Randomization in clinical trials in orthodontics: its significance in research design and methods to achieve it
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Randomization is a key step in reducing selection bias during the treatment allocation phase in randomized clinical trials. The process of randomization follows specific steps, which include generation of the randomization list, allocation concealment, and implementation of randomization. The phenomenon in the dental and orthodontic literature of characterizing treatment allocation as random is frequent; however, often the randomization procedures followed are not appropriate. Randomization methods assign, at random, treatment to the trial arms without foreknowledge of allocation by either the participants or the investigators thus reducing selection bias. Randomization entails generation of random allocation, allocation concealment, and the actual methodology of implementing treatment allocation randomly and unpredictably. Most popular randomization methods include some form of restricted and/or stratified randomization. This article introduces the reasons, which make randomization an integral part of solid clinical trial methodology, and presents the main randomization schemes applicable to clinical trials in orthodontics.Keywords:
Restricted randomization
Random assignment
Randomized experiments are increasingly used in development economics, with researchers now facing the question of not just whether to randomize, but how to do so. Pure random assignment guarantees that the treatment and control groups will have identical characteristics on average, but in any particular random allocation, the two groups will differ along some dimensions. Methods used to pursue greater balance include stratification, pair-wise matching, and re-randomization. This paper presents new evidence on the randomization methods used in existing randomized experiments, and carries out simulations in order to provide guidance for researchers. Three main results emerge. First, many researchers are not controlling for the method of randomization in their analysis. The authors show this leads to tests with incorrect size, and can result in lower power than if a pure random draw was used. Second, they find that in samples of 300 or more, the different randomization methods perform similarly in terms of achieving balance on many future outcomes of interest. However, for very persistent outcome variables and in smaller sample sizes, pair-wise matching and stratification perform best. Third, the analysis suggests that on balance the re-randomization methods common in practice are less desirable than other methods, such as matching.
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AbstractAnthracyclines are quite effective at curing certain cancers of childhood, but they may damage the heart. The ACE-Inhibitor After Anthracycline (AAA) study compared enalapril to placebo in a randomized trial in an effort to determine whether treatment with enalapril would preserve or improve cardiac function among children previously treated with anthracylines. As is true in many clinical trials, patient compliance with the study protocol was imperfect; some children took less than the prescribed dose of enalapril or placebo. Most analytical procedures that acknowledge imperfect compliance do so at significant cost, abandoning the tight logic of random assignment. With noncompliance, assignment to enalapril or placebo is randomized, but the dose of enalapril actually received is not, and self-selection effects parallel to those in observational studies can exist and have been documented in some instances. Some researchers advocate adherence to the strict logic of randomization by reporting only, or else strongly emphasizing, the so-called "intent-to-treat" analysis, which makes no use of information about compliance. Other researchers report analyses that are not justified by random assignment and can be subject to substantial biases, such as "per protocol" analyses or "treatment received" analyses. Here we apply a recent proposal for randomization inference with an instrumental variable that uses randomization as the "reasoned basis for inference" in Fisher's phrase. We make no assumption that compliance is random; indeed, compliance may be severely biased. Importantly, the proposed analysis will find a statistically significant effect of the treatment if and only if the intent-to-treat analysis finds a significant effect; yet, unlike intent-to-treat analysis, our analysis acknowledges that a patient assigned to a drug that he or she does not take will not receive the drug's pharmacological benefits.KEY WORDS : Clinical trialHodges–Lehmann estimateInstrumental variableNoncompliancePermutation testRandomization testRandomized experimentRandomized trial
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Summary Even though randomization tests are the most powerful of nonparametric tests and are the only valid tests to employ when there has been random assignment, but not random selection, of subjects in experiments (a common practice in psychology), such tests are rarely used by psychologists. The limited adoption of randomization tests is primarily a consequence of the great amount of computation they require. The present study shows, however, that the computation for randomization test counterparts of the t test and one-way analysis of variance can be relatively inexpensive when performed by a high-speed computer.
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Though therapeutic clinical trials are often categorized as using either "randomization" or "historical controls" as a basis for treatment evaluation, pure random assignment of treatments is rarely employed. Instead various restricted randomization designs are used. The restrictions include the balancing of treatment assignments over time and the stratification of the assignment with regard to covariates that may affect response. Restricted randomization designs for clinical trials differ from those of other experimental areas because patients arrive sequentially and a balanced design cannot be ensured. The major restricted randomization designs and arguments concerning the proper role of stratification are reviewed here. The effect of randomization restrictions on the validity of significance tests is discussed.
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This article describes the design of the evaluation of Healthy for Life (HFL), an adolescent health promotion project involving students in 21 middle schools in Wisconsin. The original design was a blocked random assignment of 21 schools to one of three conditions. However, most of the interested schools could not accommodate the random design. A two-step alternative procedure allowed schools to select one of two treatment conditions, with random assignment to the control condition from either treatment condition. This randomized control group design nested within two self-selected treatment options is a viable alternative to total randomization.
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Random allocation is commonly used in medical researches, and has become an essential part of designing clinical trials. It produces comparable groups with regard to known or unknown prognostic factors, and prevents the selection bias which occurs due to the arbitrary assignment of subjects to groups. It also provides the background for statistical testing. Depending on the change in allocation probability, random allocation is divided into two categories: fixed allocation randomization and dynamic allocation randomization. In this paper, the author briefly introduces both the theory and practice of randomization. The definition, necessity, principal, significance, and classification of randomization are also explained. Advantages and disadvantages of each randomization technique are further discussed. Dynamic allocation randomization (Adaptive randomization), which is as yet unfamiliar with the anesthesiologist, is also introduced. Lastly, the methods and procedures for random sequence generation using Microsoft Excel is provided. Keywords: Microsoft excel; Random allocation; Research design; Software; Statistics
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