| 1. |
- Ryeznik, Yevgen, et al.
(författare)
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A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios
- 2018
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 37:21, s. 3056-3077
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Tidskriftsartikel (refereegranskat)abstract
- Randomization designs for multiarm clinical trials are increasingly used in practice, especially in phase II dose-ranging studies. Many new methods have been proposed in the literature; however, there is lack of systematic, head-to-head comparison of the competing designs. In this paper, we systematically investigate statistical properties of various restricted randomization procedures for multiarm trials with fixed and possibly unequal allocation ratios. The design operating characteristics include measures of allocation balance, randomness of treatment assignments, variations in the allocation ratio, and statistical characteristics such as type I error rate and power. The results from the current paper should help clinical investigators select an appropriate randomization procedure for their clinical trial. We also provide a web-based R shiny application that can be used to reproduce all results in this paper and run simulations under additional user-defined experimental scenarios.
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| 2. |
- Ryeznik, Yevgen, et al.
(författare)
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Adaptive Optimal Designs for Dose-Finding Studies with Time-to-Event Outcomes
- 2018
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Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- We consider optimal design problems for dose-finding studies with censored Weibull time-to-event outcomes. Locally D-optimal designs are investigated for a quadratic dose-response model for log-transformed data subject to right censoring. Two-stage adaptive D-optimal designs using maximum likelihood estimation (MLE) model updating are explored through simulation for a range of different dose-response scenarios and different amounts of censoring in the model. The adaptive optimal designs are found to be nearly as efficient as the locally D-optimal designs. A popular equal allocation design can be highly inefficient when the amount of censored data is high and when the Weibull model hazard is increasing. The issues of sample size planning/early stopping for an adaptive trial are investigated as well. The adaptive D-optimal design with early stopping can potentially reduce study size while achieving similar estimation precision as the fixed allocation design.
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| 3. |
- Ryeznik, Yevgen, et al.
(författare)
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Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group
- 2018
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Ingår i: AAPS Journal. - : SPRINGER. - 1550-7416. ; 20:5
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Tidskriftsartikel (refereegranskat)abstract
- In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.
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| 4. |
- Ryeznik, Yevgen, 1979-
(författare)
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Optimal adaptive designs and adaptive randomization techniques for clinical trials
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this Ph.D. thesis, we investigate how to optimize the design of clinical trials by constructing optimal adaptive designs, and how to implement the design by adaptive randomization. The results of the thesis are summarized by four research papers preceded by three chapters: an introduction, a short summary of the results obtained, and possible topics for future work.In Paper I, we investigate the structure of a D-optimal design for dose-finding studies with censored time-to-event outcomes. We show that the D-optimal design can be much more efficient than uniform allocation design for the parameter estimation. The D-optimal design obtained depends on true parameters of the dose-response model, so it is a locally D-optimal design. We construct two-stage and multi-stage adaptive designs as approximations of the D-optimal design when prior information about model parameters is not available. Adaptive designs provide very good approximations to the locally D-optimal design, and can potentially reduce total sample size in a study with a pre-specified stopping criterion.In Paper II, we investigate statistical properties of several restricted randomization procedures which target unequal allocation proportions in a multi-arm trial. We compare procedures in terms of their operational characteristics such as balance, randomness, type I error/power, and allocation ratio preserving (ARP) property. We conclude that there is no single “best” randomization procedure for all the target allocation proportions, but the choice of randomization can be done through computer-intensive simulations for a particular target allocation.In Paper III, we combine the results from the papers I and II to implement optimal designs in practice when the sample size is small. The simulation study done in the paper shows that the choice of randomization procedure has an impact on the quality of dose-response estimation. An adaptive design with a small cohort size should be implemented with a procedure that ensures a “well-balanced” allocation according to the D-optimal design at each stage.In Paper IV, we obtain an optimal design for a comparative study with unequal treatment costs and investigate its properties. We demonstrate that unequal allocation may decrease the total study cost while having the same power as traditional equal allocation. However, a larger sample size may be required. We suggest a strategy on how to choose a suitable randomization procedure which provides a good trade-off between balance and randomness to implement optimal allocation. If there is a strong linear trend in observations, then the ARP property is important to maintain the type I error and power at a certain level. Otherwise, a randomization-based inference can be a good alternative for non-ARP procedures.
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| 5. |
- Ryeznik, Yevgen, et al.
(författare)
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Pharmacometrics meets statistics-A synergy for modern drug development
- 2021
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Ingår i: CPT. - : John Wiley & Sons. - 2163-8306. ; 10:10, s. 1134-1149
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 6. |
- Ryeznik, Yevgen, et al.
(författare)
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RARtool : A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes
- 2015
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Ingår i: Journal of Statistical Software. - 1548-7660. ; 66:1
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Tidskriftsartikel (refereegranskat)abstract
- Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool, a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.
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| 7. |
- Sverdlov, Oleksandr, et al.
(författare)
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Efficient and ethical response-adaptive randomization designs for multi-arm clinical trials with Weibull time-to-event outcomes.
