Logistic regression, survival analysis, model II regression

Slides:



Advertisements
Similar presentations
Assumptions underlying regression analysis
Advertisements

Survival Analysis. Key variable = time until some event time from treatment to death time for a fracture to heal time from surgery to relapse.
Chapter 2 Describing Contingency Tables Reported by Liu Qi.
If we use a logistic model, we do not have the problem of suggesting risks greater than 1 or less than 0 for some values of X: E[1{outcome = 1} ] = exp(a+bX)/
A. The Basic Principle We consider the multivariate extension of multiple linear regression – modeling the relationship between m responses Y 1,…,Y m and.
Survival Analysis-1 In Survival Analysis the outcome of interest is time to an event In Survival Analysis the outcome of interest is time to an event The.
4.1: Linearizing Data.
- Word counts - Speech error counts - Metaphor counts - Active construction counts Moving further Categorical count data.
Multinomial Logistic Regression David F. Staples.
Logistic Regression Psy 524 Ainsworth.
Logistic Regression.
Departments of Medicine and Biostatistics
Logistic Regression STA302 F 2014 See last slide for copyright information 1.
Models with Discrete Dependent Variables
April 25 Exam April 27 (bring calculator with exp) Cox-Regression
Logistic Regression Multivariate Analysis. What is a log and an exponent? Log is the power to which a base of 10 must be raised to produce a given number.
Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review
Introduction to Linear and Logistic Regression. Basic Ideas Linear Transformation Finding the Regression Line Minimize sum of the quadratic residuals.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Generalized Linear Models
1 B. The log-rate model Statistical analysis of occurrence-exposure rates.
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.
Regression and Correlation
Survival analysis Brian Healy, PhD. Previous classes Regression Regression –Linear regression –Multiple regression –Logistic regression.
Education 795 Class Notes Applied Research Logistic Regression Note set 10.
Overall agenda Part 1 and 2  Part 1: Basic statistical concepts and descriptive statistics summarizing and visualising data describing data -measures.
G Lecture 121 Analysis of Time to Event Survival Analysis Language Example of time to high anxiety Discrete survival analysis through logistic regression.
Design and Analysis of Clinical Study 11. Analysis of Cohort Study Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia.
ALISON BOWLING THE GENERAL LINEAR MODEL. ALTERNATIVE EXPRESSION OF THE MODEL.
Excepted from HSRP 734: Advanced Statistical Methods June 5, 2008.
Logistic Regression STA2101/442 F 2014 See last slide for copyright information.
Lecture 6 Forestry 3218 Forest Mensuration II Lecture 6 Double Sampling Cluster Sampling Sampling for Discrete Variables Avery and Burkhart, Chapter 3.
Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.
Logistic regression. Analysis of proportion data We know how many times an event occurred, and how many times did not occur. We want to know if these.
CS 478 – Tools for Machine Learning and Data Mining Linear and Logistic Regression (Adapted from various sources) (e.g., Luiz Pessoa PY 206 class at Brown.
Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Assessing Binary Outcomes: Logistic Regression Peter T. Donnan Professor of Epidemiology and Biostatistics Statistics for Health Research.
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.
Introduction to logistic regression and Generalized Linear Models July 14, 2011 Introduction to Statistical Measurement and Modeling Karen Bandeen-Roche,
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Model Building and Model Diagnostics Chapter 15.
Multiple Logistic Regression STAT E-150 Statistical Methods.
Multiple Regression  Similar to simple regression, but with more than one independent variable R 2 has same interpretation R 2 has same interpretation.
01/20151 EPI 5344: Survival Analysis in Epidemiology Actuarial and Kaplan-Meier methods February 24, 2015 Dr. N. Birkett, School of Epidemiology, Public.
Université d’Ottawa - Bio Biostatistiques appliquées © Antoine Morin et Scott Findlay :32 1 Logistic regression.
Logistic Regression Analysis Gerrit Rooks
1 Introduction to Modeling Beyond the Basics (Chapter 7)
Roger B. Hammer Assistant Professor Department of Sociology Oregon State University Conducting Social Research Logistic Regression Categorical Data Analysis.
Nonparametric Statistics
Logistic Regression For a binary response variable: 1=Yes, 0=No This slide show is a free open source document. See the last slide for copyright information.
Instructor: R. Makoto 1richard makoto UZ Econ313 Lecture notes.
Logistic Regression: Regression with a Binary Dependent Variable.
Carolinas Medical Center, Charlotte, NC Website:
Nonparametric Statistics
April 18 Intro to survival analysis Le 11.1 – 11.2
Survival curves We know how to compute survival curves if everyone reaches the endpoint so there is no “censored” data. Survival at t = S(t) = number still.
Metodología de la investigación cuantitativa FIBHUG
THE LOGIT AND PROBIT MODELS
Generalized Linear Models
Generalized Linear Models
Statistics 103 Monday, July 10, 2017.
Jeffrey E. Korte, PhD BMTRY 747: Foundations of Epidemiology II
THE LOGIT AND PROBIT MODELS
Nonparametric Statistics
Chapter 12 Inference on the Least-squares Regression Line; ANOVA
DCAL Stats Workshop Bodo Winter.
Jeffrey E. Korte, PhD BMTRY 747: Foundations of Epidemiology II
Regression Part II.
Diagnostics and Remedial Measures
Presentation transcript:

Logistic regression, survival analysis, model II regression

Logistic regression Response (dependent) variable is either yes/ no (alive/ dead, flowering/sterile) Number of positive cases out of total (seed germination, number of flowering individuals out of total no of individuals) – assuming binomial distribution Regression model predicts probability, i.e. value between 0 and 1

Logistic regression 2 Logit transformation: log( p / (1-p) ) = log (odds ratio) Can not be applied directly to 0/1, applied on predicted probabilities: p in (0, 1) Special case of Generalized linear models (GLM)

Logistická regrese a Statistica Example – survival of winter depending on flowering and rhizome size Advanced Linear / Nonlinear Models Generalized ... Models Logit model Or non-linear estimation...

Possible application Example – how is the probability of survival over the winter affected by flowering in previous summer, storage of sugars, and length of the winter? Surmalog.xls, list ReprEff

Survival analysis Survival analysis, mainly in medicine Useful for data (usually about time) with censoring Most often right censoring: I have finished the experiment, but some individuals are still alive (or did not germinate yet etc.] Left censoring For data without censoring are probably simpler methods available - mostly generalized linear models)

Survival curve Kaplan-Meier method:

Míra rizika Hazard rate, l: pravděpodobnost, že jedinec přežije časový úsek t, pokud se jej již dožil Kumulativní funkce míry rizika L(t): ve vztahu ke křivce přežívání platí: L(t) = - log S(t) Využití l u složitějších modelů analýzy přežívání (Coxův model relativního rizika, Cox proportional hazard rate): l(t) = l0(t)*eb0+b1x1+b2x2+…

Use of survival analysis? Comparison of survival curves among groups Estimate “halftime” (of life, survival time, time to germination) with confidence interval Testing effects of both quantitative and qualitative predictors

Survival analysis - exercises Germination dynamics affected by chilling, file Surmalog.xls, sheet Germination, method Comparing two samples Effect of radio-collars on survival of antilops -obojků na úmrtnost antilop, file Surmalog.xls, sheet RadioCollars, method Regression / Proportional hazard (Cox) regression

Regression model typ II In ordinary Least Squares, in dependence of Y on X, vertical differences are minimized (i.e. (Y-Ypredicted)2 Similarly, if we study X ~ Y, (X-Xpredicted)2 is minimized. The angel among the two lines decreases with increasing (r) Major axis (MA) regression – symmetric – what is perpendicular depends on units – various standardizations

MA regression: motivation Zkoumáme vztah mezi délkou (L) a hmotností (M) jedinců určitého druhu Pokud se tvar těla s růstem nemění (isometrický růst), lze vztah popsat takto: M = c*L3 a po logaritmování: log(M) = b0 + 3*log(L), kde b0 = log(c) Při užití „normální“ regrese bude ale odhadnutý koeficient b1 < 3

Alometric biomass partitioning Allometric biomass partitioning theory (APT): : Mleaves = b1*Mroots3/4 B.J. Enquist & K.J. Nikolas (2002): Global allocation rules for patterns of biomass partitioning in seed plants. Science 295, 1517-1520.

MA regression: example Vztah biomasy listů a stonků: Mleaves = b1*Mroots3/4 After log transformation, slope should be 0.75 Various herb species RMA program : http://www.bio.sdsu.edu/pub/andy/rma.html