Lecture 2: Poisson and logistic regression
|
|
- Blaise Potter
- 5 years ago
- Views:
Transcription
1 Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK S 3 RI, December 2014
2 introduction to Poisson regression application to the BELCAP study introduction to logistic regression confounding and effect modification comparing of different generalized regression models meta-analysis of BCG vaccine against tuberculosis
3 introduction to Poisson regression the Poisson distribution count data may follow such a distribution, at least approximately Examples: number of deaths, of diseased cases, of hospital admissions and so on... Y Po(µ): where µ > 0 P(Y = y) = µ y exp( µ)/y!
4 introduction to Poisson regression P(Y=y) y
5 introduction to Poisson regression but why not use a linear regression model? for a Poisson distribution we have E(Y ) = Var(Y ). This violates the constancy of variance assumption (for the conventional regression model) a conventional regression model assumes we are dealing with a normal distribution for the response Y, but the Poisson distribution may not look very normal the conventional regression model may give negative predicted means (negative counts are impossible!)
6 introduction to Poisson regression the Poisson regression model log E(Y i ) = log µ i = α + βx i the RHS of the above is called the linear predictor Y i Po(µ i ) this model is the log-linear model
7 introduction to Poisson regression the Poisson regression model can be written equivalently as log E(Y i ) = log µ i = α + βx i µ i = exp[α + βx i ] Hence it is clear that any fitted log-linear model will always give non-negative fitted values!
8 introduction to Poisson regression an interesting interpretation in the Poisson regression model suppose x represents a binary variable (yes/no, treatment present/not present) { 1 if person is in intervention group x = 0 otherwise log E(Y ) = log µ = α + βx x = 0: log µ no intervention = α + βx = α x = 1: log µ intervention = α + βx = α + β hence log µ intervention log µ no intervention = β
9 introduction to Poisson regression an interesting interpretation in the Poisson regression model hence or log µ intervention log µ no intervention = β µ intervention µ no intervention = exp(β) the coefficient exp(β) corresponds to the risk ratio comparing the mean risk in the treatment group to the mean risk in the control group
10 introduction to Poisson regression Poisson regression model for several covariates log E(Y i ) = α + β 1 x 1i + + β p x pi where x 1i,, x pi are the covariates of interest testing the effect of covariate x j is done by the size of the estimate ˆβ j of β j t j = ˆβ j s.e.( ˆβ j ) if t j > 1.96 covariate effect is significant
11 introduction to Poisson regression estimation of model parameters consider the likelihood (the probability for the observed data) L = n i=1 for model with p covariates: µ y i i exp( µ i )/y i! log µ i = α + β 1 x i1 + β 2 x i β p x ip finding parameter estimates by maximizing the likelihood L (or equivalently the log-likelihood log L) guiding principle: choosing the parameters that make the observed data the most likely
12 application to the BELCAP study The simple regression model for BELCAP with Y = DMFSe: log E(DMFSe i ) = α + β 1 OHE i + β 2 ALL 2i + β 4 ESD i + β 5 MW i + β 6 OHY i + β 7 DMFSb i { 1 if child i is in intervention OHE OHE i = 0 otherwise { 1 if child i is in intervention ALL ALL i = 0 otherwise
13 application to the BELCAP study analysis of BELCAP study using the Poisson regression model including the DMFS at baseline covariate ˆβj s.e.( ˆβ j ) t j P-value OHE ALL ESD MW OHY DMFSb
14 introduction to logistic regression Introduction to logistic regression Binary Outcome Y Y = { 1, Person diseased 0, Person healthy Probability that Outcome Y = 1 Pr(Y = 1) = p is probability for Y = 1
15 introduction to logistic regression Odds odds = p 1 p p = odds odds + 1 Examples p = 1/2 odds = 1 p = 1/4 odds = 1/3 p = 3/4 odds = 3/1 = 3
16 introduction to logistic regression Odds Ratio OR = odds( in exposure ) odds( in non-exposure ) = p 1/(1 p 1 ) p 0 /(1 p 0 ) Properties of odds ratio 0 < OR < OR = 1(p 1 = p 0 ) is reference value
17 introduction to logistic regression Examples risk = risk = { p 1 = 1/4 p 0 = 1/8 { p 1 = 1/100 p 0 = 1/1000 effect measure = eff. meas. = Fundamental Theorem of Epidemiology p 0 small OR RR { OR = p 1 /(1 p 1 ) p 0 /(1 p 0 ) = 1/3 1/7 = 2.33 RR = p 1 p 0 = 2 { OR = 1/99 1/999 = RR = p 1 p 0 = 10 benefit: OR is interpretable as RR which is easier to deal with
18 introduction to logistic regression A simple example: Radiation Exposure and Tumor Development cases non-cases E NE odds and OR odds for disease given exposure (in detail): 52/ /2872 = 52/2820 odds for disease given non-exposure (in detail): 6/ /5049 = 6/5043
