all units start out untreated, and randomly chosen units switch to intervention at sequential time points until all receive intervention. As randomization is not always feasible, non-randomized stepped-wedge (NR-SW) designs (units switching to intervention are not randomly chosen) have attracted researchers. We develop an orthogonlized generalized least squares framework for both R-SW and NR-SW designs. The variance of th

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27

Mandrekar, Sumithra J., Ming-Wen An, and Daniel J. Sargent. "A phase II trial design with direct assignment option for initial marker validation." Journal of Clinical Oncology 30, no. 30_suppl (2012): 34. http://dx.doi.org/10.1200/jco.2012.30.30_suppl.34.

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34 Background: Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. Testing targeted therapies with predictive biomarkers mandates efficient trial designs. Current biomarker-based trial designs, including the enrichment, all-comers, and adaptive designs, randomize patients to receive treatment or not throughout the entire duration of the trial. Recognizing the need for randomization yet acknowledging the possibility of promising but nonconclusive results after a preplanned interim analysis (IA), we propose a two-stage phase II design

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28

Murray, David M., Monica Taljaard, Elizabeth L. Turner, and Stephanie M. George. "Essential Ingredients and Innovations in the Design and Analysis of Group-Randomized Trials." Annual Review of Public Health 41, no. 1 (2020): 1–19. http://dx.doi.org/10.1146/annurev-publhealth-040119-094027.

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This article reviews the essential ingredients and innovations in the design and analysis of group-randomized trials. The methods literature for these trials has grown steadily since they were introduced to the biomedical research community in the late 1970s, and we summarize those developments. We review, in addition to the group-randomized trial, methods for two closely related designs, the individually randomized group treatment trial and the stepped-wedge group-randomized trial. After describing the essential ingredients for these designs, we review the most important developments in the e

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29

Freidlin, Boris, Lisa M. McShane, Mei-Yin C. Polley, and Edward L. Korn. "Randomized Phase II Trial Designs With Biomarkers." Journal of Clinical Oncology 30, no. 26 (2012): 3304–9. http://dx.doi.org/10.1200/jco.2012.43.3946.

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Efficient development of targeted therapies that may only benefit a fraction of patients requires clinical trial designs that use biomarkers to identify sensitive subpopulations. Various randomized phase III trial designs have been proposed for definitive evaluation of new targeted treatments and their associated biomarkers (eg, enrichment designs and biomarker-stratified designs). Before proceeding to phase III, randomized phase II trials are often used to decide whether the new therapy warrants phase III testing. In the presence of a putative biomarker, the phase II trial should also provide

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30

Egbewale, BolajiEmmanuel. "Common design concepts in randomized controlled trials." Nigerian Medical Journal 61, no. 2 (2020): 51. http://dx.doi.org/10.4103/nmj.nmj_112_19.

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31

van Oldenrijk, Jakob, Inger N. Sierevelt, Matthias U. Schafroth, and Rudolf W. Poolman. "Design Considerations in Implant-Related Randomized Trials." Journal of Long-Term Effects of Medical Implants 17, no. 2 (2007): 153–63. http://dx.doi.org/10.1615/jlongtermeffmedimplants.v17.i2.80.

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32

Richter, Felicitas, and Marc Dewey. "Zelen Design in Randomized Controlled Clinical Trials." Radiology 272, no. 3 (2014): 919. http://dx.doi.org/10.1148/radiol.14140834.

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33

Sonnad, Seema. "Design and Analysis of Group-Randomized Trials." Medicine & Science in Sports & Exercise 31, no. 2 (1999): 355. http://dx.doi.org/10.1097/00005768-199902000-00025.

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34

Kao, Ming-Yang, Manan Sanghi, and Robert Schweller. "Randomized fast design of short DNA words." ACM Transactions on Algorithms 5, no. 4 (2009): 1–24. http://dx.doi.org/10.1145/1597036.1597047.

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35

Law, M. "Randomized clinical trials: design, practice and reporting." Gut 59, no. 8 (2010): 1154. http://dx.doi.org/10.1136/gut.2010.214676.

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36

Shieh, Gwowen, and Show-Li Jan. "The effectiveness of randomized complete block design." Statistica Neerlandica 58, no. 1 (2004): 111–24. http://dx.doi.org/10.1046/j.0039-0402.2003.00109.x.

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37

McClure, Graham R., William F. McIntyre, Richard P. Whitlock, and Emilie P. Belley-Cote. "Understanding randomized trial design in vascular surgery." Journal of Vascular Surgery 72, no. 2 (2020): 771–72. http://dx.doi.org/10.1016/j.jvs.2020.04.013.

