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Table 4 Statistical analysis for the cohort of 185. Predictors of Scoliosis: Logistic Regression – Univariate Models Results

From: Growth rates and the prevalence and progression of scoliosis in short-statured children on Australian growth hormone treatment programmes

 

B

S.E.

Wald

Sig.

Exp(B)

Age Comm.

.039

.067

.335

.563

1.040

Duration

-.166

.099

2.814

.093

.847

Mg per Kg1

2.544

1.277

3.965

.046

12.728

Mg per Kg2

2.420

1.122

4.656

.031

11.245

Mg per Kg3

.716

2.085

.118

.731

2.046

Mg per Kg4

7.827

4.661

2.819

.093

2507.794

Last PCTL

-.015

.016

.863

.353

.985

Turner syndr.

-1.790

.471

14.430

<.001

.167

First PCTL_Age

-.046

.076

.364

.546

.955

First_PCTL

-.026

.024

1.255

.263

.974

  1. B = Multiple regression co-efficient
  2. S.E = Standard error
  3. Wald (Wald Abraham. Selected papers in statistics and probability. Stanford University Press. New York, Toronto, London. 1955.)
  4. Sig.= Significance
  5. Exp (B) = Risk ratio
  6. Each variable was used in turn to predict the occurrence of scoliosis in a logistic regression model. The size of the first and second doses of HGH (MG per KG1 and 2) and having Turner syndrome were predictive of having scoliosis. Variables for analysis where numbers exceeded 30 –
  7. a) Age of commencement
  8. b) Duration of HGH treatment
  9. c) Dose of HGH in Mg/Kg body weight
  10. d) First percentile height at commencement of HGH treatment (PCTL)
  11. e) Last percentile height at cessation of HGH treatment (PCTL)
  12. f) Presence of Turner syndrome