Stata Technical Bulletin
27
We can compare this to the logistic regression analysis using only the complete observations:
. keep if x”=.
. logit y x, or
Logit estimates Log likelihood = -299.1S92S |
Number of obs = LR chi2(l) Prob > chi2 = Pseudo R2 = |
500 92.97 0.0000 | ||
— I odd-ratio Std. Err. |
z |
P>∣z∣ |
[957, Conf. |
— Interval] |
---------+------------------------- x I 2.771684 .3326964 |
8.493 |
0.000 |
2.190638 |
3.506847 |
Note that the mean score estimate above had smaller standard error, reflecting the additional information used in the analysis.
Also, since i is a surrogate for .r, it is not used in the complete case analysis.
Next, we consider a real example of an application of the mean score method to a case-control study of the association
between ectopic pregnancy and sexually transmitted diseases; see Reilly and Pepe (1995) for a full description of the data
. use ectopic
. meanscor y gonn-chlam,first(gonn-sexptn) second(chlam)
meanscore estimates
I |
odd-ratio |
Std. Err. |
z |
P>∣z∣ |
[957. Conf. |
Interval] | |
cons |
I |
.4543184 |
.0987123 |
-3.631 |
0.000 |
.2967666 |
.6955137 |
gonn |
I |
.9495978 |
.2856096 |
-0.172 |
0.863 |
.5266531 |
1.712201 |
contr |
I |
.0943838 |
.0176643 |
-12.612 |
0.000 |
.0654021 |
.1362082 |
sexptn |
I |
2.099286 |
.4938943 |
3.152 |
0.002 |
1.323766 |
3.329139 |
chlam |
I |
2.471606 |
.7808384 |
2.864 |
0.004 |
1.330653 |
4.590858 |
For comparison, an analysis of complete cases only gives
. keep if chlam ~=.
. logit y gonn-chlam, or | |||||
Logit estimates Log likelihood = -169.54627 |
Number of obs = Prob > chi2 = Pseudo R2 = |
327 | |||
— I |
odd-ratio Std. Err. |
z |
P>∣z∣ |
[957. Conf. |
— Interval] |
— — —--— —--+— |
— | ||||
gonn I |
.7445515 .3132037 |
-0.701 |
0.483 |
.3264582 |
1.698095 |
contr I |
.1098308 .0303352 |
-7.997 |
0.000 |
.063918 |
.1887231 |
sexptn I |
1.93898 .7101447 |
1.808 |
0.071 |
.945853 |
3.97487 |
chlam I |
2.47682 .7576623 |
2.965 |
0.003 |
1.359912 |
4.511054 |
References
Reilly, M. 1996. Optimal sampling strategies for two-stage studies. American Journal of Epidemiology 143: 92-100.
Reilly, M. and M. S. Pepe. 1995. A mean score method for missing and auxiliary covariate data in regression models. Biometrika 82: 299-314.
sg157 Predicted values calculated from linear or logistic regression models
Joanne M. Garrett, University of North Carolina, [email protected]
Abstract: The program predcalc for easily calculating predicted values and confidence intervals from linear or logistic regression
model estimates for specified values of the X variables is introduced and illustrated.
Keywords: regression models, predicted values.
Syntax
predcalc yvar, 7yt⅛x(xvarli.st) [ level (#) model linear ]
More intriguing information
1. The name is absent2. Retirement and the Poverty of the Elderly in Portugal
3. Dynamic Explanations of Industry Structure and Performance
4. The name is absent
5. IMMIGRATION AND AGRICULTURAL LABOR POLICIES
6. The name is absent
7. ADJUSTMENT TO GLOBALISATION: A STUDY OF THE FOOTWEAR INDUSTRY IN EUROPE
8. Chebyshev polynomial approximation to approximate partial differential equations
9. A THEORETICAL FRAMEWORK FOR EVALUATING SOCIAL WELFARE EFFECTS OF NEW AGRICULTURAL TECHNOLOGY
10. The name is absent