\Header{infert}{Infertility after spontaneous and induced abortion}
\keyword{datasets}{infert}
\begin{Usage}
\begin{verbatim}data(infert)\end{verbatim}
\end{Usage}
\begin{Format}
\Tabular{rll}{
1.	& Education	& 0 = 0-5  years \\{}
&		& 1 = 6-11 years \\{}
&		& 2 = 12+  years  \\{}
2.	& age	& age in years of case \\{}
3.	& parity	& count \\{}
4.	& number of prior & 0 = 0 \\{}
& induced abortions & 1 = 1 \\{}
&		& 2 = 2 or more \\{}
5.	& case status& 1 = case \\{}
&		& 0 = control \\{}
6.	& number of prior & 0 = 0 \\{}
& spontaneous abortions & 1 = 1 \\{}
&		& 2 = 2 or more \\{}
7.	& matched set number & 1-83 \\{}
8.	& stratum number & 1-63 \\{}
}\end{Format}
\begin{Description}
This is a matched case-control study dating from before the
availability of conditional logistic regression.\end{Description}
\begin{Note}
One case with two prior spontaneous abortions and two prior induced
abortions is omitted.\end{Note}
\begin{Source}
Trichopoulos et al. (1976) Br. J. of Obst. and Gynaec. vol.83,
pp. 645-650.\end{Source}
\begin{Examples}
\begin{ExampleCode}
data(infert)
model1 <- glm(case ~ spontaneous+induced, data=infert,family=binomial())
summary(model1)
## adjusted for other potential confounders:
summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
                data=infert,family=binomial()))
## Really should be analysed by conditional logistic regression
## which is equivalent to a Cox model :
if(require(survival4)){
  faketime <- rep(42,nrow(infert))
  model3 <- coxph(Surv(faketime,case)~spontaneous+induced+strata(stratum),
                data=infert,method="exact")
  summary(model3)
  detach()# survival4 (conflicts)
}
\end{ExampleCode}
\end{Examples}

