scholarly journals Balancing Treatment Allocation over Continuous Covariates: A New Imbalance Measure for Minimization

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yanqing Hu ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Efficient Algorithm ◽  
Allocation Rule ◽  
Simulation Studies ◽  
Treatment Allocation ◽  
Good Balance ◽  
Loss Of Information ◽  
Continuous Covariates ◽  
Continuous Covariate ◽  
Balance Treatment

In many clinical trials, it is important to balance treatment allocation over covariates. Although a great many papers have been published on balancing over discrete covariates, the procedures for continuous covariates have been less well studied. Traditionally, a continuous covariate usually needs to be transformed to a discrete one by splitting its range into several categories. Such practice may lead to loss of information and is susceptible to misspecification of covariate distribution. The more recent papers seek to define an imbalance measure that preserves the nature of continuous covariates and set the allocation rule in order to minimize that measure. We propose a new design, which defines the imbalance measure by the maximum assignment difference when all possible divisions of the covariate range are considered. This measure depends only on ranks of the covariate values and is therefore free of covariate distribution. In addition, we developed an efficient algorithm to implement the new procedure. By simulation studies we show that the new procedure is able to keep good balance properties in comparison with other popular designs.

Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

2021 ◽  
pp. 096228022110558
Author(s):  
Steven D Lauzon ◽  
Wenle Zhao ◽  
Paul J Nietert ◽  
Jody D Ciolino ◽  
Michael D Hill ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Logistic Regression ◽  
Selection Bias ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Simulation Studies ◽  
Binary Outcome ◽  
Treatment Allocation ◽  
Analysis Phase ◽  
The Impact

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Patient Registration and Treatment Allocation in Multicenter Clinical Trials Using a FAX-OCR System

1994 ◽  
Vol 33 (05) ◽  
pp. 530-534 ◽  
Author(s):  
T. Kamakura ◽  
M. Sakamoto ◽  
T. Odaka ◽  
Y. Nose ◽  
K. Akazawa
Keyword(s):  
Clinical Trials ◽  
Patient Identification ◽  
Minimization Method ◽  
Treatment Allocation ◽  
Staff Members ◽  
Patient Registration ◽  
Allocation Procedure ◽  
Multicenter Clinical Trials ◽  
Balance Treatment

Abstract:This article describes the design and results of implementation of an automated patient registration and freatment allocation system (RETAS) used in multicenter clinical trials. RETAS was developed using a FAX-OCR system by which handwritten Japanese and English characters, as well as numericals and forms with check boxes, are sent from participating institutions by Fax, processed using an optical character reader, and then transmitted to a host computer at a statistical center. Based on the facsimile data, RETAS can automatically review eligibility, collect patient identification data and provide a randomized treatment allocation. RETAS permits uninterrupted, unattended operation at a statistical center, 24 hours a day, 7 days a week. Therefore, it drastically decreases the workload of personnel at the statistical center needed to support central telephone registration coverage. Consequently, staff members are free to focus on patient registration, treatment allocation, and follow-up of patients. The treatment allocation procedure in this system is based on Pocock and Simon’s minimization method combined with Zelen’s method for institution balancing. By this system it was possible to balance treatment numbers for each level of various prognostic factors over an entire trial and, at the same time, balance the allocation of treatments within an institution. The system currently supports the protocol of a clinical trial for Adjuvant Chemo-Endocrine Therapy for Breast Cancer in West Japan.

scholarly journals A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Primary Outcome ◽  
Wald Test ◽  
Simple Solution ◽  
Nuisance Parameters ◽  
Simulation Studies ◽  
Fundamental Properties ◽  
Adaptive Clinical Trials ◽  
Treatment Comparisons ◽  
Practical Recommendations

AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

Testing hypotheses under adaptive randomization with continuous covariates in clinical trials

2018 ◽  
Vol 28 (6) ◽  
pp. 1609-1621
Author(s):  
Xiaoming Li ◽  
Jianhui Zhou ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Hypothesis Testing ◽  
Treatment Effects ◽  
Linear Models ◽  
Adaptive Designs ◽  
Cumulative Distribution ◽  
Type I ◽  
Percentile Method ◽  
Continuous Covariates ◽  
Leibler Divergence

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su’s percentile method, the empirical cumulative-distribution method, the Kullback–Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid.

