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Lecture 11 (Chapter 9)

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**Generalized Linear Mixed Models with Random Effects**

The logistic regression model with random intercept Example: 2x2 crossover trial Example: Indonesian Children’s Health Study The Poisson regression model with random intercept Example: seizure data

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**Basic idea: There is natural heterogeneity among subjects. **

Random Effects GLM Basic idea: There is natural heterogeneity among subjects. Systematic part Random part

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**Example: 2x2 crossover trial**

In random effects models, the regression coefficients measure the more direct influence of explanatory variables on the responses for heterogeneous individuals. For example:

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**Example: 2x2 crossover trial**

(Recall) Example: This model states that: 1. each person has his/her own probability of positive response under a placebo (B)

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**Example: 2x2 crossover trial**

This model also states that: 2. a person’s odds of a normal response are multiplied by when taking drug A, regardless of the initial risk Individual odds of normal response when taking drug (x=1) Individual odds of normal response when on placebo (x=0) (“initial risk”)

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In Logistic Models In other words: Marginal model estimates are smaller than random effects model estimates Tests of hypotheses approximately the same in random effects as in marginal

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**Let β be the vector of regression coefficients under a marginal model **

Correspondence between regression parameters in random effects and marginal models Let β be the vector of regression coefficients under a marginal model Let β* be the vector of regression coefficients under a random effects model G is the variance of the random effects Ui ~ N(0,G) Random effects model β* > β G=0 => β* = β Marginal model

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**Estimation of Generalized Linear Mixed Models**

Setting: f(Yij|Ui) in the exponential family Yi1,…,Yini | Ui are independent Ui ~ f(Ui,G) Maximum Likelihood Estimation Ui is a set of unobserved variables which we integrate out of the likelihood

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**Maximum Likelihood Estimation of G and β**

Assume: Ui ~ N(0, G) We can learn about one individual’s coefficients by understanding the variability in coefficients across the population (G)

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**Maximum Likelihood Estimation of G and β (cont’d)**

If Gi is small, then rely on population average coefficients to estimate those for an individuals We weight the cross-sectional information more heavily and we borrow strength across subjects If Gi is large, then rely heavily on the data from each individual to estimate their own coefficients We weight the longitudinal information more heavily since comparisons within a subject are likely to be more precise than comparisons among subjects

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**Maximum Likelihood Estimation**

“Average-away” the random effects (Ui) Use all the data Use an EM algorithm Use numerical integration

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**Example: 2x2 crossover trial**

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**Max. Likelihood. Estimation - Example: 2x2 crossover trial**

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**Max. Likelihood. Estimation - Example: 2x2 crossover trial**

95% of the subjects would fall between ±(2 x 4.9) logit units of the overall mean This range on the logit scale translates into probabilities between 0 and 1, i.e. some people have little change and others have very high hance of a normal reading in the placebo and the treatment groups

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**Max. Likelihood. Estimation - Example: 2x2 crossover trial**

Assuming a constant treatment effect for all persons, the odds of a normal response for a subject are estimated to be = exp(1.9) = 6.7 times higher on the active drug than on the placebo

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*robust SE **model-based SE

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**Assume: constant treatment effect for everybody**

exp(1.9)=6.7 => odds of a normal response for a subject are 6.7 times higher on the active drug than on the placebo exp(0.57)=1.67 => population average odds of a normal response are 1.67 times higher on the active drug than on the placebo

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**Example: 2x2 crossover trial Summary**

Marginal model Random effects model (maximum likelihood) The smaller value from the marginal analysis is consistent with the theoretical inequality:

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**Example: Indonesian study**

Logistic regression with random intercept Relative odds of infection associated with Xerophthalmia are: exp(0.57)=1.7, but this is not stat sig different from 1 β* more similar to β because G is smaller

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**Indonesian Study: Maximum Likelihood Approach**

With a random effects model, we can address the question of how an individual child’s risk for respiratory infection will change if his/her vitamin A status were to change. We assume that each child has a distinct intercept which represents his/her propensity to infection. We account for correlation by including random intercepts, Ui ~ N(0,G).

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**Indonesian Study: Maximum Likelihood Approach (cont’d)**

considerable heterogeneity among children Among children with linear predictor equal to the intercept (-2.2), i.e. children of average age and height, who are female, and who are vitamin A sufficient, about 95% would have a probability of infection between (0.025) and (0.31): Relative odds of infection associated with vitamin A deficiency are exp(0.54)=1.7

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**Indonesian Study: Maximum Likelihood Approach (cont’d)**

The longitudinal age effect on the risk of respiratory infection in Model 2 can be explained by the seasonal trend in Model 3 Because of the small heterogeneity (summarized by G), the estimates of the coefficients obtained under a random effects model are similar to those obtained under a marginal model Ratio of the random effects coefficients and marginal coefficients are close to

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**Poisson model with random intercept: Epileptic seizure example**

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**Poisson model with random intercept: Epileptic seizure example**

2 weeks each 8 weeks Randomly assigned to either placebo or progabide

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Example: Seizure

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**Example: Seizure (cont’d)**

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**Example: Seizure (cont’d)**

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**Example: Seizure (cont’d)**

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**Example: Seizure (cont’d)**

The model does not fit well => Extend the model by including a random slope Ui2

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**Model 1: random intercept only (similar result to conditional likelihood)**

Model 2: random intercept + random slope

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**Poisson-Gaussian Random Effects Models: Epileptic Seizure**

Here, we assume there might be heterogeneity among subjects in the ratio of the expected seizure counts before and after randomization. This degree of heterogeneity can be measured by G22.

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**Poisson-Gaussian Random Effects Models: Epileptic Seizure**

We use maximum likelihood estimation.

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**Poisson-Gaussian Random Effects Models: Epileptic Seizure**

The ratio of seizure counts in the placebo post-to-pre treatment is subject specific: The ratio of seizure counts in the progabide post-to-pre treatment is subject specific:

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**Poisson-Gaussian Random Effects Models: Epileptic Seizure**

Results The estimate of G22 is statistically significant, therefore the data give support for between-subject variability in the ratio of the expected seizure counts before and after randomization. subjects in the placebo group with Ui2=0 have expected seizure rates after treatment, which are estimated to be roughly similar to those before treatment

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**Poisson-Gaussian Random Effects Models: Epileptic Seizure**

Results (cont’d) Among subjects in the progabide group with Ui2=0, the seizure rates are reduced after treatment by about 27%, The treatment seems to have a modest effect: Without patient 207, the evidence for progabide is stronger:

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