S. Nassir Ghaemi · Cambridge University Press eBooks · 2023
Randomization solves the problem of confounding bias; it addresses systematic error, which is the most important source of error, not chance. It equalizes all potential confounding factors, known and unknown, in all groups so that they equally influence the results, and thus can be ignored. Only then can the results of randomized treatment be interpreted at face value and causal inferences made. Sample size and other factors are relevant, though, and small randomized clinical trials (RCTs) can be misleading. Examples are given.
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Bryan F. J. Manly · Wiley Interdisciplinary Reviews Computational Statistics · 2010
Abstract There are three aspects of randomization in statistics that are considered here. The first aspect is randomization as part of a sampling design to estimate one or more parameters for a statistical population such as all the farms in a certain area of a country, based on information obtained about the parameters from only a part of the population. The second aspect is using randomization as part of an experimental design to ensure that the allocation of treatment levels to the experimental units is not biased in any way. For example, the test of a new drug for relieving the symptoms of a disease might involve this drug being randomly allocated to half of a group of patients, while the other half of the patients receive a standard drug that is used for the disease. Finally, the third aspect is using randomization to test some statistical hypothesis. For example, to see if there is a significant difference between two drugs for the treatment of a disease in terms of some suitable outcome measure, the observed mean difference between means for this outcome measure might be compared to the distribution of mean differences that is obtained by randomly reallocating the observed values of the measure to the drugs. The null hypothesis being tested would then be that each of the observed values of the measure was equally likely to have occurred with each of the two drugs. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Sampling
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Encyclopedia of Epidemiology · 2008
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Anindya Hapsari · Open Science Framework · 2024
No response
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Shao Jun · 2003
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Facundo Nahuel Urteaga · OSF Preprints (OSF Preprints) · 2023
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Shaifali Mahajan, Sajal Kakkar, Rajesh Vashistha et al. · Elsevier eBooks · 2023
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Esha Rani Kumar, Kristin Schneider, Patricia Mumby · OSF Preprints (OSF Preprints)
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Maria Meier · OSF Preprints (OSF Preprints) · 2022
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Dena Plemmons, David D Lo, kennett lai et al. · OSF Preprints (OSF Preprints) · 2018
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Mary Panos, Alexander Rothman, Traci Mann · OSF Preprints (OSF Preprints) · 2016
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Pellegrino, Peter Ricci · Open Science Framework · 2015
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2006
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2024
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David D Lo, kennett lai, Kyle Harp-Rushing et al. · OSF Preprints (OSF Preprints) · 2018
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J. A. Lewis · BMJ · 1975
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Mary Panos, Alexander Rothman, Traci Mann · OSF Preprints (OSF Preprints) · 2016
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Yanqiong Zhang, William F. Rosenberger, R. T. Smythe · Biometrics · 2007
In many clinical trials, it is desirable to establish a sequential monitoring plan, whereby the test statistic is computed at an interim point or points in the trial and a decision is made whether to stop early due to evidence of treatment efficacy. In this article, we will set up a sequential monitoring plan for randomization-based inference under the permuted block design, stratified block design, and stratified urn design. We will also propose a definition of information fraction in these settings and discuss its calculation under these different designs.
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Vance W. Berger, Klejda Bejleri, Rebecca Agnor · Statistics in Medicine · 2015
Randomization is one of the cornerstones of the randomized clinical trial, and there is no shortage of methods one can use to randomize patients to treatment groups. When deciding which one to use, researchers must bear in mind that not all randomization procedures are equally adept at achieving the objective of randomization, namely, balanced treatment groups. One threat is chronological bias, and permuted blocks randomization does such a good job at controlling chronological bias that it has become the standard randomization procedure in clinical trials. But permuted blocks randomization is especially vulnerable to selection bias, so as a result, the maximum tolerated imbalance (MTI) procedures were proposed as better alternatives. In comparing the procedures, we have somewhat of a false controversy, in that actual practice goes uniformly one way (permuted blocks), whereas scientific arguments go uniformly the other way (MTI procedures). There is no argument in the literature to suggest that the permuted block design is better than or even as good as the MTI procedures, but this dearth is matched by an equivalent one regarding actual trials using the MTI procedures. So the 'controversy', if we are to call it that, pits misguided precedent against sound advice that tends to be ignored in practice. We shall review the issues to determine scientifically which of the procedures is better and, therefore, should be used.
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
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