Treatment allocation in clinical trials

Transfusion ◽  
2007 ◽  
Vol 47 (12) ◽  
pp. 2187-2188 ◽  
Author(s):  
Kathryn E. Webert
Keyword(s):  
Clinical Trials ◽  
Treatment Allocation

scholarly journals Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

2021 ◽  
Author(s):  
Elja Arjas ◽  
Dario Gasbarra
Keyword(s):  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Statistical Significance ◽  
Adaptive Designs ◽  
Critical Threshold ◽  
Attractive Alternative ◽  
Posterior Probabilities ◽  
Statistical Significance Testing ◽  
Treatment Allocation ◽  
Frequentist Paradigm

Abstract Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during Phase II and III. This approach is based on comparing the performance of the different treatment arm in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm (Rule 1), and treatment selection, removing an arm from the trial permanently (Rule 2). The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package. Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

A Treatment Allocation Procedure for Sequential Clinical Trials

Biometrics ◽  
1980 ◽  
Vol 36 (1) ◽  
pp. 81 ◽  
Author(s):  
Colin B. Begg ◽  
Boris Iglewicz
Keyword(s):  
Clinical Trials ◽  
Treatment Allocation ◽  
Allocation Procedure

A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate

2020 ◽  
Vol 29 (10) ◽  
pp. 2919-2931
Author(s):  
Xinyi Ge ◽  
Yingwei Peng ◽  
Dongsheng Tu
Keyword(s):  
Clinical Trial ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Function ◽  
Specific Treatment ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Longitudinal Outcomes ◽  
Continuous Covariate

Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer.

scholarly journals Standardising and Assessing Digital Images for Use in Clinical Trials: A Practical, Reproducible Method That Blinds the Assessor to Treatment Allocation

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e110395 ◽  
Author(s):  
Asha C. Bowen ◽  
Kara Burns ◽  
Steven Y. C. Tong ◽  
Ross M. Andrews ◽  
Robyn Liddle ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Digital Images ◽  
Treatment Allocation ◽  
Reproducible Method

scholarly journals Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Márcio A. Diniz ◽  
Gillian Gresham ◽  
Sungjin Kim ◽  
Michael Luu ◽  
N. Lynn Henry ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Adverse Events ◽  
Correspondence Analysis ◽  
Ductal Carcinoma ◽  
Single Agent ◽  
R Package ◽  
Two Dimensional ◽  
Loss Of Information ◽  
Essential Components ◽  
Shiny App

Abstract Background Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis. Methods We propose the application of stacked CA using contribution biplots as a tool to explore differences in AE data among treatments in clinical trials. We defined five levels of refinement for the analysis based on data derived from the Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms and their combinations. In addition, we developed a Shiny app built in an R-package, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 factorial trial comparing radiation therapy with either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to anastrozole in postmenopausal women with hormone-positive ductal carcinoma in situ. Results In the R04 trial (n = 1308), CA biplots displayed the discrepancies between single agent treatments and their combinations with Oxa at all levels of AE classes, such that these discrepancies were responsible for the largest portion of the explained variability among treatments. In addition, an interaction effect when adding Oxa to Cape/5-FU was identified when the distance between Cape+Oxa and 5-FU + Oxa was observed to be larger than the distance between 5-FU and Cape, with Cape+Oxa and 5-FU + Oxa in different quadrants of the CA biplots. In the B35 trial (n = 3009), CA biplots showed different patterns for non-adherent Anastrozole and Tamoxifen compared with their adherent counterparts. Conclusion CA with contribution biplot is an effective tool that can be used to summarize AE data in a two-dimensional display while minimizing the loss of information and interpretation.

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