bacteria                package:MASS                R Documentation

_P_r_e_s_e_n_c_e _o_f _B_a_c_t_e_r_i_a _a_f_t_e_r _D_r_u_g _T_r_e_a_t_m_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Tests of the presence of the bacteria _H. influenzae_ in children
     with otitis media in the Northern Territory of Australia.

_U_s_a_g_e:

     bacteria

_F_o_r_m_a_t:

     This data frame has 220 rows and the following columns:

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

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

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

     _w_e_e_k numeric: week of test.

     _I_D subject ID: a factor.

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

_D_e_t_a_i_l_s:

     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.

_S_o_u_r_c_e:

     Menzies School of Health Research 1999-2000 Annual Report pp.
     18-21 (<URL:
     http://www.menzies.edu.au/publications/anreps/MSHR00.pdf>).

_R_e_f_e_r_e_n_c_e_s:

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

_E_x_a_m_p_l_e_s:

     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))

