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SITAR modelling of growth in preterm infants and the effect of antenatal steroids

Neil Lawrence

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The effect of antenatal steroids on preterm infant growth

Antenatal steroids are excellent at reducing poor short term outcomes

Neonatal death by 31%

Respiratory distress syndrome by 44%

Intraventricular haemorrhage by 46%

Possible negative impact of ADHD and mental health in later life

Previous guidance for potential preterm deliveries <39 weeks altered to <34 weeks

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sitar_model <- sitar(

x = ‘natural_log_age’,

y = ‘height’,

id = ‘id’,

data = ‘data_frame’,

df = 12 )

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sitar_model <- sitar(

x = ‘natural_log_age’,

y = ‘height’,

id = ‘id’,

data = ‘data_frame’,

df = 12 )

Log-likelihood: -2941.496

Fixed: s1 + s2 + s3 + s4 + s5 + s6 + s7 + s8 + s9 + s10 + s11 + s12 + a + b + c ~ 1

s1 s2 s3 s4 s5 s6 s7 s8 s9

24.6 35.8 45.1 55.6 61.6 72.8 82.8 94.9 107.9

s10 s11 s12 a b c

128.8 139.9 126.7 33.2 -0.01 0.06

Random effects:

Formula: list(a ~ 1, b ~ 1, c ~ 1)

Level: id

Structure: General positive-definite, Log-Cholesky parametrization

StdDev Corr

a 5.36 a b

b 0.08 0.76

c 0.05 0.74 0.37

Residual 1.43

Population level ‘fixed effects’ – the ‘average’ growth

Distribution of random effects across the population

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sitar_model <- sitar(

x = ‘natural_log_age’,

y = ‘height’,

id = ‘id’,

data = ‘data_frame’,

df = 12 )

ranef(sitar_model)

‘a’

Extract model random effects

‘b’

‘c’

SIZE

TIMING

VELOCITY

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SIZE

TIMING

VELOCITY

Growth outcomes

Treatment and covariates

Antenatal steroids

Maternal height

Paternal height

Gestational age at birth

Base R!

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Girls

Boys

Outcome variable = a : ‘size’

Flextable / officer packages

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Problems:

Explainability!

Defining uncertainty around individual patient random effects

Post hoc estimation of individual random effects without the fitting of a new model