Contrasting Marginal and Mixed Effects Models Recall: two approaches to handling dependence in Generalized Linear Models:

Transcription

1 Contrasting Marginal and Mixed Effects Models Recall: two approaches to handling dependence in Generalized Linear Models: Marginal models: based on the consequences of dependence on estimating model parameters. Target of inference is the population. Random effect models: based on the sources of dependence. Target of inference is the subject. 1

2 Special case: Linear models Model for mean response: E (Y i ) = X i β Alternative specifications for variance: covariance pattern e.g. AR(1) or Toeplitz; Cov (Y i ) = Σ i 2

3 random effects E (Y i b i ) = X i β + Z i,j b i with E (b i ) = 0, Cov (b i ) = G, whence Σ i = Z i,j GZ i,j + σ2 I ni 3

4 Interpretation of β: in both cases, E ( Y i,j ) = X i,j β = k X i,j,k β k so E ( Y i,j ) But also, with random effects, X i,j,k = β k. E ( Y i,j b i ) = X i,j β + Z i,j b i so, if the k th covariate is not associated with a random effect, E ( Y i,j b i ) X i,j,k = β k. 4

5 That is, β k is the rate of change of the mean response as the k th covariate changes, both on average across subjects (marginally), and for a specific subject with random effects b (conditionally). This might be either a rate of change over time, if the covariate is time (within-subject factor), or a treatment effect, if the covariate is a dummy variable (between-subject factor). 5

6 Generalized Linear Models With link function g( ) and inverse link h( ) = g 1 ( ): the marginal model is or g { E ( Y i,j )} = X i,j β, E ( Y i,j ) = h ( X i,j β ) ; the mixed effects model is or g { E ( Y i,j b i )} = X i,j β + Z i,j b i E ( Y i,j b i ) = h ( X i,j β + Z i,j b i). 6

7 In the mixed effects case, E ( ) { ( )} Y i,j = E E Yi,j b i = and in general = E { h ( X i,j β + Z i,j b i E ( ) ( Y i,j h X i,j β ) for any β. )} h ( X i,j β + Z i,j b i) fb (b i )db i, 7

8 Logistic regression with random intercept: logit { E ( Y i,j b i )} = X i,j β + b i so and E ( Y i,j ) = E = E ( Y i,j b i ) = e 1 + e e 1 + e ) (X i,j β +b i ) (X i,j β +b i ) (X i,j β +b i e 1 + e ( (X i,j β +b i ) (X i,j β +b i ) ) 1 X i,j β +b i 2πσb 2 e 1 2 b2 i /σ2 b db i. 8

9 No closed-form solution, but logit { E ( Y i,j )} X i,j β 1 + k 2 σ 2 b where k = π = and k2 = 0.346, so β β σ 2 b. If σ 2 b = 8, then β β /2 9

10 Example: one covariate, sample of size 13 with β1 = 1.5, β2 = 0.75, g 1,1 = σb 2 = 4; population average is shown in red, and the approximation in blue. y x 10

11 Case study: abnormal ECG in a drug trial. options linesize = 80 pagesize = 21 nodate; data ecg; retain id 0; infile ecg.txt firstobs = 32; input seq r1 r2 count; if seq = 1 then /* Placebo followed by drug */ do i = 1 to count; id = id + 1; trt = 0; y = r1; period = 0; output; trt = 1; y = r2; period = 1; output; end; else /* Drug followed by placebo */ do i = 1 to count; id = id + 1; trt = 1; y = r1; period = 0; output; trt = 0; y = r2; period = 1; output; end; run; 11

12 title1 Marginal Logistic Regression Model ; title2 ; proc genmod descending; class id; model y = trt period / d=bin; repeated subject=id / logor=fullclust; run; title1 Mixed Effects Logistic Regression Model (Random Intercept) ; title2 ; proc nlmixed qpoints=100; /* Initial values from GEE output: */ parms beta1= beta2=.5689 beta3=.2951 g11=1; eta=beta1 + beta2*trt + beta3*period + b; p=exp(eta)/(1 + exp(eta)); model y ~ binary(p); random b ~ normal(0,g11) subject=id; run;

