bpdrds: Bronchopulmonary dysplasia and respiratory distress syndrome...

In wbonat/mglm4twin: Multivariate Generalized linear Models for Twin Data

Bronchopulmonary dysplasia and respiratory distress syndrome on preterm infants

Description

We use the dataset analysed by Feng et al. (2009) regarding bronchopulmonary dysplasia (BPD) and respiratory distress syndrome (RDS) on preterm infants. Both diseases are lung related and expected to have a genetic component. The dataset consists of 200 twin-pairs being 137 DZ and 63 MZ. Additionally, we considered the covariates: birth weight (BW), gestation age (GA) and gender (female and male).

• Twin - Code of twin pair.

• gender - Twin age gender (male and female).

• GA - Gestation age.

• BW - Birth weight.

• RDS - Respiratory distress syndrome (binary).

• BPD - Bronchopulmonary dysplasia (binary).

• Group - Twin zygosity (DZ - dizygotic; MZ - monozygotic).

• Twin_pair - Code of twin within the pair (1 and 2).

Usage

data(bmi)

Format

a data.frame with 400 records and 8 variables.

Source

Feng, R., Zhou, G., Zhang, M. and Zhang, H. (2009) Analysis of twin data using sas. Biometrics, 65, 584–589.

Bonat, W. H. and Hjelmborg, J. v. B. (2020) Multivariate Generalized Linear Models for Twin and Family data. to appear.

Examples

require(mglm4twin)
data(bpdrds, package="mglm4twin")
form_BPD <- BPD ~ BW + GA + gender + Group*Twin_pair
form_RDS <- RDS ~ BW + GA + gender + Group*Twin_pair
AE <- mt_twin(N_DZ = 137, N_MZ = 63, n_resp = 2, model = "AE")
fitAE <- mglm4twin(linear_pred = c(form_BPD, form_RDS), matrix_pred = AE,
                   link = c("logit","logit"),
                   variance = c("binomialP","binomialP"), data = bpdrds)

wbonat/mglm4twin documentation built on Oct. 14, 2023, 9:37 p.m.