Topic
Restricted randomization
About: Restricted randomization is a research topic. Over the lifetime, 597 publications have been published within this topic receiving 34293 citations.
Papers published on a yearly basis
1 / 2
Papers
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
Abstract: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments. The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and necessary procedure in many cases. Recent psychological and educational literature has included extensive criticism of the use of nonrandomized studies to estimate causal effects of treatments (e.g., Campbell & Erlebacher, 1970). The implication in much of this literature is that only properly randomized experiments can lead to useful estimates of causal effects. If taken as applying to all fields of study, this position is untenable. Since the extensive use of randomized experiments is limited to the last half century,8 and in fact is not used in much scientific investigation today,4 one is led to the conclusion that most scientific "truths" have been established without using randomized experiments. In addition, most of us successfully determine the causal effects of many of our everyday actions, even interpersonal behaviors, without the benefit of randomization. Even if the position that causal effects of treatments can only be well established from randomized experiments is taken as applying only to the social sciences in which
10,201 citations
TL;DR: A new general procedure for treatment assignment is described which concentrates on minimizing imbalance in the distributions of treatment numbers within the levels of each individual prognostic factor.
Abstract: In controlled clinical trials there are usually several prognostic factors known or thought to influence the patient's ability to respond to treatment. Therefore, the method of sequential treatment assignment needs to be designed so that treatment balance is simultaneously achieved across all such patients factor. Traditional methods of restricted randomization such as "permuted blocks within strata" prove inadequate once the number of strata, or combinations of factor levels, approaches the sample size. A new general procedure for treatment assignment is described which concentrates on minimizing imbalance in the distributions of treatment numbers within the levels of each individual prognostic factor. The improved treatment balance obtained by this approach is explored using simulation for a simple model of a clinical trial. Further discussion centers on the selection, predictability and practicability of such a procedure.
2,311 citations
Book•
5 Oct 2000
TL;DR: In this article, the impact of cluster randomization on the design and analysis of a trial is discussed, and the most commonly used experimental designs are the completely randomized design, the matched-pair design and the stratified design.
Abstract: Acknowledgements. Preface. 1. Introduction. 1.1 Why randomize clusters? 1.2 What is the impact of cluster randomization on the design and analysis of a trial? 1.3 Quantifying the effect of clustering. 1.4 Randomized versus non-randomized comparisons. 1.5 The unit of inference. 1.6 Terminology: what's in a name? 2. The historical development of cluster randomized trials. 2.1 Randomized trials before 1950. 2.2 Cluster randomized trials between 1950 and 1978. 2.3 Cluster randomized trails since 1978. 3. Issues arising in the planning of cluster randomization trials. 3.1 Selecting interventions. 3.2 Setting eligibility criteria. 3.3 Measuring subject response. 3.4 The most commonly used experimental designs. 3.5 Factorial and crossover designs. 3.6 Selecting an experimental design. 3.7 The importance of cluster-level replication. 3.8 Strategies for conducting successful trials. 4. The role of informed consent and other ethical issues. 4.1 The risk of harm. 4.2 Informed consent. 4.3 Subject blindness and informed consent. 4.4 Randomized consent designs. 4.5 Ethical issues and trial monitoring. 5. Sample size estimation for cluster randomization designs. 5.1 General issues of sample size estimation. 5.2 The completely randomized design. 5.3 The matched-pair design. 5.4 The stratified design. 5.5 Issues involving losses to follow-up. 5.6 Strategies for achieving desired power. 6. Analysis of binary outcomes. 6.1 Selecting the unit of analysis. 6.2 The completely randomized design. 6.3 The matched-pair design. 6.4 The stratified design. 7. Analysis of quantitative outcomes. 7.1 The completely randomized design. 7.2 The matched-pair design. 7.3 The stratified design. 8. Analysis of count, time to event and categorical outcomes. 8.1 Count and time to event data. 8.2 Categorical data. 9. Reporting of cluster randomization trials. 9.1 Reporting of study design. 9.2 Reporting of study results. References. Index.
1,614 citations
TL;DR: A reasonable standard design and conduct of trials will facilitate the interpretation of those with conflicting results and help in making valid combinations of undersized trials.
1,458 citations
TL;DR: In this paper, a unified strategy for selecting spatially balanced probability samples of natural resources is presented, which is based on creating a function that maps two-dimensional space into onedimensional space, thereby defining an ordered spatial address.
Abstract: The spatial distribution of a natural resource is an important consideration in designing an efficient survey or monitoring program for the resource. Generally, sample sites that are spatially balanced, that is, more or less evenly dispersed over the extent of the resource, are more efficient than simple random sampling. We review a unified strategy for selecting spatially balanced probability samples of natural resources. The technique is based on creating a function that maps two-dimensional space into one-dimensional space, thereby defining an ordered spatial address. We use a restricted randomization to randomly order the addresses, so that systematic sampling along the randomly ordered linear structure results in a spatially well-balanced random sample. Variable inclusion probability, proportional to an arbitrary positive ancillary variable, is easily accommodated. The basic technique selects points in a two-dimensional continuum, but is also applicable to sampling finite populations or one-dimension...
1,257 citations
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Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 6 |
| 2024 | 17 |
| 2023 | 27 |
| 2022 | 26 |
| 2021 | 14 |
| 2020 | 9 |