Fitting Emax models to clinical trial dose-response data when the high dose asymptote is ill defined

2014 ◽  
Vol 13 (6) ◽  
pp. 364-370 ◽  
Author(s):  
P. Brain ◽  
S. Kirby ◽  
R. Larionov
Keyword(s):  
Clinical Trial ◽  
Dose Response ◽  
High Dose ◽  
Response Data

Fitting Emax models to clinical trial dose-response data

2011 ◽  
Vol 10 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Simon Kirby ◽  
Phil Brain ◽  
Byron Jones
Keyword(s):  
Clinical Trial ◽  
Dose Response ◽  
Response Data

Anticoagulation by Constant Subcutaneous Heparin Infusion

1982 ◽  
Vol 47 (01) ◽  
pp. 001-002 ◽  
Author(s):  
Nenita Parrilla ◽  
Jack Ansell
Keyword(s):  
Clinical Trial ◽  
Impedance Plethysmography ◽  
High Dose ◽  
Intravenous Therapy ◽  
Heparin Dose ◽  
Heparin Infusion ◽  
Subcutaneous Infusion ◽  
Subcutaneous Heparin ◽  
Therapeutic Anticoagulation ◽  
Continuous Subcutaneous Infusion

SummaryA preliminary clinical trial was conducted to determine the feasibility of achieving and regulating therapeutic anticoagulation with heparin given by continuous subcutaneous infusion. Five patients with deep venous thrombosis confirmed by impedance plethysmography and/or venography were studied. All patients received an initial heparin dose of 5000 units by IV bolus. This was followed by a continuous subcutaneous heparin infusion at a dose of 15 to 25 units per kilogram per hour. Effective levels of anticoagulation were achieved in all five patients. Regulation and maintenance of therapeutic anticoagulation were no more difficult than with intravenous therapy. No major complications were encountered during therapy.Continuous subcutaneous infusion of heparin may have advantages over standard intravenous therapy or high dose intermittent subcutaneous therapy. However, more extensive clinical evaluation is warranted.

scholarly journals Weighted mixed-effects dose–response models for tables of correlated contrasts

2021 ◽  
Vol 21 (2) ◽  
pp. 320-347
Author(s):  
Nicola Orsini
Keyword(s):  
Dose Response ◽  
Response Pattern ◽  
Mixed Effects ◽  
Data Generation ◽  
Measures Of Association ◽  
Response Models ◽  
Response Data ◽  
Hazard Ratios ◽  
Health Related ◽  
Generation Mechanisms

Recognizing a dose–response pattern based on heterogeneous tables of contrasts is hard. Specification of a statistical model that can consider the possible dose–response data-generating mechanism, including its variation across studies, is crucial for statistical inference. The aim of this article is to increase the understanding of mixed-effects dose–response models suitable for tables of correlated estimates. One can use the command drmeta with additive (mean difference) and multiplicative (odds ratios, hazard ratios) measures of association. The postestimation command drmeta_graph greatly facilitates the visualization of predicted average and study-specific dose–response relationships. I illustrate applications of the drmeta command with regression splines in experimental and observational data based on nonlinear and random-effects data-generation mechanisms that can be encountered in health-related sciences.

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