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Lecture 18 Ordinal and Polytomous Logistic Regression BMTRY 701 Biostatistical Methods II.

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1 Lecture 18 Ordinal and Polytomous Logistic Regression BMTRY 701 Biostatistical Methods II

2 Categorical Outcomes  Logistic regression is appropriate for binary outcomes  What about other kinds of categorical data? >2 categories ordinal data  Standard logistic is not applicable unless you ‘threshold’ the date or collapse categories  BMTRY 711: Analysis of Categorical Data  This is just an overview

3 Ordinal Logistic Regression  Ordinal Dependent Variable Teaching experience SES (high, middle, low) Degree of Agreement Ability level (e.g. literacy, reading) Severity of disease/outcome Severity of toxicity  Context is important  Example: attitudes towards smoking

4 Proportional Odds Model  One of several possible regression models for the analysis of ordinal data, and also the most common.  Model predicts the ln(odds) of being in category j or beyond.  Simplifying assumption: “proportional odds” Effect of covariate assumed to be invariant across splits Example: 4 categories  0 vs 1,2,3  0,1 vs 2,3  0,1,2 vs 3 Assumes that each of these comparisons yields the same odds ratio

5 Motivating Example: YTS  The South Carolina Youth Tobacco Survey (SC YTS) is part of the National Youth Tobacco Survey program sponsored by the Centers for Disease Control and Prevention. The YTS is an annual school-based survey designed to evaluate youth-related smoking practices, including initiation and prevalence, cessation, attitudes towards smoking, media influences, and more. The SC YTS is coordinated by the SC Department of Health and Environmental Control and has been administered yearly since 2005. Data for this report are based on years 2005-2007. The SC YTS uses a two-stage sample cluster design to select a representative sample of public middle (grades 6-8) and high school (grades 9-12) students.

6 Ordinal Outcomes. tab cr44 “do you think | smoking | cigarettes | makes young | people look | cool or fit | in?” | Freq. Percent Cum. ---------------+----------------------------------- definitely yes | 460 6.07 6.07 probably yes | 818 10.79 16.86 probably not | 1,329 17.53 34.38 definitely not | 4,975 65.62 100.00 ---------------+----------------------------------- Total | 7,582 100.00. “do you think | young people | risk harming | themselves if | they smoke | from 1 - 5 | ciga | Freq. Percent Cum. ---------------+----------------------------------- definitely yes | 5,387 70.98 70.98 probably yes | 1,283 16.91 87.89 probably not | 360 4.74 92.63 definitely not | 559 7.37 100.00 ---------------+----------------------------------- Total | 7,589 100.00

7 What factors are related to these attitudes?  Gender?  Grade?  Race?  parental education (surrogate for SES)?  year? (2005, 200, 2007)  have tried cigarettes?  school performance?  smoker in the home?

8 Tabulation of gender vs. look cool “do you think | smoking | cigarettes | makes young | people look | cool or fit | gender in?” | 0 1 | Total ---------------+----------------------+---------- definitely yes | 278 177 | 455 probably yes | 446 364 | 810 probably not | 692 628 | 1,320 definitely not | 2,158 2,797 | 4,955 ---------------+----------------------+---------- Total | 3,574 3,966 | 7,540

9 Possible “breaks” OR = 1.81 malefemale def yes 278177 else32963789 OR = 1.59 malefemale yes724541 no28503425 OR = 1.57 malefemale else14161169 def no21582797

10 Proportional Odds Assumption  How to implement this?  Model the probability of ‘cumulative’ logits  Instead of  Here, we have

11 The (simple) ordinal logistic model Warning! different packages parameterize it different ways! Stata codes it differently than SAS and R. Notice how this differs from logistic regression: there is a ‘level’ specific intercept. But, there is just ONE log odds ratio describing the association between x and y.

