Respiratory Physiotherapy on Premature Newborns.

Source: R/mcglm.R

The NewBorn dataset consists of a prospective study to assess the effect of respiratory physiotherapy on the cardiopulmonary function of ventilated preterm newborn infants with birth weight lower than 1500 g. The data set was collected and kindly made available by the nursing team of the Waldemar Monastier hospital, Campo Largo, PR, Brazil. The NewBorn dataset was analysed in Bonat and Jorgensen (2016) as an example of mixed outcomes regression model.

  • Sex - Factor two levels Female and Male.

  • GA - Gestational age (weeks).

  • BW - Birth weight (mm).

  • APGAR1M - APGAR index in the first minute of life.

  • APGAR5M - APGAR index in the fifth minute of life.

  • PRE - Factor, two levels (Premature: YES; NO).

  • HD - Factor, two levels (Hansen's disease, YES; NO).

  • SUR - Factor, two levels (Surfactant, YES; NO).

  • JAU - Factor, two levels (Jaundice, YES; NO).

  • PNE - Factor, two levels (Pneumonia, YES; NO).

  • PDA - Factor, two levels (Persistence of ductus arteriosus, YES; NO).

  • PPI - Factor, two levels (Primary pulmonary infection, YES; NO).

  • OTHERS - Factor, two levels (Other diseases, YES; NO).

  • DAYS - Age (days).

  • AUX - Factor, two levels (Type of respiratory auxiliary, HOOD; OTHERS).

  • RR - Respiratory rate (continuous).

  • HR - Heart rate (continuous).

  • SPO2 - Oxygen saturation (bounded).

  • TREAT - Factor, three levels (Respiratory physiotherapy, Evaluation 1; Evaluation 2; Evaluation 3).

  • NBI - Newborn index.

  • TIME - Days of treatment.

data(NewBorn)

Format

a data.frame with 270 records and 21 variables.

Source

Bonat, W. H. and Jorgensen, B. (2016) Multivariate covariance generalized linear models. Journal of Royal Statistical Society - Series C 65:649--675.

Examples

library(mcglm)
library(Matrix)
data(NewBorn, package="mcglm")
formu <- SPO2 ~ Sex + APGAR1M + APGAR5M + PRE + HD + SUR
Z0 <- mc_id(NewBorn)
fit <- mcglm(linear_pred = c(formu), matrix_pred = list(Z0),
            link = c("logit"), variance = c("binomialP"),
            power_fixed = c(TRUE),
            data = NewBorn,
            control_algorithm = list(verbose = FALSE, tuning = 0.5))
#> Warning: non-integer counts in a binomial glm!
#> Automatic initial values selected. 
summary(fit)
#> Call: SPO2 ~ Sex + APGAR1M + APGAR5M + PRE + HD + SUR
#> <environment: 0x55609314e8f0>
#> 
#> Link function: logit
#> Variance function: binomialP
#> Covariance function: identity
#> Regression:
#>               Estimates  Std.error    Z value     Pr(>|z|)
#> (Intercept)  3.22171596 0.30226814 10.6584703 1.591943e-26
#> SexMale      0.21653136 0.11053825  1.9588818 5.012663e-02
#> APGAR1M      0.09979746 0.03381981  2.9508579 3.168926e-03
#> APGAR5M     -0.10574923 0.05644095 -1.8736261 6.098198e-02
#> PREYES      -0.02757547 0.12642369 -0.2181195 8.273360e-01
#> HDYES        0.32213019 0.16314703  1.9744778 4.832744e-02
#> SURYES      -0.12363785 0.16668188 -0.7417594 4.582331e-01
#> 
#> Dispersion:
#>    Estimates   Std.error  Z value     Pr(>|z|)
#> 1 0.01633439 0.001223725 13.34809 1.215219e-40
#> 
#> Algorithm: chaser
#> Correction: TRUE
#> Number iterations: 15