Treatment Allocation for Nonlinear Models in Clinical Trials: The Logistic Model

Biometrics ◽  
1984 ◽  
Vol 40 (2) ◽  
pp. 409 ◽  
Author(s):  
Colin B. Begg ◽  
Leslie A. Kalish
Keyword(s):  
Clinical Trials ◽  
Logistic Model ◽  
Nonlinear Models ◽  
Treatment Allocation

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

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.

scholarly journals Growth in weight and of some tissues in the bullfrog: fitting nonlinear models during the fattening phase

2014 ◽  
Vol 38 (6) ◽  
pp. 598-606 ◽  
Author(s):  
Marcelo Maia Pereira ◽  
Cleber Fernando Menegasso Mansano ◽  
Edney Pereira da Silva ◽  
Marta Verardino De Stéfani
Keyword(s):  
Food Intake ◽  
Logistic Model ◽  
Fat Body ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Live Weight ◽  
Initial Weight ◽  
Expected Values ◽  
Growth Of Animals ◽  
Cumulative Food Intake

Knowledge of the growth of animals is important so that zootechnical activity can be more accurate and sustainable. The objective of this study was to describe the live weight, development of liver tissue and fat body, leg growth, and cumulative food intake of bullfrogs during the fattening phase using nonlinear models. A total of 2,375 bullfrog froglets with an initial weight of 7.03 ± 0.16 g were housed in five fattening pens (12 m²). Ten samplings were performed at intervals of 14 days to obtain the variables studied. These data were used to estimate the parameters of Gompertz and logistic models as a function of time. The estimated values of weight (Wm) and food intake (FIm) at maturity and time when the growth rate is maximum (t*) were closer to expected values when the logistic model was used. The Wm values for live weight and liver, adipose and leg weights and the FIm value for food intake were 343.7, 15.7, 19.6, 96.03 and 369.3 g, respectively, with t* at 109, 98, 105, 109 and 107 days. Therefore, the logistic model was the best model to estimate the growth and food intake of bullfrogs during the fattening phase.

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

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 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.

scholarly journals A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials

2015 ◽  
Vol 26 (3) ◽  
pp. 1078-1092 ◽  
Author(s):  
Amy S Nowacki ◽  
Wenle Zhao ◽  
Yuko Y Palesch
Keyword(s):  
Clinical Trials ◽  
Acute Stroke ◽  
Computer Simulations ◽  
Primary Outcome ◽  
Expected Number ◽  
Treatment Allocation ◽  
Adaptive Randomization ◽  
Surrogate Outcome ◽  
Surrogate Outcomes ◽  
Replacement Algorithm

Response-adaptive randomization (RAR) offers clinical investigators benefit by modifying the treatment allocation probabilities to optimize the ethical, operational, or statistical performance of the trial. Delayed primary outcomes and their effect on RAR have been studied in the literature; however, the incorporation of surrogate outcomes has not been fully addressed. We explore the benefits and limitations of surrogate outcome utilization in RAR in the context of acute stroke clinical trials. We propose a novel surrogate-primary (S-P) replacement algorithm where a patient’s surrogate outcome is used in the RAR algorithm only until their primary outcome becomes available to replace it. Computer simulations investigate the effect of both the delay in obtaining the primary outcome and the underlying surrogate and primary outcome distributional discrepancies on complete randomization, standard RAR and the S-P replacement algorithm methods. Results show that when the primary outcome is delayed, the S-P replacement algorithm reduces the variability of the treatment allocation probabilities and achieves stabilization sooner. Additionally, the S-P replacement algorithm benefit proved to be robust in that it preserved power and reduced the expected number of failures across a variety of scenarios.

Sequential treatment allocation in clinical trials

Biometrika ◽  
1971 ◽  
Vol 58 (3) ◽  
pp. 419-426 ◽  
Author(s):  
B. J. FLEHINGER ◽  
T. A. LOUIS
Keyword(s):  
Clinical Trials ◽  
Sequential Treatment ◽  
Treatment Allocation
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