19 introduction to logistic regression A simple example: Radiation Exposure and Tumor Development cases non-cases E NE OR odds ratio for disease (in detail): OR = 52/2820 6/5043 = = or, log OR = log = 2.74 for comparison RR = 52/2872 6/5049 = 15.24
20 introduction to logistic regression Logistic regression model for this simple situation where log p x 1 p x = α + βx p x = Pr(Y = 1 x) { 1, if exposure present x = 0, if exposure not present log px 1 p x is called the logit link that connects p x with the linear predictor
21 introduction to logistic regression benefits of the logistic regression model is feasible since whereas log p x = p x 1 p x = α + βx exp(α + βx) (0, 1) 1 + exp(α + βx) p x = α + βx is not feasible
22 introduction to logistic regression Interpretation of parameters α and β log p x 1 p x = α + βx now p 0 x = 0 : log 1 p 0 = α (1) p 1 x = 1 : log 1 p 1 = α + β (2) p 1 (2) (1) = log log = α + β α = β 1 p 1 1 p }{{ 0 } log p 1 1 p 1 p 0 1 p 0 =log OR p 0 log OR = β OR = e β
23 confounding and effect modification A simple illustration example cases non-cases E NE OR odds ratio: OR = =
24 confounding and effect modification stratified: Stratum 1: Stratum 2: cases non-cases E NE OR = = 1 cases non-cases E NE OR = = 1
25 confounding and effect modification Y E S freq
26 confounding and effect modification The logistic regression model for simple confounding where log p x 1 p x = α + βe + γs x = (E, S) is the covariate combination of exposure E and stratum S
27 confounding and effect modification in detail for stratum 1 now log p x 1 p x = α + βe + γs p 0,0 E = 0, S = 0 : log 1 p 0,0 = α (3) p 1,0 E = 1, S = 0 : log 1 p 1,0 = α + β (4) (4) (3) = log OR 1 = α + β α = β log OR = β OR = e β the log-odds ratio in the first stratum is β
28 confounding and effect modification in detail for stratum 2: now: log p x 1 p x = α + βe + γs p 0,1 E = 0, S = 1 : log 1 p 0,1 = α + γ (5) p 1,1 E = 1, S = 1 : log 1 p 1,1 = α + β + γ (6) (6) (5) = log OR 2 = α + β + γ α γ = β the log-odds ratio in the second stratum is β
29 confounding and effect modification important property of the confounding model: assumes the identical exposure effect in each stratum!
30 confounding and effect modification (crude analysis) Logistic regression Log likelihood = Y Odds Ratio Std. Err. [95% Conf. Interval] E (adjusted for confounder) Logistic regression Log likelihood = Y Odds Ratio Std. Err. [95% Conf. Interval] E S
31 confounding and effect modification A simple illustration example: passive smoking and lung cancer cases non-cases E NE OR odds ratio: OR = = 1.19
32 confounding and effect modification stratified: Stratum 1 (females): Stratum 2 (males): cases non-cases E NE OR = = 1.10 cases non-cases E NE OR = = 1.63
33 confounding and effect modification interpretation: effect changes from one stratum to the next stratum!
34 confounding and effect modification The logistic regression model for effect modification where log p x = α + βe + γs + (βγ) E S 1 p x }{{} effect modif. par. x = (E, S) is the covariate combination of exposure E and stratum S
35 confounding and effect modification in detail for stratum 1 now log p x 1 p x = α + βe + γs + (βγ)e S p 0,0 E = 0, S = 0 : log 1 p 0,0 = α (7) p 1,0 E = 1, S = 0 : log 1 p 1,0 = α + β (8) (8) (7) = log OR 1 = α + β α = β log OR = β OR = e β the log-odds ratio in the first stratum is β
36 confounding and effect modification in detail for stratum 2: log p x 1 p x = α + βe + γs + (βγ)e S now: p 0,1 E = 0, S = 1 : log 1 p 0,1 = α + γ (9) p 1,1 E = 1, S = 1 : log 1 p 1,1 = α + β + γ + (βγ) (10) (10) (9) = log OR 2 = α + β + γ + (βγ) α γ = β + (βγ) log OR = β OR = e β+(βγ) the log-odds ratio in the second stratum is β + (βγ)
37 confounding and effect modification important property of the effect modification model: effect modification model allows for different effects in the strata!
38 confounding and effect modification Data from passive smoking and LC example are as follows: Y E S ES freq
39 confounding and effect modification CRUDE EFFECT MODEL Logistic regression Log likelihood = Y Coef. Std. Err. z P> z E _cons
40 confounding and effect modification CONFOUNDING MODEL Logistic regression Log likelihood = Y Coef. Std. Err. z P> z E S _cons
41 confounding and effect modification EFFECT MODIFICATION MODEL Logistic regression Log likelihood = Y Coef. Std. Err. z P> z E S ES _cons
42 confounding and effect modification interpretation of crude effects model: log OR = OR = e = 1.19 interpretation of confounding model: log OR = OR = e = 1.24
43 confounding and effect modification interpretation of effect modification model: stratum 1: stratum 2: log OR 1 = OR 1 = e = 1.10 log OR 2 = OR 2 = e = 1.63
44 comparing of different generalized regression models Model evaluation in logistic regression: the likelihood approach: L = n i=1 is called the likelihood for models log p xi 1 p xi = p y i x i (1 p xi ) 1 y i { α + βe i + γs i + (βγ)e i S i, (M 1 ) α + βe i + γs i, (M 0 ) where M 1 is the effect modification model and M 0 is the confounding model
