Design issues related to allocation of experimental units with known covariates into two treatment groups

Hore, Samrat; Dewanji, Anup; Chatterjee, Amlan

doi:10.1016/j.jspi.2014.06.002

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…From the optimal design perspective, the aim is to obtain an exact design by optimizing a design objective with the treatment allocation as the design variable given fixed covariates, which often results in an optimization problem with binary decision variables. For two treatment allocation, under the linear model assumption without interaction between covariates and treatments, [16] proposed computationally tractable algorithms to search D-and A-optimal designs. Also, with a particular interest of minimizing the variance of the estimated treatment effect (i.e., a D s optimal design criterion), [5] demonstrated that the problem can be solved efficiently based on a generalization of the MAX-CUT semi-definite programming (SDP) relaxation of [14].…”

Section: Instructionmentioning

confidence: 99%

Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates

Li¹,

Zhang²,

Khademi³

et al. 2023

The New England Journal of Statistics in Data Science

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Controlled experiments are widely applied in many areas such as clinical trials or user behavior studies in IT companies. Recently, it is popular to study experimental design problems to facilitate personalized decision making. In this paper, we investigate the problem of optimal design of multiple treatment allocation for personalized decision making in the presence of observational covariates associated with experimental units (often, patients or users). We assume that the response of a subject assigned to a treatment follows a linear model which includes the interaction between covariates and treatments to facilitate precision decision making. We define the optimal objective as the maximum variance of estimated personalized treatment effects over different treatments and different covariates values. The optimal design is obtained by minimizing this objective. Under a semi-definite program reformulation of the original optimization problem, we use a YALMIP and MOSEK based optimization solver to provide the optimal design. Numerical studies are provided to assess the quality of the optimal design.

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Section: Instructionmentioning

confidence: 99%

Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates

Li¹,

Zhang²,

Khademi³

et al. 2023

The New England Journal of Statistics in Data Science

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“…Stepwise procedures, on the other hand, often lead to locally optimal solutions rather than globally optimal solutions. However, there are several optimization techniques that help to find exact (global) optimal solution in the field of optimal design of experiments and clinical trials (Hore et al, 13 Hore et al 14 ). A variable neighborhood search algorithm coupled with a stochastic approach can be used for finding the optimal solution (Hore et al 15 ).…”

Section: Group Lassomentioning

confidence: 99%

A Bayesian group lasso classification for ADNI volumetrics data

Majumder

Maiti

Datta

2021

Stat Methods Med Res

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The primary objective of this paper is to develop a statistically valid classification procedure for analyzing brain image volumetrics data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in elderly subjects with cognitive impairments. The Bayesian group lasso method thereby proposed for logistic regression efficiently selects an optimal model with the use of a spike and slab type prior. This method selects groups of attributes of a brain subregion encouraged by the group lasso penalty. We conduct simulation studies for high- and low-dimensional scenarios where our method is always able to select the true parameters that are truly predictive among a large number of parameters. The method is then applied on dichotomous response ADNI data which selects predictive atrophied brain regions and classifies Alzheimer’s disease patients from healthy controls. Our analysis is able to give an accuracy rate of 80% for classifying Alzheimer’s disease. The suggested method selects 29 brain subregions. The medical literature indicates that all these regions are associated with Alzheimer’s patients. The Bayesian method of model selection further helps selecting only the subregions that are statistically significant, thus obtaining an optimal model.

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“…These three conditions play a very important role in the construction of optimal covariate designs under different complete/ incomplete block design set-ups without any neighbor effects. The monograph ofDas et al (2015) shows in detail Hore et al (2014). proposed a new algorithm for optimal or near optimal allocation where there is large number of experimental units and several covariates.Sinha et al (2014) discussed the formulation of problem of allocation of one controllable covariate in the context of an experiment involving several treatments.…”

mentioning

confidence: 99%

Study of Covariates’ Effects in the Presence of Neighbor Effects : An Informative Review

Sapam,

Sinha,

Meitei

2024

Int. J. of Stat. Sci.

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With reference to a Gauss-Markov Model, Analysis of Covariance (ANCOVA) is a standard exercise in the study of differential treatment effects in the presence of covariates. Again in the presence of ‘Neighbor Effects’, we carry out necessary data analysis in a routine manner. In this paper we present a review of this area of research, encompassing both covariates’ effects and neighbor effects. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 65-73

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Design issues related to allocation of experimental units with known covariates into two treatment groups

Cited by 11 publications

References 12 publications

Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates

Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates

A Bayesian group lasso classification for ADNI volumetrics data

Study of Covariates’ Effects in the Presence of Neighbor Effects : An Informative Review

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