Estimation of a generalized linear mixed model for response‐adaptive designs in multi‐centre clinical trials

Selvaratnam, Selvakkadunko; Oyet, Alwell J.; Yi, Yanqing; Gadag, Veeresh

doi:10.1002/cjs.11324

Cited by 2 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selvaratnam et al. () have described how the allocation function of DBCD developed by Hu and Zhang () can be used in a RA design to assign treatments to patients in a way that targets the odds‐ratio‐based limiting allocation proportion

ρ false(P_{A S}, P_{B S} false) = \frac{O R (P_{A S}, P_{B S})}{1 + O R (P_{A S}, P_{B S})},

where

P_{A S}

and

P_{B S}

are the success probabilities for those patients assigned to treatment A and B , respectively, and

O R false(P_{A S}, P_{B S} false) = \frac{P_{A S}}{(1 - P_{A S})} \frac{(1 - P_{B S})}{P_{B S}} .

This odds‐ratio‐based RA design was used in our simulation studies for comparison with CARA and CR designs due to its similarity in formulation to the CARA design we have also used in our simulation. Concerning the assignment of treatments based on CARA design, suppose that the minimum number of observations required for finding a unique solution to the ML estimating equation is n 0 .…”

Section: The Logit Model and Parameter Estimationmentioning

confidence: 99%

“…We note that to implement CARA designs, a user must first identify a suitable ideal model for the relationship between the response, treatment assigned to each patient and covariates. An initial model is however not required to implement RA designs (see Rosenberger et al., and Selvaratnam et al., ). The absence of sufficient data at the initial stage of a trial makes the identification of a suitable model a challenging task since the primary concern at the initial stage of a trial is the selection of a suitable design for treatment assignment.…”

Section: Introductionmentioning

confidence: 99%

“…This class of models, first introduced by Nelder and Wedderburn (), have been implemented in various disciplines such as agriculture, economics, engineering, medicine, and the social sciences (see Lindsey ). Recently, Selvaratnam, Oyet, Yi, and Gadag () developed a GLMM for RA designs in multi‐center clinical trials to account for within center correlation, between center heterogeneity and common fixed effects across centers. They assumed that a RA design was implemented in each of J centers, randomly selected from a large number of centers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Selvaratnam

Oyet

2018

Biometrical J

Self Cite

View full text Add to dashboard Cite

Due to increasing discoveries of biomarkers and observed diversity among patients, there is growing interest in personalized medicine for the purpose of increasing the well-being of patients (ethics) and extending human life. In fact, these biomarkers and observed heterogeneity among patients are useful covariates that can be used to achieve the ethical goals of clinical trials and improving the efficiency of statistical inference. Covariate-adjusted response-adaptive (CARA) design was developed to use information in such covariates in randomization to maximize the well-being of participating patients as well as increase the efficiency of statistical inference at the end of a clinical trial. In this paper, we establish conditions for consistency and asymptotic normality of maximum likelihood (ML) estimators of generalized linear models (GLM) for a general class of adaptive designs. We prove that the ML estimators are consistent and asymptotically follow a multivariate Gaussian distribution. The efficiency of the estimators and the performance of response-adaptive (RA), CARA, and completely randomized (CR) designs are examined based on the well-being of patients under a logit model with categorical covariates. Results from our simulation studies and application to data from a clinical trial on stroke prevention in atrial fibrillation (SPAF) show that RA designs lead to ethically desirable outcomes as well as higher statistical efficiency compared to CARA designs if there is no treatment by covariate interaction in an ideal model. CARA designs were however more ethical than RA designs when there was significant interaction. K E Y W O R D Sadaptive designs, clinical trials, consistency, generalized linear models, maximum likelihood estimation 630

show abstract

ρ false(P_{A S}, P_{B S} false) = \frac{O R (P_{A S}, P_{B S})}{1 + O R (P_{A S}, P_{B S})},

where

P_{A S}

and

P_{B S}

are the success probabilities for those patients assigned to treatment A and B , respectively, and

O R false(P_{A S}, P_{B S} false) = \frac{P_{A S}}{(1 - P_{A S})} \frac{(1 - P_{B S})}{P_{B S}} .

Section: The Logit Model and Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Selvaratnam

Oyet

2018

Biometrical J

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, various adaptive designs for treatment assignments which satisfy the ethical principles of clinical trials have been developed and found to be useful alternatives to completely randomized designs. See for instance, Selvaratnam, Oyet, Yi and Gadag (2017) for a recent discussion of examples of adaptive designs such as response adaptive (RA), covariate adaptive (CA) and covariate-adjusted response adaptive (CARA) designs. Since patients react differently to a given treatment, it has become increasingly important to also account for the effect of certain characteristics or covariates of individual patients as well as treatmentby-covariate interaction on the response of patients during treatment comparisons.…”

Section: Introductionmentioning

confidence: 99%

Three influential design quantities on the power of Wald-type tests for treatment comparisons in clinical trials

Selvaratnam

Oyet

2023

J. of Statis. Res.

View full text Add to dashboard Cite

In clinical trials, efﬁcient statistical inference is critical to the well-being of future patients. We therefore construct Wald-type tests for the hypothesis of treatment-by-covariate interaction when treatments are assigned to patients by an adaptive design and the true model is a generalized linear model. Our measure of efﬁciency is the power of the test while ethics of a trial or well-being of participating patients is measured by the success rate of treatments. We demonstrate that the power of the test depends on the target allocation proportion, the bias of the randomization procedure from the target, and the variability induced by the randomization process (design variability) for adaptive designs. We prove that these quantities inﬂuence the power when the trial involves two treatments and a single covariate. We also show that, in this case, as design variability decreases the power increases. Due to the complexity of the problem, we demonstrate by simulation that this result still holds when more than one covariate is present in the model. In simulation studies, we compare the measures of efﬁciency and ethics under response-adaptive (RA), covariate-adjusted responseadaptive (CARA), and completely randomized (CR) designs. The methods are applied to data from a clinical trial on stroke prevention in atrial ﬁbrillation (SPAF). Journal of Statistical Research 2022, Vol. 56, No. 1, pp. 11-36

show abstract

Estimation of a generalized linear mixed model for response‐adaptive designs in multi‐centre clinical trials

Cited by 2 publications

References 31 publications

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Three influential design quantities on the power of Wald-type tests for treatment comparisons in clinical trials

Contact Info

Product

Resources

About