45 comparing of different generalized regression models Model evaluation in logistic regression using the likelihood ratio: let L(M 1 ) and L(M 0 ) be the likelihood for models M 1 and M 0 then LRT = 2 log L(M 1 ) 2 log L(M 0 ) = 2 log L(M 1) L(M 0 ) is called the likelihood ratio for models M 1 and M 0 and has a chi-square distribution with 1 df under M 0
46 comparing of different generalized regression models illustration for passive smoking and LC example: model log-likelihood LRT crude homogeneity effect modification note: for valid comparison on chi-square scale: models must be nested
47 comparing of different generalized regression models Model evaluation in more general: consider the likelihood L = n i=1 p y i x i (1 p xi ) 1 y i for a general model with p covariates: example: log p xi 1 p xi = α + β 1 x i1 + β 2 x i β p x ip (M 0 ) log p xi 1 p xi = α + β 1 AGE i + β 2 SEX i + β 3 ETS i
48 comparing of different generalized regression models Model evaluation in more general: example: log p xi 1 p xi = α + β 1 AGE i + β 2 SEX i + β 3 ETS i where these covariates can be mixed: quantitative, continuous such as AGE categorical binary (use 1/0 coding) such as SEX non-binary ordered or unordered categorical such as ETS (none, moderate, large)
49 comparing of different generalized regression models Model evaluation in more general: consider the likelihood L = n i=1 p y i x i (1 p xi ) 1 y i for model with additional k covariates: log p xi 1 p xi = α + β 1 x i1 + β 2 x i β p x ip +β p+1 x i,p β k+p x i,k+p (M 1 )
50 comparing of different generalized regression models Model evaluation in more general for our example: log p xi 1 p xi = α + β 1 AGE i + β 2 SEX i + β 3 ETS i +β 4 RADON i + β 5 AGE-HOUSE i
51 comparing of different generalized regression models Model evaluation using the likelihood ratio: again let L(M 1 ) and L(M 0 ) be the likelihood for models M 1 and M 0 then the likelihood ratio LRT = 2 log L(M 1 ) 2 log L(M 0 ) = 2 log L(M 1) L(M 0 ) has a chi-square distribution with k df under M 0
52 comparing of different generalized regression models Model evaluation for our example: then { M 0 : α + β 1 AGE i + β 2 SEX i + β 3 ETS i M 1 :...M β 4 RADON i + β 5 AGE-HOUSE i LRT = 2 log L(M 1) L(M 0 ) has under model M 0 a chi-square distribution with 2 df
53 comparing of different generalized regression models model evaluation for model assessment we will use criteria that compromise between model fit and model complexity Akaike information criterion Bayesian Information criterion AIC = 2 log L + 2k BIC = 2 log L + k log n where k is the number of parameters in the model and n is the number of clustered observations we seek a model for which AIC and/or BIC are small
54 meta-analysis of BCG vaccine against tuberculosis Meta-Analysis Meta-Analysis is a methodology for investigating the study results from several, independent studies with the purpose of an integrative analysis Meta-Analysis on BCG vaccine against tuberculosis Colditz et al. 1974, JAMA provide a meta-analysis to examine the efficacy of BCG vaccine against tuberculosis
55 meta-analysis of BCG vaccine against tuberculosis Data on the meta-analysis of BCG and TB the data contain the following details 13 studies each study contains: TB cases for BCG intervention number at risk for BCG intervention TB cases for control number at risk for control also two covariates are given: year of study and latitude expressed in degrees from equator latitude represents the variation in rainfall, humidity and environmental mycobacteria suspected of producing immunity against TB
56 meta-analysis of BCG vaccine against tuberculosis intervention control study year latitude TB cases total TB cases total
57 meta-analysis of BCG vaccine against tuberculosis Data analysis on the meta-analysis of BCG and TB these kind of data can be analyzed by taking TB case as disease occurrence response intervention as exposure study as confounder
58 meta-analysis of BCG vaccine against tuberculosis Log likelihood = Pseudo R2 = TB_Case Odds Ratio Std. Err. z P> z [95% Conf. Interval] Intervention _cons estat ic, n(13) Model Obs ll(null) ll(model) df AIC BIC Note: N=13 used in calculating BIC
59 meta-analysis of BCG vaccine against tuberculosis Logistic regression Number of obs = LR chi2(2) = Prob > chi2 = Log likelihood = Pseudo R2 = TB_Case Odds Ratio Std. Err. z P> z [95% Conf. Interval] Latitude Intervention _cons estat ic, n(13) Model Obs ll(null) ll(model) df AIC BIC Note: N=13 used in calculating BIC
60 meta-analysis of BCG vaccine against tuberculosis Logistic regression Number of obs = LR chi2(3) = Prob > chi2 = Log likelihood = Pseudo R2 = TB_Case Odds Ratio Std. Err. z P> z [95% Conf. Interval] Latitude Intervention Year _cons estat ic, n(13) Model Obs ll(null) ll(model) df AIC BIC Note: N=13 used in calculating BIC.
