An iterative dimension-by-dimension method for structural interval response prediction with multidimensional uncertain variables

Electroconvulsive therapy response prediction: A new role for plasminogen activator inhibitor and tissue-type plasminogen activator?

10.1055/s-0039-1679146 ◽

2019 ◽

Author(s):

N Moschny ◽

A Neyazi ◽

K Jahn ◽

H Maier ◽

M Bajbouj ◽

...

Keyword(s):

Plasminogen Activator ◽

Electroconvulsive Therapy ◽

Therapy Response ◽

Plasminogen Activator Inhibitor ◽

Response Prediction ◽

Tissue Type ◽

Tissue Type Plasminogen Activator ◽

Type Plasminogen Activator ◽

Activator Inhibitor

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Liquid Biopsies in the Management of Bladder Cancer: Next-Generation Biomarkers for Diagnosis, Surveillance, and Treatment-Response Prediction

Critical Reviews™ in Oncogenesis ◽

10.1615/critrevoncog.2017020803 ◽

2017 ◽

Vol 22 (5-6) ◽

pp. 389-401 ◽

Cited By ~ 1

Author(s):

Yu Yang ◽

Catherine R. Miller ◽

Antonio Lopez-Beltran ◽

Rodolfo Montironi ◽

Monica Cheng ◽

...

Keyword(s):

Bladder Cancer ◽

Treatment Response ◽

Response Prediction ◽

Next Generation ◽

Liquid Biopsies ◽

Treatment Response Prediction

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Statistical method of steepest improvement of response

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2018-84-11-74-87 ◽

2018 ◽

Vol 84 (11) ◽

pp. 74-87

Author(s):

V. B. Bokov

Keyword(s):

Statistical Method ◽

Linear Model ◽

Response Prediction ◽

Initial Experiment ◽

First Order ◽

Prediction Response ◽

Conditional Extremum ◽

Order Polynomial ◽

Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

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