- 2014
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Ingår i: Journal of Biopharmaceutical Statistics. - : Informa UK Limited. - 1054-3406 .- 1520-5711. ; 24:4, s. 732-54
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Tidskriftsartikel (refereegranskat)abstract
- We consider a design problem for a clinical trial with multiple treatment arms and time-to-event primary outcomes that are modeled using the Weibull family of distributions. The D-optimal design for the most precise estimation of model parameters is derived, along with compound optimal allocation designs that provide targeted efficiencies for various estimation problems and ethical considerations. The proposed optimal allocation designs are studied theoretically and are implemented using response-adaptive randomization for a clinical trial with censored Weibull outcomes. We compare the merits of our multiple-objective response-adaptive designs with traditional randomization designs and show that our designs are more flexible, realistic, generally more ethical, and frequently provide higher efficiencies for estimating different sets of parameters.
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| 8. |
- Sverdlov, Oleksandr, et al.
(författare)
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Exact Bayesian Inference Comparing Binomial Proportions, With Application to Proof-of-Concept Clinical Trials
- 2015
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Ingår i: THERAPEUTIC INNOVATION & REGULATORY SCIENCE. - : Springer Science and Business Media LLC. - 2168-4790 .- 2168-4804. ; 49:1, s. 163-174
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Tidskriftsartikel (refereegranskat)abstract
- The authors revisit the problem of exact Bayesian inference comparing two independent binomial proportions. Numerical integration in R is used to compute exact posterior distribution functions, probability densities, and quantiles of the risk difference, relative risk, and odds ratio. An application of the methodology is given in the context of randomized comparative proof-of-concept clinical trials that are driven by evaluation of quantitative criteria combining statistical significance and clinical relevance. A two-stage adaptive design based on predictive probability of success is proposed and its operating characteristics are studied via Monte Carlo simulation. The authors conclude that exact Bayesian methods provide an elegant and efficient way to facilitate design and analysis of proof-of-concept studies.
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| 9. |
- Sverdlov, Oleksandr, et al.
(författare)
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Implementing Unequal Randomization in Clinical Trials with Heterogeneous Treatment Costs
- 2019
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 38:16, s. 2905-2927
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Tidskriftsartikel (refereegranskat)abstract
- Equal randomization has been a popular choice in clinical trial practice. However, in trials with heterogeneous variances and/or variable treatment costs, as well as in the settings where maximization of every trial participant’s benefit is an important design consideration, optimal allocation proportions may be unequal across study treatment arms. In this paper, we investigate optimal allocation designs minimizing study cost under statistical efficiency constraints for parallel group clinical trials comparing several investigational treatments against the control. We show theoretically that equal allocation designs may be suboptimal, and unequal allocation designs can provide higher statistical power for the same budget, or result in a smaller cost for the same level of power. We also show how the optimal allocation can be implemented in practice by means of restricted randomization procedures, and how to perform statistical inference following these procedures, using invoked population-based or randomization-based approaches. Our results provide further support to some previous findings in the literature that unequal randomization designs can be cost-efficient and can be successfully implemented in practice. We conclude that the choice of the target allocation, the randomization procedure and the statistical methodology for data analysis are essential components to ensure valid, powerful, and robust clinical trial results.
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| 10. |
- Sverdlov, Oleksandr, et al.
(författare)
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On Optimal Designs for Clinical Trials : An Updated Review
- 2020
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Ingår i: Journal of Statistical Theory and Practice. - : Springer Nature. - 1559-8608 .- 1559-8616. ; 14:1
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Forskningsöversikt (refereegranskat)abstract
- Optimization of clinical trial designs can help investigators achieve higher quality results for the given resource constraints. The present paper gives an overview of optimal designs for various important problems that arise in different stages of clinical drug development, including phase I dose-toxicity studies; phase I/II studies that consider early efficacy and toxicity outcomes simultaneously; phase II dose-response studies driven by multiple comparisons (MCP), modeling techniques (Mod), or their combination (MCP-Mod); phase III randomized controlled multi-arm multi-objective clinical trials to test difference among several treatment groups; and population pharmacokinetics-pharmacodynamics experiments. We find that modern literature is very rich with optimal design methodologies that can be utilized by clinical researchers to improve efficiency of drug development.
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| 11. |
- Sverdlov, Oleksandr, et al.
(författare)
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Opportunity for efficiency in clinical development : An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field
- 2021
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Ingår i: Contemporary Clinical Trials. - : Elsevier. - 1551-7144 .- 1559-2030. ; 105
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Forskningsöversikt (refereegranskat)abstract
- Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.
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| 12. |
- Sverdlov, Oleksandr, et al.
(författare)
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Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations
- 2024
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Ingår i: BMC Medical Research Methodology. - : BioMed Central (BMC). - 1471-2288. ; 24
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Tidskriftsartikel (refereegranskat)abstract
- Background: The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process.Methods: We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize n=500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence.Results: Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff.Conclusions: The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.
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