61 meta-analysis of BCG vaccine against tuberculosis model evaluation model log L AIC BIC intervention latitude year
Lecture 5: Poisson and logistic regression
Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK S 3 RI, 3-5 March 2014 introduction to Poisson regression application to the BELCAP study introduction
More informationLecture 4: Generalized Linear Mixed Models
Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK S 3 RI, 11-12 December 2014 An example with one random effect An example with two nested random effects
More informationLecture 12: Effect modification, and confounding in logistic regression
Lecture 12: Effect modification, and confounding in logistic regression Ani Manichaikul amanicha@jhsph.edu 4 May 2007 Today Categorical predictor create dummy variables just like for linear regression
More informationLecture 10: Introduction to Logistic Regression
Lecture 10: Introduction to Logistic Regression Ani Manichaikul amanicha@jhsph.edu 2 May 2007 Logistic Regression Regression for a response variable that follows a binomial distribution Recall the binomial
More informationHomework Solutions Applied Logistic Regression
Homework Solutions Applied Logistic Regression WEEK 6 Exercise 1 From the ICU data, use as the outcome variable vital status (STA) and CPR prior to ICU admission (CPR) as a covariate. (a) Demonstrate that
More informationConfounding and effect modification: Mantel-Haenszel estimation, testing effect homogeneity. Dankmar Böhning
Confounding and effect modification: Mantel-Haenszel estimation, testing effect homogeneity Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK Advanced Statistical
More informationModelling Rates. Mark Lunt. Arthritis Research UK Epidemiology Unit University of Manchester
Modelling Rates Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester 05/12/2017 Modelling Rates Can model prevalence (proportion) with logistic regression Cannot model incidence in
More informationBinomial Model. Lecture 10: Introduction to Logistic Regression. Logistic Regression. Binomial Distribution. n independent trials
Lecture : Introduction to Logistic Regression Ani Manichaikul amanicha@jhsph.edu 2 May 27 Binomial Model n independent trials (e.g., coin tosses) p = probability of success on each trial (e.g., p =! =
More informationCorrelation and regression
1 Correlation and regression Yongjua Laosiritaworn Introductory on Field Epidemiology 6 July 2015, Thailand Data 2 Illustrative data (Doll, 1955) 3 Scatter plot 4 Doll, 1955 5 6 Correlation coefficient,
More information8 Nominal and Ordinal Logistic Regression
8 Nominal and Ordinal Logistic Regression 8.1 Introduction If the response variable is categorical, with more then two categories, then there are two options for generalized linear models. One relies on
More informationClass Notes: Week 8. Probit versus Logit Link Functions and Count Data
Ronald Heck Class Notes: Week 8 1 Class Notes: Week 8 Probit versus Logit Link Functions and Count Data This week we ll take up a couple of issues. The first is working with a probit link function. While
More informationSociology 362 Data Exercise 6 Logistic Regression 2
Sociology 362 Data Exercise 6 Logistic Regression 2 The questions below refer to the data and output beginning on the next page. Although the raw data are given there, you do not have to do any Stata runs
More informationModel Selection in GLMs. (should be able to implement frequentist GLM analyses!) Today: standard frequentist methods for model selection
Model Selection in GLMs Last class: estimability/identifiability, analysis of deviance, standard errors & confidence intervals (should be able to implement frequentist GLM analyses!) Today: standard frequentist
More information4. MA(2) +drift: y t = µ + ɛ t + θ 1 ɛ t 1 + θ 2 ɛ t 2. Mean: where θ(l) = 1 + θ 1 L + θ 2 L 2. Therefore,
61 4. MA(2) +drift: y t = µ + ɛ t + θ 1 ɛ t 1 + θ 2 ɛ t 2 Mean: y t = µ + θ(l)ɛ t, where θ(l) = 1 + θ 1 L + θ 2 L 2. Therefore, E(y t ) = µ + θ(l)e(ɛ t ) = µ 62 Example: MA(q) Model: y t = ɛ t + θ 1 ɛ
More informationPerson-Time Data. Incidence. Cumulative Incidence: Example. Cumulative Incidence. Person-Time Data. Person-Time Data
Person-Time Data CF Jeff Lin, MD., PhD. Incidence 1. Cumulative incidence (incidence proportion) 2. Incidence density (incidence rate) December 14, 2005 c Jeff Lin, MD., PhD. c Jeff Lin, MD., PhD. Person-Time
More information7/28/15. Review Homework. Overview. Lecture 6: Logistic Regression Analysis
Lecture 6: Logistic Regression Analysis Christopher S. Hollenbeak, PhD Jane R. Schubart, PhD The Outcomes Research Toolbox Review Homework 2 Overview Logistic regression model conceptually Logistic regression
More information11 November 2011 Department of Biostatistics, University of Copengen. 9:15 10:00 Recap of case-control studies. Frequency-matched studies.
