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Blood, Vol. 110, Issue 7, 2727-2735, October 1, 2007
A network model to predict the risk of death in sickle cell disease
Blood Sebastiani et al.
110: 2727
Supplemental materials for Sebastiani et al, Vol. 110, Issue 7, 2727-2735
Files in this Data Supplement:
- Document 1. Statistical analysis (PDF, 30.9 KB)
- Table S1. Categories used for continuous laboratory variables (PDF, 12.8 KB) -
The categories were defined using, in part, the normal range defined in the nonsickle population, and in part reference values inferred from the sickle cell disease population.19
- Table S2. Summary of the strength of associations in the data from the Cooperative Study of Sickle Cell Disease study (PDF, 15.8 KB) -
Column 2 reports the Bayes factor in logarithmic scale as explained in the technical notes. The third column reports the average Bayes factor in logarithmic scale that was computed by inducing the network of associations in the sets randomly selected from the original dataset during the assessment of the error rate. Although the associations are less strong because of the smaller sample size, the close match between the Bayes factors in column 2 and the average Bayes factors provides strong evidence in favor of the associations. The fourth and fifth columns report the effect of the variable in the rows on the disease severity score measured by the odds ratio for early death and 95% Bayesian credible intervals in round brackets. Names in square brackets describe the category compared to the referent group when the variable has more than two categories: for example 2.67 in column 4, row 2 are the odds for early death of a subject aged between 18 and 40 years, compared to a subject aged below 18 years.
- Table S3. Results of logistic regression model on death (PDF, 12.5 KB) -
The first column is the name of the covariate, the second column reports the levels of these categorical variables as described in Table S1. If a level of a variable is the referent group, the corresponding row is filled with dots. The parameters are estimated using maximum likelihood method, and all the P-values are from the Wald Chi-squared statistic.
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