Comparing effects across nested logistic regression models

Transcription

1 Comparing effects across nested logistic regression models CAPS Methods Core Quantitative Working Group Seminar September 3, 011 Steve Gregorich SEGregorich 1 Sept 3, 011

2 SEGregorich Sept 3, 011

3 Comparing parameter estimates across two nested linear models Covariate-adjusted (Full) model yɺ = a + x bɺ + c bɺ + eɺ i F i x.f i c.f i.f Unadjusted (Restricted) model yɺ = a + xɺ + eɺ i R ib x. R i.r What is the effect of adjustment for c?. Compare b ɺ x.f to b ɺ x.r, either formally or just 'eyeball' the difference SEGregorich 3 Sept 3, 011

4 Comparing parameter estimates across two nested logistic models Covariate-adjusted (Full) model logit(y i = 1 x i, c i ) = a F + x i b x.f + c i b c.f Unadjusted (Restricted) model logit(y i = 1 x i ) = a R + x i b x.r. Here, comparing b x.f to b x.r is more complex. To understand why, we'll look at the binary outcome threshold model SEGregorich 4 Sept 3, 011

5 Binary outcome regression represented as a threshold model.y* is an unobserved (latent) continuous outcome variable representing the propensity of outcome occurrence * yi = a + xib + ei ɺ ɺ ɺ, where e i ~ Logistic(0,π /3) for logistic or N(0,1) for probit ɺ. Usually, the relationship between continuous y* and binary y is defined as if y i * >0 then y i = 1; else y i = 0 Given e i ~ Logistic(0,π /3), model parameters for correctly specified models ɺ are equivalent across linear model of y*, and logistic model of y SEGregorich 5 Sept 3, 011

6 Three identifying assumptions of logistic regression model. conditional mean of e i = 0 ɺ. Var( e i x) = π /3 ɺ. threshold value for y* is 0 (usually): if y* > 0 then y = 1; else y = 0 SEGregorich 6 Sept 3, 011

7 Comparing linear and logistic regression Basics of modeled variation outcome variance linear regression (y) σ y is observed logistic regression σ y * is model-dependent Effects of added X variables on modeled variation outcome variance linear regression (y) σ y unchanged residual variance σ e ɺ is model-dependent σ e ɺ is fixed residual variance σ e ɺ decreased logistic regression (y*) σ y * increased σ e ɺ unchanged. Adding explanatory vars. to a logistic model, increases implied variance of y*. Essentially, y* is rescaled.. When y* is rescaled, model parameters are also rescaled. Same for models of y SEGregorich 7 Sept 3, 011

8 Comparing parameters across nested logistic regression models logit(y i = 1 x i, c i ) = a F + x i b x.f + c i b c.f (Full model) logit(y i = 1 x i ) = a R + x i b x.r (Restricted model). b 1.F and b 1.R may differ because of. confounding (expectation: b x.f < b x.r ). negative confounding (expectation: b x.f > b x.r ). rescaling (expectation: b x.f > b x.r ). a combination (expectation:??) Parameter rescaling is almost universally unknown/ignored except in specific contexts. testing mediation. generalized linear mixed models SEGregorich 8 Sept 3, 011

9 A simulated example of faux negative confounding Simulated data. A single sample with N=500,000. x and c are bivariate normal with the following sample statistics (exactly). x = c = 0. σ = 1, σ = 4 x c. r xc = 0. Next, I used x and c values to generate a continuous y * variate as y = x + c + e, (i.e., both regression parameters equaled unity) ɺ * i i i i where the e i ~ Logistic(0,π /3) ɺ. Finally, I created a binary version of y * as y = 1 if y * > 0; y = 0 otherwise SEGregorich 9 Sept 3, 011

10 A simulated example of faux negative confounding Results of linear models regressing y * onto x and c Full model Adjusted b Restricted model Unadjusted b x modeled, c excluded c modeled, x excluded Results of logistic models regressing y onto x and c Full model Adjusted b Restricted model Unadjusted b x modeled, c excluded c modeled, x excluded SEGregorich 10 Sept 3, 011

11 A simulated example of faux negative confounding Explanation for results on previous slide In this simplified example, x and c are orthogonal, so the implied variance of y* equals Full model σ y*.f = σ xbx.f + σ c bc.f + π 3 = 8.9 Restricted model including x σ y*.r = σ xbx.r + π 3 = 3.66 Scaling of the outcome and parameter estimates is not equivalent across models SEGregorich 11 Sept 3, 011

12 One attempted solution in the literature. In the context of testing mediation, Winship and Mare (1984) and MacKinnon & Dwyer (1993) suggested a rescaling of model parameters based upon the σ and * * y. This is known as y-standardization. However, it does not work very well.f σ to allow comparison of, e.g., b x.r and b x.f y. For the previous example, the rescaled value of b x.r equals.r rescaled b x.r = 0.61 σ σ y y * *.F.R = = = 0.9, not There have been other proposed solutions that I have not studied (reportedly they don't work well, either) SEGregorich 1 Sept 3, 011

13 Karlson, Holm, & Breen (KHB) (in press). KHB argue that the scaling is a factor of the error standard deviation, σ e, not the standard deviation of y*. Of course y* and σ e are unobserved, in practice, ɺ but given our simulated data, we can take a look. For the Full model, σ e.f = π 3 = 3.9 ɺ. For the Restricted model, σ e.r = σ c + π 3 = 7.9 ɺ. Therefore, the KHB-suggested rescaled value equals rescaled b x.r = 0.61 ɺ σ σ ɺ e.r e.f 7.9 = π 3 = = 0.91, not 1.00 SEGregorich 13 Sept 3, 011

