On Optimal Designs for Clinical Trials: An Updated Review

Sverdlov, Oleksandr; Ryeznik, Yevgen; Wong, Weng Kee

doi:10.1007/s42519-019-0073-4

Cited by 14 publications

(12 citation statements)

References 124 publications

(157 reference statements)

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“…Our proposed design essentially works in the opposite direction, by acknowledging that research practicalities will often lead evaluators to select clusters for participation conditional on baseline covariates, while still pursuing generalizable inferences -an instance of experimental design with multiple objectives (Sverdlov & Rosenberger, 2013;Sverdlov et al, 2020;Woodcock & LaVange, 2017). For example, our approach can support trials designed to test hypotheses in subgroups of clusters, by oversampling clusters with certain characteristics, and uses the known-by-design sampling probabilities to produce inferences that apply to the target population.…”

Section: Discussionmentioning

confidence: 99%

Analyzing cluster randomized trials designed to support generalizable inferences

Robertson¹,

Steingrimsson²,

Dahabreh³

2022

Preprint

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Background: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics to improve trial economy or to support inference about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. Methods: We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. Results: We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies we show that different estimators have low bias but markedly different precision.Conclusions: Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

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Section: Discussionmentioning

confidence: 99%

Analyzing cluster randomized trials designed to support generalizable inferences

Robertson¹,

Steingrimsson²,

Dahabreh³

2022

Preprint

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“…When the primary outcome follows a more complex statistical model, optimal allocation may be unequal across the treatment groups; however, 1:1 allocation is still nearly optimal for binary outcomes [ 62 , 63 ], survival outcomes [ 64 ], and possibly more complex data types [ 65 , 66 ]. Therefore, a randomization design that balances treatment numbers frequently promotes efficiency of the treatment comparison.…”

Section: Methodsmentioning

confidence: 99%

A roadmap to using randomization in clinical trials

Berger

Bour

Carter

et al. 2021

BMC Med Res Methodol

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Background Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. Methods We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. Results Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. Conclusions The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

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“…Optimal designs (ODs) present an example of a methodology where statistical and PMx approaches can provide further interdisciplinary advances 55 . For a PK/PD experiment with a given NLMEM, one can construct a likelihood function and obtain the Fisher Information Matrix (FIM), which depends on the model parameters and the design points.…”

Section: Integration Of Fieldsmentioning

confidence: 99%

Pharmacometrics meets statistics—A synergy for modern drug development

Ryeznik

Sverdlov

Svensson

et al. 2021

CPT Pharmacom & Syst Pharma

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Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology‐based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

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On Optimal Designs for Clinical Trials: An Updated Review

Cited by 14 publications

References 124 publications

Analyzing cluster randomized trials designed to support generalizable inferences

Analyzing cluster randomized trials designed to support generalizable inferences

A roadmap to using randomization in clinical trials

Pharmacometrics meets statistics—A synergy for modern drug development

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