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38

Lim, Michael L. "Randomized Clinical Trials: Design, Practice and Reporting." Annals of Pharmacotherapy 44, no. 12 (2010): 2045. http://dx.doi.org/10.1345/aph.1p400.

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39

Freidlin, B., L. M. McShane, and E. L. Korn. "Randomized Clinical Trials With Biomarkers: Design Issues." JNCI Journal of the National Cancer Institute 102, no. 3 (2010): 152–60. http://dx.doi.org/10.1093/jnci/djp477.

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40

NAVANEETHAN, SANKAR D., SUETONIA C. PALMER, ALICIA SMITH, DAVID W. JOHNSON, and GIOVANNI FM STRIPPOLI. "How to design a randomized controlled trial." Nephrology 15, no. 8 (2010): 732–39. http://dx.doi.org/10.1111/j.1440-1797.2010.01428.x.

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41

Christensen, Erik. "Effective randomized clinical trial design: sequential analysis." Journal of Hepatology 38, no. 4 (2003): 550–51. http://dx.doi.org/10.1016/s0168-8278(03)00048-5.

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42

Finn, P. W., L. E. Kavraki, J. C. Latombe, et al. "RAPID: Randomized pharmacophore identification for drug design." Computational Geometry 10, no. 4 (1998): 263–72. http://dx.doi.org/10.1016/s0925-7721(98)00008-x.

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43

Gąsieniec, Leszek, Cindy Y. Li, Paul Sant, and Prudence W. H. Wong. "Randomized probe selection algorithm for microarray design." Journal of Theoretical Biology 248, no. 3 (2007): 512–21. http://dx.doi.org/10.1016/j.jtbi.2007.05.036.

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44

Rietbergen, Charlotte, and Mirjam Moerbeek. "The Design of Cluster Randomized Crossover Trials." Journal of Educational and Behavioral Statistics 36, no. 4 (2011): 472–90. http://dx.doi.org/10.3102/1076998610379136.

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45

Ostrosky-Zeichner, L. "Randomized Clinical Trials Design, Practice, and Reporting." Clinical Infectious Diseases 52, no. 2 (2010): 281–82. http://dx.doi.org/10.1093/cid/ciq122.

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46

Sun, Jian, Dachuan Xu, Deren Han, Wenjing Hou, and Xiaoyan Zhang. "Randomized mechanism design for decentralized network scheduling." Optimization Methods and Software 35, no. 4 (2020): 722–40. http://dx.doi.org/10.1080/10556788.2020.1713129.

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47

Hayes, John L. "Design flaws in some randomized controlled trials." American Journal of Orthodontics and Dentofacial Orthopedics 134, no. 4 (2008): 466–67. http://dx.doi.org/10.1016/j.ajodo.2008.08.011.

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48

Trenouth, M. J. "Design flaws in some randomized controlled trials." American Journal of Orthodontics and Dentofacial Orthopedics 135, no. 2 (2009): 141. http://dx.doi.org/10.1016/j.ajodo.2008.12.010.

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49

Wang, Xingbo. "Densification of Witnesses for Randomized Algorithm Design." Journal of Advances in Mathematics and Computer Science 38, no. 10 (2023): 44–69. http://dx.doi.org/10.9734/jamcs/2023/v38i101823.

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This paper investigates the densification of witnesses for randomized algorithm design and its application in factoring integers. By defining a set operation named with Cartesian subtraction on two countable sets and proving several properties of the operation, it is shown that the Cartesian subtraction can densify certain elements in a countable set so as to promote the abundance of witnesses for the randomized algorithm design. It is also proven that the Cartesian subtraction of two sets containing consecutive integers can form a triangular lattice zone that has a higher density of witnesses

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50

Hassan, Alsuhabi, and Magel Rhonda. "Modified Nonparametric Tests for the Umbrella Alternative with Known Peak in a Mixed Design." Journal of Progressive Research in Mathematics 16, no. 1 (2020): 2845–60. https://doi.org/10.5281/zenodo.3974011.

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The Mack-Wolfe and Kim-Kim statistics are two of the most commonly used non-parametric tests for the umbrella alternative problem when the underlying designs follow a CRD or an RCBD, respectively. In this paper, modifications of the Mack-Wolfe and Kim-Kim test are proposed to developtest statistics for the umbrella alternative with known peak when the data are mixture of a randomized complete block and a completely randomized design. The two proposed test statistics are compared to each other and some other existing tests. Results are given.

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