Matched and nested case-control studies Bendix Carstensen Steno Diabetes Center, Gentofte, Denmark http://staff.pubhealth.ku.dk/~bxc/ Department of Biostatistics, University of Copengen 11 November 2011
More informationCase-control studies
Matched and nested case-control studies Bendix Carstensen Steno Diabetes Center, Gentofte, Denmark b@bxc.dk http://bendixcarstensen.com Department of Biostatistics, University of Copenhagen, 8 November
More informationStatistical Modelling with Stata: Binary Outcomes
Statistical Modelling with Stata: Binary Outcomes Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester 21/11/2017 Cross-tabulation Exposed Unexposed Total Cases a b a + b Controls
More informationLecture 14: Introduction to Poisson Regression
Lecture 14: Introduction to Poisson Regression Ani Manichaikul amanicha@jhsph.edu 8 May 2007 1 / 52 Overview Modelling counts Contingency tables Poisson regression models 2 / 52 Modelling counts I Why
More informationModelling counts. Lecture 14: Introduction to Poisson Regression. Overview
Modelling counts I Lecture 14: Introduction to Poisson Regression Ani Manichaikul amanicha@jhsph.edu Why count data? Number of traffic accidents per day Mortality counts in a given neighborhood, per week
More information22s:152 Applied Linear Regression. Example: Study on lead levels in children. Ch. 14 (sec. 1) and Ch. 15 (sec. 1 & 4): Logistic Regression
22s:52 Applied Linear Regression Ch. 4 (sec. and Ch. 5 (sec. & 4: Logistic Regression Logistic Regression When the response variable is a binary variable, such as 0 or live or die fail or succeed then
More informationGeneralized logit models for nominal multinomial responses. Local odds ratios
Generalized logit models for nominal multinomial responses Categorical Data Analysis, Summer 2015 1/17 Local odds ratios Y 1 2 3 4 1 π 11 π 12 π 13 π 14 π 1+ X 2 π 21 π 22 π 23 π 24 π 2+ 3 π 31 π 32 π
More informationExample 7b: Generalized Models for Ordinal Longitudinal Data using SAS GLIMMIX, STATA MEOLOGIT, and MPLUS (last proportional odds model only)
CLDP945 Example 7b page 1 Example 7b: Generalized Models for Ordinal Longitudinal Data using SAS GLIMMIX, STATA MEOLOGIT, and MPLUS (last proportional odds model only) This example comes from real data
More informationADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES. Cox s regression analysis Time dependent explanatory variables
ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES Cox s regression analysis Time dependent explanatory variables Henrik Ravn Bandim Health Project, Statens Serum Institut 4 November 2011 1 / 53
More informationLecture 3: Measures of effect: Risk Difference Attributable Fraction Risk Ratio and Odds Ratio
Lecture 3: Measures of effect: Risk Difference Attributable Fraction Risk Ratio and Odds Ratio Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK March 3-5,
More informationLogistic Regression. Building, Interpreting and Assessing the Goodness-of-fit for a logistic regression model
Logistic Regression In previous lectures, we have seen how to use linear regression analysis when the outcome/response/dependent variable is measured on a continuous scale. In this lecture, we will assume
More informationST3241 Categorical Data Analysis I Multicategory Logit Models. Logit Models For Nominal Responses
ST3241 Categorical Data Analysis I Multicategory Logit Models Logit Models For Nominal Responses 1 Models For Nominal Responses Y is nominal with J categories. Let {π 1,, π J } denote the response probabilities
More informationCase of single exogenous (iv) variable (with single or multiple mediators) iv à med à dv. = β 0. iv i. med i + α 1
Mediation Analysis: OLS vs. SUR vs. ISUR vs. 3SLS vs. SEM Note by Hubert Gatignon July 7, 2013, updated November 15, 2013, April 11, 2014, May 21, 2016 and August 10, 2016 In Chap. 11 of Statistical Analysis
More informationOne-stage dose-response meta-analysis
One-stage dose-response meta-analysis Nicola Orsini, Alessio Crippa Biostatistics Team Department of Public Health Sciences Karolinska Institutet http://ki.se/en/phs/biostatistics-team 2017 Nordic and
More informationStat 642, Lecture notes for 04/12/05 96
Stat 642, Lecture notes for 04/12/05 96 Hosmer-Lemeshow Statistic The Hosmer-Lemeshow Statistic is another measure of lack of fit. Hosmer and Lemeshow recommend partitioning the observations into 10 equal
More informationLatent class analysis and finite mixture models with Stata
Latent class analysis and finite mixture models with Stata Isabel Canette Principal Mathematician and Statistician StataCorp LLC 2017 Stata Users Group Meeting Madrid, October 19th, 2017 Introduction Latent
More informationFaculty of Health Sciences. Regression models. Counts, Poisson regression, Lene Theil Skovgaard. Dept. of Biostatistics
Faculty of Health Sciences Regression models Counts, Poisson regression, 27-5-2013 Lene Theil Skovgaard Dept. of Biostatistics 1 / 36 Count outcome PKA & LTS, Sect. 7.2 Poisson regression The Binomial
More informationIntroduction to logistic regression
Introduction to logistic regression Tuan V. Nguyen Professor and NHMRC Senior Research Fellow Garvan Institute of Medical Research University of New South Wales Sydney, Australia What we are going to learn
More informationLOGISTIC REGRESSION Joseph M. Hilbe
LOGISTIC REGRESSION Joseph M. Hilbe Arizona State University Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of
More informationSection IX. Introduction to Logistic Regression for binary outcomes. Poisson regression
Section IX Introduction to Logistic Regression for binary outcomes Poisson regression 0 Sec 9 - Logistic regression In linear regression, we studied models where Y is a continuous variable. What about
More informationUnit 5 Logistic Regression
PubHlth 640 - Spring 2014 5. Logistic Regression Page 1 of 63 Unit 5 Logistic Regression To all the ladies present and some of those absent - Jerzy Neyman What behaviors influence the chances of developing
More informationConsider Table 1 (Note connection to start-stop process).
