#### Transcript Panel on Collaboration Discussion Questions

```Introduction to logistic regression
and Generalized Linear Models
Karen Bandeen-Roche, PhD
Department of Biostatistics
Johns Hopkins University
July 14, 2011
Introduction to Statistical
Measurement and Modeling
Data motivation
 Osteoporosis data
 Scientific question: Can we detect osteoporosis earlier
and more safely?
 Some related statistical questions:
 How does the risk of osteoporosis vary as a function of
measures commonly used to screen for osteoporosis?
 Does age confound the relationship of screening
measures with osteoporosis risk?
 Do ultrasound and DPA measurements discriminate
osteoporosis risk independently of each other?
Outline
 Why we need to generalize linear models
 Generalized Linear Model specification
 Systematic, random model components
 Maximum likelihood estimation
 Logistic regression as a special case of GLM
 Systematic model / interpretation
 Inference
 Example
Regression for categorical outcomes

Why not just apply linear regression to categorical Y’s?
 Linear model (A1) will often be unreasonable.
 Assumption of equal variances (A3) will nearly always be
unreasonable.
 Assumption of normality will never be reasonable
Introduction:
Regression for binary outcomes

Yi = 1{event occurs for sampling unit i}
= 1 if the event occurs
= 0 otherwise.

pi = probability that the event occurs for sampling unit i
:= Pr{Yi = 1}
 Begin by generalizing random model (A5):
 Probability mass function: Bernoulli
Pr{Yi = 1} = pi; Pr{Yi = 0} = 1-pi
all other yi occur with 0 probability
fYi(y) : Pr{Yiy}  piy(1
pi)1y.
Binary regression
 By assuming Bernoulli: (A3) is definitely not reasonable
 Var(Yi ) = pi(1-pi)
 Variance is not constant: rather a function of the mean
 Systematic model
 Goal remains to describe E[Yi|xi]
 Expectation of Bernoulli Yi = pi
 To achieve a reasonable linear model (A1): describe some
function of E[Yi|xi] as a linear function of covariates
 g(E[Yi|xi]) = xi’β
 Some common g: log, log{p/(1-p)}, probit
General framework:
Generalized Linear Models
 Random model
 Y~a density or mass function, fY, not necessarily normal
 Technical aside: fY within the “exponential family”
 Systematic model
 g(E[Yi|xi]) = xi’β = ηi
 “g” = “link function”; “xi’β” = “linear predictor”
 Reference: Nelder JA, Wedderburn RWM, Generalized
Types of Generalized Linear Models
Model
function)
Linear
Logistic
Log-linear
Proportional
hazards
Response
Distribution
Regression
Coef Interp
Continuous
Gaussian
Change in
ave(Y) per unit
change in X
Binary
Binomial
Log odds ratio
Times to
events/counts
Poisson
Log relative
rate
Times to
events
Semiparametric
Log hazard
Estimation
 Estimation:
 maximizes L(β,a;y,X) =
n
f (y ;μ(x ,β),a)
i 1
Yi
i
i
 General method: Maximum likelihood (Fisher)
 Given {Y1,...,Yn} distributed with joint density or mass
function fY(y;θ), a likelihood function L(θ;y) is any
function (of θ) that is proportional to fY(y;θ).
 If sampling is random, {Y1,...,Yn} are statistically
independent, and L(θ;y) α product of individual f.
Maximum likelihood
 The maximum likelihood estimate (MLE),  ,
maximizes L(θ;y):
L(θ)
Θ
Θ
 Under broad assumptions MLEs are asymptotically
 Unbiased (consistent)
 Efficient (most precise / lowest variance)
Logistic regression
 Yi binary with pi = Pr{Yi = 1}
 Example: Yi = 1{person i diagnosed with heart disease}
 Simple logistic regression (1 covariate)
 Random Model: Bernoulli / Binomial
 Systematic Model: log{pi/(1- pi)}= β0 + β1xi
 log odds; logit(pi)
 Parameter interpretation
 β0 = log(heart disease odds) in subpopulation with x=0
 β1 = log{px+1/(1-px+1)}- log{px/(1-px)}
Logistic regression
Interpretation notes
 β1 = log{px+1/(1-px+1)}- log{px/(1-px)}
=
log
 exp(β1)log
=
px1/(1px1)
px/(1px)
px1/(1px1)
px/(1px)
= odds ratio for association of prevalent heart disease
with each (say) one year increment in age
= factor by which odds of heart disease increases /
decreases with each 1-year cohort of age
Multiple logistic regression
 Systematic Model: log{pi/(1- pi)}= β0 + β1xi1 + … + βpxip
 Parameter interpretation
 β0 = log(heart disease odds) in subpopulation with all x=0
 βj = difference in log outcome odds comparing
subpopulations who differ by 1 on xj, and whose values on
all other covariates are the same
 “Adjusting for,” “Controlling for” the other covariates
 One can define variables contrasting outcome odds
differences between groups, nonlinear relationships,
interactions, etc., just as in linear regression
Logistic regression - prediction
 Translation from ηi to pi
 log{pi/(1- pi)}= β0 + β1xi1 + … + βpxip
 Then pi
e
β0β1xi1...βpxip
1e
β0β1xi1...βpxip

e
ηi
1e
ηi
= logistic
function of ηi
 Graph of pi versus ηi has a sigmoid shape
GLMs - Inference
 The negative inverse Hessian matrix of the log likelihood

 SE( ) obtained as square root of the jth diagonal entry
 Typically, substituting
 for β
 “Wald” inference applies the paradigm from Lecture 2

 

0 j is asympotically ~ N(0,1) under H0: βj= β0j
Z= j
SE (  j )
 Z provides a test statistic for H0: βj= β0j versus HA: βj≠ β0j

 ± z(1-α/2) SE{ } =(L,U) is a (1-α)x100% CI for βj
 {exp(L),exp(U)} is a (1-α)x100% CI for exp(βj)
GLMs: “Global” Inference
 Analog: F-testing in linear regression
 The only difference: log likelihoods replace SS
 Hypothesis to be tested is H0: βj1=...=βjk = 0
 Fit model excluding xj1,...,xjpj: Save -2 log likelihood = Ls
 Fit “full” (or larger) model adding xj1,...,xjpj to smaller
model. Save -2 log likelihood = LL
 Test statistic S = Ls - LL
 Distribution under null hypothesis: χ2pj
 Define rejection region based on this distribution
 Compute S
 Reject or not as S is in rejection region or not
GLMs: “Global” Inference
 Many programs refer to “deviance” rather than -2 log
likelihood
 This quantity equals the difference in -2 log likelihoods
between ones fitted model and a “saturated model”
 Deviance measures “fit”
 Differences in deviances can be substituted for differences
in -2 log likelihood in the method given on the previous
page
 Likelihood ratio tests have appealing optimality
properties
Outline: A few more topics
 Model checking: Residuals, influence points
 ML can be written as an iteratively reweighted least
squares algorithm
 Predictive accuracy
 Framework generalizes easily
Main Points
 Generalized linear modeling provides a flexible
regression framework for a variety of response types
 Continuous, categorical measurement scales
 Probability distributions tailored to the outcome
 Systematic model to accommodate
 Measurement range, interpretation
 Logistic regression
 Binary responses (yes, no)
 Bernoulli / binomial distribution
 Regression coefficients as log odds ratios for association
between predictors and outcomes
Main Points
 Generalized linear modeling accommodates
description, inference, adjustment with the same
flexibility as linear modeling
 Inference
 “Wald”- statistical tests and confidence intervals via
parameter estimator standardization
 “Likelihood ratio” / “global” – via comparison of log
likelihoods from nested models
```