13 SAS output Marginal Logistic Regression Model 1 The GENMOD Procedure Model Information Data Set Distribution Link Function Dependent Variable WORK.ECG Binomial Logit y Number of Observations Read 134 Number of Observations Used 134 Number of Events 42 Number of Trials

14 Marginal Logistic Regression Model 2 Class Levels Values The GENMOD Procedure Class Level Information id

15 Marginal Logistic Regression Model 3 The GENMOD Procedure Response Profile Ordered Total Value y Frequency PROC GENMOD is modeling the probability that y= 1. 14

16 Marginal Logistic Regression Model 4 The GENMOD Procedure Parameter Information Parameter Prm1 Prm2 Prm3 Effect Intercept trt period Criteria For Assessing Goodness Of Fit Criterion DF Value Value/DF Deviance Scaled Deviance Pearson Chi-Square

17 Marginal Logistic Regression Model 5 The GENMOD Procedure Criteria For Assessing Goodness Of Fit Criterion DF Value Value/DF Scaled Pearson X Log Likelihood Algorithm converged. 16

18 Marginal Logistic Regression Model 6 The GENMOD Procedure Analysis Of Initial Parameter Estimates Standard Wald 95% Chi- Parameter DF Estimate Error Confidence Limits Square Pr > ChiSq Intercept trt period Scale NOTE: The scale parameter was held fixed. 17

19 Marginal Logistic Regression Model 7 The GENMOD Procedure GEE Model Information Log Odds Ratio Structure Fully Parameterized Clusters Subject Effect id (67 levels) Number of Clusters 67 Correlation Matrix Dimension 2 Maximum Cluster Size 2 Minimum Cluster Size 2 18

20 Marginal Logistic Regression Model 8 The GENMOD Procedure Log Odds Ratio Parameter Information Parameter Group Alpha1 (1, 2) Algorithm converged. 19

21 Marginal Logistic Regression Model 9 The GENMOD Procedure Analysis Of GEE Parameter Estimates Empirical Standard Error Estimates Standard 95% Confidence Parameter Estimate Error Limits Z Pr > Z Intercept <.0001 trt period Alpha <

22 Mixed Effects Logistic Regression Model (Random Intercept) 10 The NLMIXED Procedure Specifications Data Set Dependent Variable Distribution for Dependent Variable Random Effects Distribution for Random Effects Subject Variable Optimization Technique Integration Method WORK.ECG y Binary b Normal id Dual Quasi-Newton Adaptive Gaussian Quadrature 21

23 Mixed Effects Logistic Regression Model (Random Intercept) 11 The NLMIXED Procedure Dimensions Observations Used 134 Observations Not Used 0 Total Observations 134 Subjects 67 Max Obs Per Subject 2 Parameters 4 Quadrature Points

24 Mixed Effects Logistic Regression Model (Random Intercept) 12 The NLMIXED Procedure Parameters beta1 beta2 beta3 g11 NegLogLike Iteration History Iter Calls NegLogLike Diff MaxGrad Slope

25 Mixed Effects Logistic Regression Model (Random Intercept) 13 The NLMIXED Procedure Iteration History Iter Calls NegLogLike Diff MaxGrad Slope E E-7 24

26 Mixed Effects Logistic Regression Model (Random Intercept) 14 The NLMIXED Procedure NOTE: GCONV convergence criterion satisfied. Fit Statistics -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better)

27 Mixed Effects Logistic Regression Model (Random Intercept) 15 The NLMIXED Procedure Parameter Estimates Standard Parameter Estimate Error DF t Value Pr > t Alpha Lower beta beta beta g

28 Mixed Effects Logistic Regression Model (Random Intercept) 16 The NLMIXED Procedure Parameter Estimates Parameter Upper Gradient beta e-6 beta beta g E-7 27

29 Notes The treatment effect is significant in both analyses, but much larger in the mixed effects model than in the marginal model. The proc nlmixed fit has a very large ĝ 1,1 of 24.4, but with a large standard error and a non-significant t-statistic. But the proc nlmixed output gives AIC = and the independence part of the proc genmod output gives Log Likelihood = , whence AIC = ( 2) ( ) = 169.9, showing that including the variance parameter leads to a much better fit. 28