Discrete-Time Data and Models Discretized duration data are still duration data! Consider Table 1 (Note connection to start-stop process). Table 1: Example of Discrete-Time Event History Data Case Event
More informationUnit 5 Logistic Regression
BIOSTATS 640 - Spring 2017 5. Logistic Regression Page 1 of 65 Unit 5 Logistic Regression To all the ladies present and some of those absent - Jerzy Neyman What behaviors influence the chances of developing
More informationUnit 5 Logistic Regression
BIOSTATS 640 - Spring 2018 5. Logistic Regression Page 1 of 66 Unit 5 Logistic Regression To all the ladies present and some of those absent - Jerzy Neyman What behaviors influence the chances of developing
More informationLinear Regression Models P8111
Linear Regression Models P8111 Lecture 25 Jeff Goldsmith April 26, 2016 1 of 37 Today s Lecture Logistic regression / GLMs Model framework Interpretation Estimation 2 of 37 Linear regression Course started
More informationBeyond GLM and likelihood
Stat 6620: Applied Linear Models Department of Statistics Western Michigan University Statistics curriculum Core knowledge (modeling and estimation) Math stat 1 (probability, distributions, convergence
More informationECON Introductory Econometrics. Lecture 11: Binary dependent variables
ECON4150 - Introductory Econometrics Lecture 11: Binary dependent variables Monique de Haan (moniqued@econ.uio.no) Stock and Watson Chapter 11 Lecture Outline 2 The linear probability model Nonlinear probability
More informationBinary Dependent Variables
Binary Dependent Variables In some cases the outcome of interest rather than one of the right hand side variables - is discrete rather than continuous Binary Dependent Variables In some cases the outcome
More informationOutline. 1 Association in tables. 2 Logistic regression. 3 Extensions to multinomial, ordinal regression. sociology. 4 Multinomial logistic regression
UL Winter School Quantitative Stream Unit B2: Categorical Data Analysis Brendan Halpin Department of Socioy University of Limerick brendan.halpin@ul.ie January 15 16, 2018 1 2 Overview Overview This 3-day
More informationZERO INFLATED POISSON REGRESSION
STAT 6500 ZERO INFLATED POISSON REGRESSION FINAL PROJECT DEC 6 th, 2013 SUN JEON DEPARTMENT OF SOCIOLOGY UTAH STATE UNIVERSITY POISSON REGRESSION REVIEW INTRODUCING - ZERO-INFLATED POISSON REGRESSION SAS
More informationHierarchical Generalized Linear Models. ERSH 8990 REMS Seminar on HLM Last Lecture!
Hierarchical Generalized Linear Models ERSH 8990 REMS Seminar on HLM Last Lecture! Hierarchical Generalized Linear Models Introduction to generalized models Models for binary outcomes Interpreting parameter
More informationMarginal versus conditional effects: does it make a difference? Mireille Schnitzer, PhD Université de Montréal
Marginal versus conditional effects: does it make a difference? Mireille Schnitzer, PhD Université de Montréal Overview In observational and experimental studies, the goal may be to estimate the effect
More informationmultilevel modeling: concepts, applications and interpretations
multilevel modeling: concepts, applications and interpretations lynne c. messer 27 october 2010 warning social and reproductive / perinatal epidemiologist concepts why context matters multilevel models
More informationEPSY 905: Fundamentals of Multivariate Modeling Online Lecture #7
Introduction to Generalized Univariate Models: Models for Binary Outcomes EPSY 905: Fundamentals of Multivariate Modeling Online Lecture #7 EPSY 905: Intro to Generalized In This Lecture A short review
More informationLecture 3.1 Basic Logistic LDA
y Lecture.1 Basic Logistic LDA 0.2.4.6.8 1 Outline Quick Refresher on Ordinary Logistic Regression and Stata Women s employment example Cross-Over Trial LDA Example -100-50 0 50 100 -- Longitudinal Data
More informationMore Statistics tutorial at Logistic Regression and the new:
Logistic Regression and the new: Residual Logistic Regression 1 Outline 1. Logistic Regression 2. Confounding Variables 3. Controlling for Confounding Variables 4. Residual Linear Regression 5. Residual
More informationWhat is Latent Class Analysis. Tarani Chandola
What is Latent Class Analysis Tarani Chandola methods@manchester Many names similar methods (Finite) Mixture Modeling Latent Class Analysis Latent Profile Analysis Latent class analysis (LCA) LCA is a
More informationSingle-level Models for Binary Responses
Single-level Models for Binary Responses Distribution of Binary Data y i response for individual i (i = 1,..., n), coded 0 or 1 Denote by r the number in the sample with y = 1 Mean and variance E(y) =
More informationExercise 7.4 [16 points]
STATISTICS 226, Winter 1997, Homework 5 1 Exercise 7.4 [16 points] a. [3 points] (A: Age, G: Gestation, I: Infant Survival, S: Smoking.) Model G 2 d.f. (AGIS).008 0 0 (AGI, AIS, AGS, GIS).367 1 (AG, AI,
More informationPart IV Statistics in Epidemiology
Part IV Statistics in Epidemiology There are many good statistical textbooks on the market, and we refer readers to some of these textbooks when they need statistical techniques to analyze data or to interpret
More informationMeasures of Fit from AR(p)
Measures of Fit from AR(p) Residual Sum of Squared Errors Residual Mean Squared Error Root MSE (Standard Error of Regression) R-squared R-bar-squared = = T t e t SSR 1 2 ˆ = = T t e t p T s 1 2 2 ˆ 1 1
More informationSTAT 5500/6500 Conditional Logistic Regression for Matched Pairs
STAT 5500/6500 Conditional Logistic Regression for Matched Pairs Motivating Example: The data we will be using comes from a subset of data taken from the Los Angeles Study of the Endometrial Cancer Data
More informationLISA Short Course Series Generalized Linear Models (GLMs) & Categorical Data Analysis (CDA) in R. Liang (Sally) Shan Nov. 4, 2014
