glow11m: GLOW11M data

In aplore3: Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd Ed., 2013)

Description

glow11m dataset.

Usage

glow11m

Format

A data.frame with 238 rows and 16 variables: the covariate are the same as those from glow500 with the addition of

pair

Pair Identification Code (1-119)

Source

Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (2013) Applied Logistic Regression, 3rd ed., New York: Wiley

Examples

head(glow11m, n = 10)
summary(glow11m)

## Table 7.2 p. 252
library(survival)
mod7.2 <- clogit(as.numeric(fracture) ~ height + weight + bmi +
                 priorfrac + premeno + momfrac + armassist + raterisk +
                 strata(pair), data = glow11m)
summary(mod7.2)

Example output

   pair sub_id site_id phy_id age height weight      bmi priorfrac premeno
1     1    302       5    288  56    158  101.2 40.53838        No      No
2     1    460       1     35  56    165  113.4 41.65289       Yes      No
3     2    184       1     29  56    168   70.8 25.08504        No      No
4     2    450       5    296  56    155   62.6 26.05619        No      No
5     3     49       2     77  56    170   96.2 33.28720        No      No
6     3    455       3    175  56    162   81.6 31.09282        No      No
7     4    347       3    143  56    160   58.5 22.85156        No      No
8     4    492       6    313  56    162   72.6 27.66347        No      No
9     5    327       3    149  57    158   62.6 25.07611        No      No
10    5    386       4    281  57    155   61.2 25.47346        No      No
   momfrac armassist smoke raterisk fracscore fracture
1       No       Yes    No     Less         2       No
2       No       Yes    No     Same         3      Yes
3       No        No    No     Less         0       No
4       No        No    No     Less         0      Yes
5       No       Yes    No     Same         2       No
6       No       Yes    No  Greater         2      Yes
7       No        No    No  Greater         0       No
8      Yes        No    No     Same         1      Yes
9       No        No    No  Greater         0       No
10      No        No    No  Greater         0      Yes
      pair            sub_id         site_id          phy_id     
 Min.   :  1.00   Min.   :  2.0   Min.   :1.000   Min.   :  1.0  
 1st Qu.: 30.25   1st Qu.:178.5   1st Qu.:2.000   1st Qu.: 65.0  
 Median : 60.00   Median :375.0   Median :3.000   Median :186.5  
 Mean   : 60.00   Mean   :311.0   Mean   :3.538   Mean   :185.6  
 3rd Qu.: 89.75   3rd Qu.:439.8   3rd Qu.:5.000   3rd Qu.:296.0  
 Max.   :119.00   Max.   :500.0   Max.   :6.000   Max.   :325.0  
      age            height          weight            bmi        priorfrac
 Min.   :56.00   Min.   :134.0   Min.   : 39.90   Min.   :15.02   No :163  
 1st Qu.:64.25   1st Qu.:157.0   1st Qu.: 59.23   1st Qu.:23.04   Yes: 75  
 Median :71.00   Median :160.0   Median : 68.00   Median :26.32            
 Mean   :71.19   Mean   :160.8   Mean   : 70.97   Mean   :27.41            
 3rd Qu.:77.75   3rd Qu.:165.0   3rd Qu.: 79.40   3rd Qu.:30.95            
 Max.   :89.00   Max.   :178.0   Max.   :124.70   Max.   :44.04            
 premeno   momfrac   armassist smoke        raterisk    fracscore     
 No :195   No :198   No :137   No :224   Less   :72   Min.   : 0.000  
 Yes: 43   Yes: 40   Yes:101   Yes: 14   Same   :89   1st Qu.: 2.000  
                                         Greater:77   Median : 4.500  
                                                      Mean   : 4.437  
                                                      3rd Qu.: 6.000  
                                                      Max.   :11.000  
 fracture 
 No :119  
 Yes:119  
          
          
          
          
Call:
coxph(formula = Surv(rep(1, 238L), as.numeric(fracture)) ~ height + 
    weight + bmi + priorfrac + premeno + momfrac + armassist + 
    raterisk + strata(pair), data = glow11m, method = "exact")

  n= 238, number of events= 119 

                    coef exp(coef) se(coef)      z Pr(>|z|)  
height           0.06327   1.06531  0.12205  0.518   0.6042  
weight          -0.15423   0.85707  0.13104 -1.177   0.2392  
bmi              0.38651   1.47183  0.34170  1.131   0.2580  
priorfracYes     0.69351   2.00072  0.35377  1.960   0.0500 *
premenoYes       0.21800   1.24359  0.55232  0.395   0.6931  
momfracYes       0.72541   2.06558  0.43262  1.677   0.0936 .
armassistYes     0.81779   2.26549  0.38241  2.139   0.0325 *
rateriskSame     0.15157   1.16366  0.34117  0.444   0.6569  
rateriskGreater  0.58880   1.80182  0.42556  1.384   0.1665  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                exp(coef) exp(-coef) lower .95 upper .95
height             1.0653     0.9387    0.8387     1.353
weight             0.8571     1.1668    0.6629     1.108
bmi                1.4718     0.6794    0.7534     2.876
priorfracYes       2.0007     0.4998    1.0001     4.002
premenoYes         1.2436     0.8041    0.4213     3.671
momfracYes         2.0656     0.4841    0.8847     4.823
armassistYes       2.2655     0.4414    1.0707     4.794
rateriskSame       1.1637     0.8594    0.5962     2.271
rateriskGreater    1.8018     0.5550    0.7825     4.149

Rsquare= 0.111   (max possible= 0.5 )
Likelihood ratio test= 27.91  on 9 df,   p=0.000989
Wald test            = 19.78  on 9 df,   p=0.01931
Score (logrank) test = 24.66  on 9 df,   p=0.003369

aplore3 documentation built on May 2, 2019, 8:24 a.m.