14 Comparing parameter rescaling methods From the earlier simulated example Full model Restricted model ˆb Restricted σ -rescaled * y Restricted σ -rescaled ɺ x e Regardless of these results, KHB suggest a method to rescale parameter estimates from binary outcome models that appears to work. SEGregorich 14 Sept 3, 011

15 KHB method. Here, C i refers to the vector of covariates in the Full model. Replace all covariates, C i, in the Full logisitc regression model with residuals from regression of C i on x, R i. Name this the KHB model. The KHB model provides an estimate of the unadjusted effect of x on y that is on the same scale as parameters from the Full model. Clever The R i are uncorrelated with x The KHB model obtains an unadjusted estimate of the x effect. (the KHB model obtains Type 1 estimates of the x effect). model-dependent σ * and σ y e are equivalent across the KHB and Full models ɺ The KHB model obtains unadjusted parameter estimates for x that are on the scale of the Full model.. Method easily extends to accommodate any number of x and c variables SEGregorich 15 Sept 3, 011

16 KHB method What about binary covariates? KHB suggest using the linear probability model (LPM) to generate residuals of the C i Then fit the KHB model in the usual way SEGregorich 16 Sept 3, 011

17 KHB method LPM. Fit a linear regression model of the binary outcome. Conditional expectation of y given x, E(y i x i ) = Pr(y i =1 x) = a + x i b x + c i b c. Binary y does not affect interpretation the parameters, compared to continuous y. For a unit change in x, the expected change in the probability that y=1 is b x, holding any control variables constant.. Because the model is linear, a unit change in x always results in the same change in probability the model is linear in the probability.. In general practice, there are problems with the linear probability model:. heteroskedasticity (the variance of y x depends on x). residuals cannot be normally distributed. predicted probabilities outside [0,1]. functional form Even so, the LPM could meet the needs of the KHB model I.e., to estimate the unadjusted effect of x on the scale of the Full model SEGregorich 17 Sept 3, 011

18 Simulation study: Population model x (σ = 1) 0.5 c1 (σ =.5) 1.0 r xc c (σ =.5) c3 (σ =.5) y; y* c4 (σ =.5) Unadjusted effects of x ( b R ) as a function of r xc : ɺ. r xc = 0.50; b R = = 1.00 ɺ. r xc = 0 ; b R = = 0.50 ɺ. r xc = 0.15; b R = = 0.5 ɺ SEGregorich 18 Sept 3, 011

19 Simulation Details. N=15, 50, 500, R=1000. x ~ N(0, 1). c1 - c4 ~N(0, 0.5); or B(0.50). conditions: norm./bin. c; variance = 0.5. b x = bx = 0.5; b c = b c = 1.0 ɺ ɺ. r xc = 0.5; 0; conditions: pos., no, and neg. confounding. r cc = 0 y i * = x i c1 i + c i +c3 i +c4 i + e i, where e i ~ Logistic(0,π /3) ɺ ɺ if y i * >0 then y i = 1; else y i = 0. y* ~ N(0, ). y ~ B(0.50) dependent on r xc : ranges from approximately 4.0 to 5.0 SEGregorich 19 Sept 3, 011

20 Simulation results: N=1000. R=1000 replicate samples Continuous x and c: linear reg: y* logistic reg: y KHB: y (a) (b) (c) (d) (e) (f) (g) (h) r xc ˆ σ b e R b (b) (c) R b ɺ π 3 R se σ b ɺɺ R covg Continuous x and binary c: linear reg: y* logistic reg: y KHB: y (a) (b) (c) (d) (e) (f) (g) (h) r xc ˆ σ b e R b (b) (c) R b ɺ π 3 R se σ b ɺɺ R covg SEGregorich 0 Sept 3, 011

21 Some implications about naïve point estimates of b R If you naïvely compare b F to b R, you might draw incorrect conclusions Results for continuous x and c r xc b F b R naïve b R KHB naïve true ɺɺ * 0.5 under-estimating the degree of positive confounding suggesting negative confounding when none exists * over-estimating the degree of negative confounding Simulations were simplistic. models with multiple covariates may include those that are positively, negatively, and un-confounded with x SEGregorich 1 Sept 3, 011

22 More Tests of differences between adjusted and rescaled unadjusted effects Normally I don't care about this (except in the context of testing mediation) KHB present a test based upon Sobel. Can accommodate multiple x and multiple c variables Known problems with Sobel, Aroian, etc SEGregorich Sept 3, 011

23 Conclusions. KHB model is simple to implement. Quality of KHB model point estimates Seems to do a good job of obtaining rescaled unadjusted point estimates Use of LPM for binary covariates seemed to work well I considered other scenarios, Varied the distribution of binary c and y Lognormal distribution of X KHB (011) report upon a fairly extensive simulation study. Quality of KHB model standard errors/coverage Coverage of rescaled unadjusted x effects was just OK in my limited simulation. If one wants to emphasize any tests of rescaled unadjusted effects, the bootstrap should be considered SEGregorich 3 Sept 3, 011

24 Resources KHB papers (contact Kristian Karlson: 1. Kristian Bernt Karlson, Anders Holm, and Richard Breen. (March, 09, 011). Comparing Regression Coefficients Between Models using Logit and Probit: A New Method. Draft manuscript.. Kohler, U., Karlson, K.B., Holm, A. (in press). Comparing coefficients of nested nonlinear probability models. The Stata Journal. 3. Breen, R., Karlson, K.B., Holm, A. (April 11, 011). Total, Direct, and Indirect Effects in Logit Models. Abstract available at 4. Karlson, K.B. and Holm, A. (011). Decomposing primary and secondary effects: A new decomposition method. Research in Social Stratification and Mobility, 9, KHB Stata ado SEGregorich 4 Sept 3, 011