LISA Short Course Series Generalized Linear Models (GLMs) & Categorical Data Analysis (CDA) in R Liang (Sally) Shan Nov. 4, 2014 L Laboratory for Interdisciplinary Statistical Analysis LISA helps VT researchers
More informationAppendix A. Numeric example of Dimick Staiger Estimator and comparison between Dimick-Staiger Estimator and Hierarchical Poisson Estimator
Appendix A. Numeric example of Dimick Staiger Estimator and comparison between Dimick-Staiger Estimator and Hierarchical Poisson Estimator As described in the manuscript, the Dimick-Staiger (DS) estimator
More informationChapter 22: Log-linear regression for Poisson counts
Chapter 22: Log-linear regression for Poisson counts Exposure to ionizing radiation is recognized as a cancer risk. In the United States, EPA sets guidelines specifying upper limits on the amount of exposure
More informationToday. HW 1: due February 4, pm. Aspects of Design CD Chapter 2. Continue with Chapter 2 of ELM. In the News:
Today HW 1: due February 4, 11.59 pm. Aspects of Design CD Chapter 2 Continue with Chapter 2 of ELM In the News: STA 2201: Applied Statistics II January 14, 2015 1/35 Recap: data on proportions data: y
More informationChapter 20: Logistic regression for binary response variables
Chapter 20: Logistic regression for binary response variables In 1846, the Donner and Reed families left Illinois for California by covered wagon (87 people, 20 wagons). They attempted a new and untried
More informationCompare Predicted Counts between Groups of Zero Truncated Poisson Regression Model based on Recycled Predictions Method
Compare Predicted Counts between Groups of Zero Truncated Poisson Regression Model based on Recycled Predictions Method Yan Wang 1, Michael Ong 2, Honghu Liu 1,2,3 1 Department of Biostatistics, UCLA School
More informationAssessing the Calibration of Dichotomous Outcome Models with the Calibration Belt
Assessing the Calibration of Dichotomous Outcome Models with the Calibration Belt Giovanni Nattino The Ohio Colleges of Medicine Government Resource Center The Ohio State University Stata Conference -
More informationSTA6938-Logistic Regression Model
Dr. Ying Zhang STA6938-Logistic Regression Model Topic 2-Multiple Logistic Regression Model Outlines:. Model Fitting 2. Statistical Inference for Multiple Logistic Regression Model 3. Interpretation of
More informationBasic Medical Statistics Course
Basic Medical Statistics Course S7 Logistic Regression November 2015 Wilma Heemsbergen w.heemsbergen@nki.nl Logistic Regression The concept of a relationship between the distribution of a dependent variable
More informationGroup Comparisons: Differences in Composition Versus Differences in Models and Effects
Group Comparisons: Differences in Composition Versus Differences in Models and Effects Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised February 15, 2015 Overview.
More informationMAS3301 / MAS8311 Biostatistics Part II: Survival
MAS3301 / MAS8311 Biostatistics Part II: Survival M. Farrow School of Mathematics and Statistics Newcastle University Semester 2, 2009-10 1 13 The Cox proportional hazards model 13.1 Introduction In the
More informationA Journey to Latent Class Analysis (LCA)
A Journey to Latent Class Analysis (LCA) Jeff Pitblado StataCorp LLC 2017 Nordic and Baltic Stata Users Group Meeting Stockholm, Sweden Outline Motivation by: prefix if clause suest command Factor variables
More informationModels for Count and Binary Data. Poisson and Logistic GWR Models. 24/07/2008 GWR Workshop 1
Models for Count and Binary Data Poisson and Logistic GWR Models 24/07/2008 GWR Workshop 1 Outline I: Modelling counts Poisson regression II: Modelling binary events Logistic Regression III: Poisson Regression
More informationGeneralized linear models
Generalized linear models Christopher F Baum ECON 8823: Applied Econometrics Boston College, Spring 2016 Christopher F Baum (BC / DIW) Generalized linear models Boston College, Spring 2016 1 / 1 Introduction
More informationSTAT 7030: Categorical Data Analysis
STAT 7030: Categorical Data Analysis 5. Logistic Regression Peng Zeng Department of Mathematics and Statistics Auburn University Fall 2012 Peng Zeng (Auburn University) STAT 7030 Lecture Notes Fall 2012
More informationUnderstanding the multinomial-poisson transformation
The Stata Journal (2004) 4, Number 3, pp. 265 273 Understanding the multinomial-poisson transformation Paulo Guimarães Medical University of South Carolina Abstract. There is a known connection between
More informationTesting methodology. It often the case that we try to determine the form of the model on the basis of data
Testing methodology It often the case that we try to determine the form of the model on the basis of data The simplest case: we try to determine the set of explanatory variables in the model Testing for
More informationIntroducing Generalized Linear Models: Logistic Regression
Ron Heck, Summer 2012 Seminars 1 Multilevel Regression Models and Their Applications Seminar Introducing Generalized Linear Models: Logistic Regression The generalized linear model (GLM) represents and
More informationespecially with continuous
Handling interactions in Stata, especially with continuous predictors Patrick Royston & Willi Sauerbrei UK Stata Users meeting, London, 13-14 September 2012 Interactions general concepts General idea of
More informationMultinomial Logistic Regression Models
Stat 544, Lecture 19 1 Multinomial Logistic Regression Models Polytomous responses. Logistic regression can be extended to handle responses that are polytomous, i.e. taking r>2 categories. (Note: The word
More informationConfidence Intervals for the Odds Ratio in Logistic Regression with One Binary X
Chapter 864 Confidence Intervals for the Odds Ratio in Logistic Regression with One Binary X Introduction Logistic regression expresses the relationship between a binary response variable and one or more
