Comparing D-optimal designs with common mixture experimental designs for logistic regression

Mancenido, Michelle V.; Pan, Rong; Montgomery, Douglas C.; Anderson‐Cook, Christine M.

doi:10.1016/j.chemolab.2019.02.003

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…Obviously, a system other than a thermally initiated RAFT polymerization will require different factors, new setting of factor levels, and perhaps even utilization of a better suited design geometry than an FC-CCD. Here, the so-called computer-generated optimal designs have especially received attention due to their great flexibility and often optimal compromise of prediction accuracy and experimental effort [ 49 , 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

Eckert

Klein

Frieler³

et al. 2021

Polymers

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Despite the great potential of design of experiments (DoE) for efficiency and plannability in academic research, it remains a method predominantly used in industrial processes. From our perspective though, DoE additionally provides greater information gain than conventional experimentation approaches, even for more complex systems such as chemical reactions. Hence, this work presents a comprehensive DoE investigation on thermally initiated reversible addition–fragmentation chain transfer (RAFT) polymerization of methacrylamide (MAAm). To facilitate the adaptation of DoE for virtually every other polymerization, this work provides a step-by-step application guide emphasizing the biggest challenges along the way. Optimization of the RAFT system was achieved via response surface methodology utilizing a face-centered central composite design (FC-CCD). Highly accurate prediction models for the responses of monomer conversion, theoretical and apparent number averaged molecular weights, and dispersity are presented. The obtained equations not only facilitate thorough understanding of the observed system but also allow selection of synthetic targets for each individual response by prediction of the respective optimal factor settings. This work successfully demonstrates the great capability of DoE in academic research and aims to encourage fellow scientists to incorporate the technique into their repertoire of experimental strategies.

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Section: Discussionmentioning

confidence: 99%

Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

Eckert

Klein

Frieler³

et al. 2021

Polymers

View full text Add to dashboard Cite

show abstract

“…Furthermore, design of experiment (DOE) approach was applied to understudy the behavior in formulation of adhesive with acceptable plasticizer based on the design and response variables [6], the effects of test variables in shear strength of adhesive produced and followed by analysis of data using one-way analysis of variance (ANOVA) to compare with regression [12], and D-Optimal mixture design in optimization of ternary matrix blends for controlled zero-order drug release from oral dosage [14]. Thermal mixture DOEs as an alternative method in optimizing the aqueous phase composition of a microemulsion was carried out [19], D-Optimal mixture design to compare with other experimental design to study logistic for regression analyses [22] and regression model to predict the California bearing ratio (CBR) values of black cotton soil for stabilization of cement [2].…”

Section: Figurementioning

confidence: 99%

D-Optimal Response Mixture Design Modeling of Polystyrene Waste Adhesive Formulations

Hamidu¹,

Aroke

Osha

et al. 2021

Int. J. Eng. Tech. Mgmt. Res.

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The importance of polystyrene in handling and transportation of fragile equipment for safe delivery cannot be under stated. However; the post-usage has raised serious concern due to adverse effects caused by the litters on environment in blocking water-ways due to its weightlessness, release of oxides of carbon and resisting decomposition among others. This work was intended to model produced adhesive from polystyrene waste using Design Expert version 6.0.8 software and D-optimal mixture design for the responses analyses to obtain the best adhesive. Eight (8) experimental runs were generated for resin formulations with only 3 feasible, coded: R1, R4 and R7, based on 2 factorial design of experiment for resin formulations. Furthermore, 14 adhesive formulations were generated for each resin, coded: R1AD, R4AD and R7AD, that is, the formulated resin was combined with additives to produce adhesive using 3 factorial mixture designs and 4 responses, namely: viscosity, pH, percentage moisture content and percentage solid content. The responses were modeled using D-optimal mixture design: the viscosity response modeling was best fitted with quadratic model for R1AD produced adhesives, while R4AD and R7AD produced adhesives were fitted with Cubic model. The pH, percentage moisture content and percentage solid content responses were all fitted with cubic model based on the statistical and modeled data. The modeling solution was further optimized and validated for the three adhesive productions, the general selection of produced adhesive based on desirability factor and line with experimental analyses from the responses shows: R7AD2>R4AD1>R1AD1 produced adhesives in the order of fitness.

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“…Park, Mancenido & Montgomery (2019); Johnson & Montgomery (2009); Mancenido et al . (2019) and many others before them take a classic optimal design approach in response surface methodology (Box & Wilson 1951) employing a holistic metric of the estimation of all design parameters via

D

‐optimality or other classic metrics of optimality (Myers, Myers & Carter Jr 1994). The design problem in these works are different than in our setting, as they study the situation where the

\boldsymbol{x}

s could be determined by the experimenter.…”

Section: Introductionmentioning

confidence: 99%

The role of pairwise matching in experimental design for an incidence outcome

Kapelner,

Krieger,

Azriel

2023

Aus NZ J of Statistics

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SummaryWe consider the problem of evaluating designs for a two‐arm randomised experiment with an incidence (binary) outcome under a non‐parametric general response model. Our two main results are that the a priori pair matching design is (1) the optimal design as measured by mean squared error among all block designs which includes complete randomisation. And (2), this pair‐matching design is minimax, that is, it provides the lowest mean squared error under an adversarial response model. Theoretical results are supported by simulations and clinical trial data where we demonstrate the superior performance of pairwise matching designs under realistic conditions.

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Comparing D-optimal designs with common mixture experimental designs for logistic regression

Cited by 17 publications

References 17 publications

Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

Experimental Design in Polymer Chemistry—A Guide towards True Optimization of a RAFT Polymerization Using Design of Experiments (DoE)

D-Optimal Response Mixture Design Modeling of Polystyrene Waste Adhesive Formulations

The role of pairwise matching in experimental design for an incidence outcome

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