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It can assess whether a factor predicts more than a single outcome. Path statistics can be used (Chaps. 2 and 3), but, traditionally, multivariate analysis of variance MANOVA) is used for the purpose. Examples are given. Chapter 5 Categorical Data (60 patients) Primary scientific question: does race have an effect on physical strength (the variable race has a categorical rather then linear pattern). The effects on physical strength (scores 0–100) assessed in 60 subjects of different races (hispanics (1), blacks (2), asians (3),and whites (4)), ages (years), and genders (0 = female, 1 = male) are in the left four columns underneath.

15 for statistical significance both the efficacy of the old laxative and patients’ age are significant predictors of the new laxative. The regression equation is as follows y ¼ a þ bx1 þ cx2 y ¼ 0:975 þ 1:890x1 þ 0:305x2 Regression Imputation 31 Using this equation, we use the y-value and x1-value to calculate the missing x2-value. Similarly, the missing y- and x1-values are calculated and imputed. The underneath data file has the imputed values. 005 (see the table on the next page). 32 7 Missing Data Imputation (35 Patients) Multiple Imputations Multiple imputations is probably a better device for missing data imputation than regression imputation.

000 Example 53 The above table shows, that, indeed, both gender and treatment are very significant predictors of the outcome after adjustment for one another. The above figure tries to explain what is going on. If one gender receives few treatments 0 and the other gender receives few treatments 1, then an overall regression line will be close to horizontal, giving rise to the erroneous conclusion that no difference in the treatment efficacy exists between the treatment modalities. This phenomenon is called confounding, and can be dealt with in several ways: (1) subclassification (Statistics on a Pocket Calculator, Part 1, Chap.

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