More informationUNIVERSITY OF TORONTO. Faculty of Arts and Science APRIL 2010 EXAMINATIONS STA 303 H1S / STA 1002 HS. Duration - 3 hours. Aids Allowed: Calculator
UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL 2010 EXAMINATIONS STA 303 H1S / STA 1002 HS Duration - 3 hours Aids Allowed: Calculator LAST NAME: FIRST NAME: STUDENT NUMBER: There are 27 pages
More informationChapter 4 Regression Models
23.August 2010 Chapter 4 Regression Models The target variable T denotes failure time We let x = (x (1),..., x (m) ) represent a vector of available covariates. Also called regression variables, regressors,
More informationRon Heck, Fall Week 8: Introducing Generalized Linear Models: Logistic Regression 1 (Replaces prior revision dated October 20, 2011)
Ron Heck, Fall 2011 1 EDEP 768E: Seminar in Multilevel Modeling rev. January 3, 2012 (see footnote) Week 8: Introducing Generalized Linear Models: Logistic Regression 1 (Replaces prior revision dated October
More informationSCHOOL OF MATHEMATICS AND STATISTICS. Linear and Generalised Linear Models
SCHOOL OF MATHEMATICS AND STATISTICS Linear and Generalised Linear Models Autumn Semester 2017 18 2 hours Attempt all the questions. The allocation of marks is shown in brackets. RESTRICTED OPEN BOOK EXAMINATION
More informationStat/F&W Ecol/Hort 572 Review Points Ané, Spring 2010
1 Linear models Y = Xβ + ɛ with ɛ N (0, σ 2 e) or Y N (Xβ, σ 2 e) where the model matrix X contains the information on predictors and β includes all coefficients (intercept, slope(s) etc.). 1. Number of
More informationAddition to PGLR Chap 6
Arizona State University From the SelectedWorks of Joseph M Hilbe August 27, 216 Addition to PGLR Chap 6 Joseph M Hilbe, Arizona State University Available at: https://works.bepress.com/joseph_hilbe/69/
More informationSections 4.1, 4.2, 4.3
Sections 4.1, 4.2, 4.3 Timothy Hanson Department of Statistics, University of South Carolina Stat 770: Categorical Data Analysis 1/ 32 Chapter 4: Introduction to Generalized Linear Models Generalized linear
More informationSTA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis. 1. Indicate whether each of the following is true (T) or false (F).
STA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis 1. Indicate whether each of the following is true (T) or false (F). (a) (b) (c) (d) (e) In 2 2 tables, statistical independence is equivalent
More informationCategorical and Zero Inflated Growth Models
Categorical and Zero Inflated Growth Models Alan C. Acock* Summer, 2009 *Alan C. Acock, Department of Human Development and Family Sciences, Oregon State University, Corvallis OR 97331 (alan.acock@oregonstate.edu).
More informationPubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH The First Step: SAMPLE SIZE DETERMINATION THE ULTIMATE GOAL The most important, ultimate step of any of clinical research is to do draw inferences;
More informationSection 9c. Propensity scores. Controlling for bias & confounding in observational studies
Section 9c Propensity scores Controlling for bias & confounding in observational studies 1 Logistic regression and propensity scores Consider comparing an outcome in two treatment groups: A vs B. In a
More informationST3241 Categorical Data Analysis I Generalized Linear Models. Introduction and Some Examples
ST3241 Categorical Data Analysis I Generalized Linear Models Introduction and Some Examples 1 Introduction We have discussed methods for analyzing associations in two-way and three-way tables. Now we will
More informationLogistic Regression. Fitting the Logistic Regression Model BAL040-A.A.-10-MAJ
Logistic Regression The goal of a logistic regression analysis is to find the best fitting and most parsimonious, yet biologically reasonable, model to describe the relationship between an outcome (dependent
More informationRegression modeling for categorical data. Part II : Model selection and prediction
Regression modeling for categorical data Part II : Model selection and prediction David Causeur Agrocampus Ouest IRMAR CNRS UMR 6625 http://math.agrocampus-ouest.fr/infogluedeliverlive/membres/david.causeur
More informationECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam
ECLT 5810 Linear Regression and Logistic Regression for Classification Prof. Wai Lam Linear Regression Models Least Squares Input vectors is an attribute / feature / predictor (independent variable) The
More informationDiscrete Choice Modeling
[Part 6] 1/55 0 Introduction 1 Summary 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit 12 Stated Preference
More informationModelling Binary Outcomes 21/11/2017
Modelling Binary Outcomes 21/11/2017 Contents 1 Modelling Binary Outcomes 5 1.1 Cross-tabulation.................................... 5 1.1.1 Measures of Effect............................... 6 1.1.2 Limitations
More information9 Generalized Linear Models
9 Generalized Linear Models The Generalized Linear Model (GLM) is a model which has been built to include a wide range of different models you already know, e.g. ANOVA and multiple linear regression models
More informationβ j = coefficient of x j in the model; β = ( β1, β2,
Regression Modeling of Survival Time Data Why regression models? Groups similar except for the treatment under study use the nonparametric methods discussed earlier. Groups differ in variables (covariates)
More informationLogistic regression. 11 Nov Logistic regression (EPFL) Applied Statistics 11 Nov / 20
Logistic regression 11 Nov 2010 Logistic regression (EPFL) Applied Statistics 11 Nov 2010 1 / 20 Modeling overview Want to capture important features of the relationship between a (set of) variable(s)
More informationConfounding, mediation and colliding
Confounding, mediation and colliding What types of shared covariates does the sibling comparison design control for? Arvid Sjölander and Johan Zetterqvist Causal effects and confounding A common aim of
More information