12 Example. ologit lookcool gender Iteration 0: log likelihood = -7465.0108 Iteration 1: log likelihood = -7418.1251 Iteration 2: log likelihood = -7418.0256 Ordered logistic regression Number of obs = 7540 LR chi2(1) = 93.97 Prob > chi2 = 0.0000 Log likelihood = -7418.0256 Pseudo R2 = 0.0063 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender |.4605529.0476442 9.67 0.000.367172.5539338 -------------+---------------------------------------------------------------- /cut1 | -2.525572.0529346 -2.629322 -2.421823 /cut2 | -1.375663.0380258 -1.450193 -1.301134 /cut3 | -.4159722.0336987 -.4820204 -.349924 ------------------------------------------------------------------------------

13 R estimation  Different parameterization  Makes you think about what the model is doing!

14 > library(Design) > oreg <- lrm(lookcool ~ gender, data=data) > oreg Logistic Regression Model lrm(formula = lookcool ~ gender, data = data) Frequencies of Responses 1 2 3 4 455 810 1320 4955 Frequencies of Missing Values Due to Each Variable lookcool gender 196 50 Obs Max Deriv Model L.R. d.f. P C Dxy 7540 2e-12 93.97 1 0 0.552 0.104 Gamma Tau-a R2 Brier 0.206 0.054 0.014 0.056 Coef S.E. Wald Z P y>=2 2.5256 0.05293 47.71 0 y>=3 1.3757 0.03803 36.18 0 y>=4 0.4160 0.03370 12.34 0 gender 0.4606 0.04764 9.67 0

15 MLR. ologit lookcool gender evertried smokerhome grade school_perf Iteration 0: log likelihood = -2123.9232 Iteration 1: log likelihood = -2052.0964 Iteration 2: log likelihood = -2051.2897 Iteration 3: log likelihood = -2051.2895 Ordered logistic regression Number of obs = 2125 LR chi2(5) = 145.27 Prob > chi2 = 0.0000 Log likelihood = -2051.2895 Pseudo R2 = 0.0342 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender |.214247.09052 2.37 0.018.0368309.391663 evertried | 1.048804.0999844 10.49 0.000.852838 1.24477 smokerhome |.1350945.0931715 1.45 0.147 -.0475182.3177072 grade |.0646475.0253649 2.55 0.011.0149332.1143618 school_per~e | -.0407656.0591738 -0.69 0.491 -.1567441.0752128 -------------+---------------------------------------------------------------- /cut1 | -.9447746.301191 -1.535098 -.3544511 /cut2 |.3491469.2940768 -.2272331.9255269 /cut3 | 1.386131.2950286.8078857 1.964377 ------------------------------------------------------------------------------

16 It is a pretty strong assumption  How can we check?  Simple check as shown in 2x2 table.  Continuous variables: harder need to consider the model no direct ‘tabular’ comparison  multiple regression: does it hold for all?  Tricky! It needs to make sense and you need to do some ‘model checking’ for all of your variables  Worthwhile to check each individually.

17 There is another approach  There is a test of proportionality.  Implemented easily in Stata with an add-on package: omodel Ho: proportionality holds Ha: proportionality is violated  Why? violation would require more parameters and would be a larger model  What does small p-value imply? but be careful of sample size! large sample sizes will make it hard to ‘adhere’ to proportionality assumption

18 Estimation in Stata. omodel logit lookcool gender Iteration 0: log likelihood = -7465.0108 Iteration 1: log likelihood = -7418.1251 Iteration 2: log likelihood = -7418.0256 Ordered logit estimates Number of obs = 7540 LR chi2(1) = 93.97 Prob > chi2 = 0.0000 Log likelihood = -7418.0256 Pseudo R2 = 0.0063 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender |.4605529.0476442 9.67 0.000.367172.5539338 -------------+---------------------------------------------------------------- _cut1 | -2.525572.0529346 (Ancillary parameters) _cut2 | -1.375663.0380258 _cut3 | -.4159722.0336987 ------------------------------------------------------------------------------ Approximate likelihood-ratio test of proportionality of odds across response categories: chi2(2) = 2.43 Prob > chi2 = 0.2964

19 . omodel logit lookcool grade Iteration 0: log likelihood = -7425.0617 Iteration 1: log likelihood = -7424.7193 Iteration 2: log likelihood = -7424.7193 Ordered logit estimates Number of obs = 7505 LR chi2(1) = 0.68 Prob > chi2 = 0.4079 Log likelihood = -7424.7193 Pseudo R2 = 0.0000 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- grade | -.0106001.0128062 -0.83 0.408 -.0356997.0144995 -------------+---------------------------------------------------------------- _cut1 | -2.784359.0678301 (Ancillary parameters) _cut2 | -1.640955.0567613 _cut3 | -.6923403.0534291 ------------------------------------------------------------------------------ Approximate likelihood-ratio test of proportionality of odds across response categories: chi2(2) = 22.31 Prob > chi2 = 0.0000

