bacteria: Presence of Bacteria after Drug Treatments

Description Usage Format Details Source References Examples

Description

Tests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia.

Usage

1

Format

This data frame has 220 rows and the following columns:

y

presence or absence: a factor with levels n and y.

ap

active/placebo: a factor with levels a and p.

hilo

hi/low compliance: a factor with levels hi amd lo.

week

numeric: week of test.

ID

subject ID: a factor.

trt

a factor with levels placebo, drug and drug+, a re-coding of ap and hilo.

Details

Dr A. Leach tested the effects of a drug on 50 children with a history of otitis media in the Northern Territory of Australia. The children were randomized to the drug or the a placebo, and also to receive active encouragement to comply with taking the drug.

The presence of H. influenzae was checked at weeks 0, 2, 4, 6 and 11: 30 of the checks were missing and are not included in this data frame.

Source

Dr Amanda Leach via Mr James McBroom.

References

Menzies School of Health Research 1999–2000 Annual Report. p.20. http://www.menzies.edu.au/icms_docs/172302_2000_Annual_report.pdf.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

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contrasts(bacteria$trt) <- structure(contr.sdif(3),
     dimnames = list(NULL, c("drug", "encourage")))
## fixed effects analyses
summary(glm(y ~ trt * week, binomial, data = bacteria))
summary(glm(y ~ trt + week, binomial, data = bacteria))
summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))

# conditional random-effects analysis
library(survival)
bacteria$Time <- rep(1, nrow(bacteria))
coxph(Surv(Time, unclass(y)) ~ week + strata(ID),
      data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID),
      data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID),
      data = bacteria, method = "exact")

# PQL glmm analysis
library(nlme)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                family = binomial, data = bacteria))

Example output

Call:
glm(formula = y ~ trt * week, family = binomial, data = bacteria)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2144   0.4245   0.5373   0.6750   1.0697  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)        1.97548    0.30053   6.573 4.92e-11 ***
trtdrug           -0.99848    0.69490  -1.437  0.15075    
trtencourage       0.83865    0.73482   1.141  0.25374    
week              -0.11814    0.04460  -2.649  0.00807 ** 
trtdrug:week      -0.01722    0.10570  -0.163  0.87061    
trtencourage:week -0.07043    0.10964  -0.642  0.52060    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 217.38  on 219  degrees of freedom
Residual deviance: 203.12  on 214  degrees of freedom
AIC: 215.12

Number of Fisher Scoring iterations: 4


Call:
glm(formula = y ~ trt + week, family = binomial, data = bacteria)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2899   0.3885   0.5400   0.7027   1.1077  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)   1.96018    0.29705   6.599 4.15e-11 ***
trtdrug      -1.10667    0.42519  -2.603  0.00925 ** 
trtencourage  0.45502    0.42766   1.064  0.28735    
week         -0.11577    0.04414  -2.623  0.00872 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 217.38  on 219  degrees of freedom
Residual deviance: 203.81  on 216  degrees of freedom
AIC: 211.81

Number of Fisher Scoring iterations: 4


Call:
glm(formula = y ~ trt + I(week > 2), family = binomial, data = bacteria)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4043   0.3381   0.5754   0.6237   1.0051  

Coefficients:
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)       2.2479     0.3560   6.315 2.71e-10 ***
trtdrug          -1.1187     0.4288  -2.609  0.00909 ** 
trtencourage      0.4815     0.4330   1.112  0.26614    
I(week > 2)TRUE  -1.2949     0.4104  -3.155  0.00160 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 217.38  on 219  degrees of freedom
Residual deviance: 199.18  on 216  degrees of freedom
AIC: 207.18

Number of Fisher Scoring iterations: 5

Call:
coxph(formula = Surv(Time, unclass(y)) ~ week + strata(ID), data = bacteria, 
    method = "exact")

         coef exp(coef) se(coef)      z       p
week -0.16256   0.84996  0.05472 -2.971 0.00297

Likelihood ratio test=9.85  on 1 df, p=0.001696
n= 220, number of events= 177 
Call:
coxph(formula = Surv(Time, unclass(y)) ~ factor(week) + strata(ID), 
    data = bacteria, method = "exact")

                  coef exp(coef) se(coef)      z      p
factor(week)2   0.1983    1.2193   0.7241  0.274 0.7842
factor(week)4  -1.4206    0.2416   0.6665 -2.131 0.0331
factor(week)6  -1.6615    0.1899   0.6825 -2.434 0.0149
factor(week)11 -1.6752    0.1873   0.6780 -2.471 0.0135

Likelihood ratio test=15.45  on 4 df, p=0.003854
n= 220, number of events= 177 
Call:
coxph(formula = Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID), 
    data = bacteria, method = "exact")

                   coef exp(coef) se(coef)      z        p
I(week > 2)TRUE -1.6701    0.1882   0.4817 -3.467 0.000527

Likelihood ratio test=15.15  on 1 df, p=9.927e-05
n= 220, number of events= 177 
iteration 1
iteration 2
iteration 3
iteration 4
iteration 5
iteration 6
Linear mixed-effects model fit by maximum likelihood
 Data: bacteria 
  AIC BIC logLik
   NA  NA     NA

Random effects:
 Formula: ~1 | ID
        (Intercept)  Residual
StdDev:    1.410637 0.7800511

Variance function:
 Structure: fixed weights
 Formula: ~invwt 
Fixed effects: y ~ trt + I(week > 2) 
                     Value Std.Error  DF   t-value p-value
(Intercept)      2.7447864 0.3784193 169  7.253294  0.0000
trtdrug         -1.2473553 0.6440635  47 -1.936696  0.0588
trtencourage     0.4930279 0.6699339  47  0.735935  0.4654
I(week > 2)TRUE -1.6072570 0.3583379 169 -4.485311  0.0000
 Correlation: 
                (Intr) trtdrg trtncr
trtdrug          0.009              
trtencourage     0.036 -0.518       
I(week > 2)TRUE -0.710  0.047 -0.046

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-5.1985361  0.1572336  0.3513075  0.4949482  1.7448845 

Number of Observations: 220
Number of Groups: 50 

MASS documentation built on May 3, 2021, 5:08 p.m.

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