Exercise 9 Building and validation of a non linear calibration curve (logistic model) for analytical purposes.

Slides:



Advertisements
Similar presentations
Aim: Creating the model of thermal decomposition of energetic material (tetryl). Data:4 results (data sets) of DSC experiment carried out in linear heating.
Advertisements

11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Chapter 7 Statistical Data Treatment and Evaluation
Correlation and Linear Regression.
Experimental Design, Response Surface Analysis, and Optimization
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
Model specification (identification) We already know about the sample autocorrelation function (SAC): Properties: Not unbiased (since a ratio between two.
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
Regression Analysis Once a linear relationship is defined, the independent variable can be used to forecast the dependent variable. Y ^ = bo + bX bo is.
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 13 Nonlinear and Multiple Regression.
A Mathematica ® based regression analysis program Analisys … A Curve Fitting Application.
Nonlinear Regression Ecole Nationale Vétérinaire de Toulouse Didier Concordet ECVPT Workshop April 2011 Can be downloaded at
P M V Subbarao Professor Mechanical Engineering Department
Regression Analysis Using Excel. Econometrics Econometrics is simply the statistical analysis of economic phenomena Here, we just summarize some of the.
Chemical Analysis Qualitative Analysis Quantitative Analysis Determination “Analyze” a paint sample for lead “Determine” lead in a paint sample.
Classification and Prediction: Regression Via Gradient Descent Optimization Bamshad Mobasher DePaul University.
x – independent variable (input)
1 Exercise 10 &11 One-compartmental model: Simultaneous IV, oral and IM dosing for a new antibiotic in pigs.
Development of Empirical Models From Process Data
Nonlinear Regression Probability and Statistics Boris Gervits.
Engineering Computation Curve Fitting 1 Curve Fitting By Least-Squares Regression and Spline Interpolation Part 7.
13-1 Designing Engineering Experiments Every experiment involves a sequence of activities: Conjecture – the original hypothesis that motivates the.
Business Statistics - QBM117 Interval estimation for the slope and y-intercept Hypothesis tests for regression.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
1 732G21/732A35/732G28. Formal statement  Y i is i th response value  β 0 β 1 model parameters, regression parameters (intercept, slope)  X i is i.
Differential Equations and Boundary Value Problems
Linear Regression/Correlation
Classification and Prediction: Regression Analysis
Review for Final Exam Some important themes from Chapters 9-11 Final exam covers these chapters, but implicitly tests the entire course, because we use.
Chemometrics Method comparison
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.
Calibration & Curve Fitting
Introduction to Linear Regression and Correlation Analysis
Elements of Multiple Regression Analysis: Two Independent Variables Yong Sept
Hypothesis Testing in Linear Regression Analysis
1 Exercise 13 Deconvolution analysis vs. modeling to document the process of drug absorption.
BsysE595 Lecture Basic modeling approaches for engineering systems – Summary and Review Shulin Chen January 10, 2013.
Analytical vs. Numerical Minimization Each experimental data point, l, has an error, ε l, associated with it ‣ Difference between the experimentally measured.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 4 Curve Fitting.
Ordinary Differential Equations
Computational Physics Introduction 3/30/11. Goals  Calculate solutions to physics problems  All physics problems can be formulated mathematically. 
CHAPTER 3 Model Fitting. Introduction Possible tasks when analyzing a collection of data points: Fitting a selected model type or types to the data Choosing.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
1 11 Simple Linear Regression and Correlation 11-1 Empirical Models 11-2 Simple Linear Regression 11-3 Properties of the Least Squares Estimators 11-4.
Population Pharmacokinetic Characteristics of Levosulpiride and Terbinafine in Healthy Male Korean Volunteers Yong-Bok Lee College of Pharmacy and Institute.
STA 286 week 131 Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression.
IX. Transient Model Nonlinear Regression and Statistical Analysis.
1 Exercise 15 Drug binding and investigation of the Michaelis Menten equation; pitfall of methods of linearisation.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Review of fundamental 1 Data mining in 1D: curve fitting by LLS Approximation-generalization tradeoff First homework assignment.
Validation Defination Establishing documentary evidence which provides a high degree of assurance that specification process will consistently produce.
Engineers often: Regress data to a model  Used for assessing theory  Used for predicting  Empirical or theoretical model Use the regression of others.
Example x y We wish to check for a non zero correlation.
1 Development of Empirical Models From Process Data In some situations it is not feasible to develop a theoretical (physically-based model) due to: 1.
Statistics 350 Review. Today Today: Review Simple Linear Regression Simple linear regression model: Y i =  for i=1,2,…,n Distribution of errors.
Linear Regression. Regression Consider the following 10 data pairs comparing the yield of an experiment to the temperature at which the experiment was.
Regression Analysis Part A Basic Linear Regression Analysis and Estimation of Parameters Read Chapters 3, 4 and 5 of Forecasting and Time Series, An Applied.
Chapter 7. Classification and Prediction
Class Notes on Nonlinear Regression
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Regression Analysis Module 3.
CHAPTER 3 NUMERICAL METHODS.
John Loucks St. Edward’s University . SLIDES . BY.
Regression Analysis Week 4.
Statistical Methods For Engineers
Nonlinear regression.
M248: Analyzing data Block D UNIT D2 Regression.
Simple Linear Regression
Autoregressive and Growth Curve Models
Presentation transcript:

Exercise 9 Building and validation of a non linear calibration curve (logistic model) for analytical purposes

Objectives of the exercise To study the 4-parameter logistic (4-PL) model for fitting ligand binding assays To create a custom model using WinNonlin command language To fit a data set with the User Model To inspect and understand some outputs of WNL as: Eigenvalues, condition number, correlation matrix… The plot of the partial derivatives for the optimal design of a calibration curve… To update the 4-PL model to fit data to a 5-PL model To see influence of adding a parameter on the goodness of the fit (Akaike’s Information criterion) … To understand why adding a parameter to a model can lead to a very poor precision in the estimate of the parameters (the question of Bias versus Variance tradeoff); numerical identifiab

Non-linear calibration cuve Many analytical methods, primarily ligand binding (LBA) assay as immunoassay, generate nonlinear standard curves. The currently accepted reference model for these calibration curves is the 4-parameter logistic (4-PL) model,

Typical calibration curve for immunoassay

Equation describing the 4-PL model Y is the response, D is the response at infinite analyte concentration, A is the response at zero analyte concentration, X is the analyte concentration, C is the inflection point on the calibration curve (named later IC50 i.e. the concentration of the standard, predicted to yield 50% activity of the assay) B is a slope factor that defines the steepness of the curve.

Creating a new ASCII model in WNL ASCII user models are custom models (with or without differential equations) written in WinNonlin commands.

Raw data : immunoassay for melatonin

Plot of raw data with WNL

ASCII model for the logistic model

The model can be copied in word and pasted in WNL remark ****************************************************** remark Developer: PL toutain remark Model Date: 03-19-2011 remark Model Version: 1.0 remark remark - Commands define model-specific commands ; remark -this block contains WNL commands you wish to make permanent part of the model as the number of functions, remark -the number of parameters, the names of parameters etc. COMMANDS NFUNCTIONS 1 NPARAMETERS 4 PNAMES 'Top', 'Bottom', 'Slope', 'EC50' END remark Functions define algebraic functions; here we have only one equation associated to our data FUNCTION 1 toto=(X/EC50)**Slope tata=1+toto titi=Top-Bottom F= Bottom+(titi/tata) remark - EOM indicates end of model EOM

Chart output: fitted curve

Partial Derivatives and optimizing the design The design includes the number and spacing of the calibrator concentrations, as well as the location of the calibrators on the plate.

WinNonlin output CV%, univariate confidence interval Correlation Matrix Condition number variance-covariance matrix for the parameters Diagnostic table (AIC, SSE etc)

5-Parameters logistic (5-PL) model Update you 4-PL model The AIC (-31.39) is greater than the one obtained with the 4-PL (-33.38) An appropriate model is one that compromises between a model with biased estimates and a model with large variances—the so-called bias-variance trade-off (Bonate page 21)