20 What would the ORs be?  Generate three separate binary outcome variables from the ordinal variable lookcool1v234 lookcool12v34 lookcool123v4  Estimate the odds ratio for each binary outcome

21 Stata Code gen lookcool1v234=1 if lookcool==2 | lookcool==3 | lookcool==4 replace lookcool1v234=0 if lookcool==1 gen lookcool12v34=1 if lookcool==3 | lookcool==4 replace lookcool12v34=0 if lookcool==1 | lookcool==2 gen lookcool123v4=1 if lookcool==4 replace lookcool123v4=0 if lookcool==2 | lookcool==3 | lookcool==1 logit lookcool1v234 grade logit lookcool12v34 grade logit lookcool123v4 grade

22 Results  For a one grade difference (range = 6 – 12) lookcool1v234 vs. grade: OR = 1.002 (0.93) lookcool12vs34 vs. grade: OR = 1.04 (p=0.03) lookcool123v4 vs. grade: OR = 0.98 (p=0.11)

23 Another approach: Polytomous Logistic Regression  Polytomous (aka Polychotomous) Logistic Regression  Fits the regression model with all contrasts.  Can be used as an inferential model  Or, can be used to estimate odds ratio to see if they look ‘ordered”  Model is different though

24 . mlogit lookcool gender Iteration 0: log likelihood = -7465.0108 Iteration 1: log likelihood = -7417.1379 Iteration 2: log likelihood = -7416.8737 Iteration 3: log likelihood = -7416.8737 Multinomial logistic regression Number of obs = 7540 LR chi2(3) = 96.27 Prob > chi2 = 0.0000 Log likelihood = -7416.8737 Pseudo R2 = 0.0064 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- definitely~s | gender | -.7108369.1003382 -7.08 0.000 -.9074962 -.5141776 _cons | -2.049316.0637222 -32.16 0.000 -2.174209 -1.924423 -------------+---------------------------------------------------------------- probably yes | gender | -.4625306.0762255 -6.07 0.000 -.6119298 -.3131314 _cons | -1.576618.0520148 -30.31 0.000 -1.678565 -1.474671 -------------+---------------------------------------------------------------- probably not | gender | -.3564113.0621157 -5.74 0.000 -.4781559 -.2346668 _cons | -1.137351.0436861 -26.03 0.000 -1.222974 -1.051728 ------------------------------------------------------------------------------ (lookcool==definitely not is the base outcome)

25 Interpretation  For gender, notice the ordered nature of the odds ratio  Suggests that it may be appropriate to use an ordinal model  This model is more general, less restrictive  but, sort of a mess to interpret

26 . mlogit lookcool grade Iteration 0: log likelihood = -7425.0617 Iteration 1: log likelihood = -7414.6932 Iteration 2: log likelihood = -7414.6755 Multinomial logistic regression Number of obs = 7505 LR chi2(3) = 20.77 Prob > chi2 = 0.0001 Log likelihood = -7414.6755 Pseudo R2 = 0.0014 ------------------------------------------------------------------------------ lookcool | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- definitely~s | grade |.0051085.0264996 0.19 0.847 -.0468299.0570468 _cons | -2.407127.1089328 -22.10 0.000 -2.620632 -2.193623 -------------+---------------------------------------------------------------- probably yes | grade | -.0390659.0207178 -1.89 0.059 -.0796721.0015402 _cons | -1.672094.0825905 -20.25 0.000 -1.833968 -1.510219 -------------+---------------------------------------------------------------- probably not | grade |.0627357.0166685 3.76 0.000.030066.0954055 _cons | -1.562533.0709374 -22.03 0.000 -1.701568 -1.423499 ------------------------------------------------------------------------------ (lookcool==definitely not is the base outcome)

27 In R?  mlogit library  requires a data transformation step

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