Computational and Mathematical Methods in Medicine / 2013 / Article / Tab 4

Research Article

Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements

Table 4

Posterior summaries for the effect on using Model 1.

Variables MeanSdMC error%2.5Median%97.5StartSample

Group
 C−3,16814,1720,0043−1,121−3,0177,016100010.000
 D−53,2151,9310,0011−53,131−52,911−50,97100010.000
 L17,31210,1710,0211−21,43116,95123,71100010.000
Age0,0170,0210,0001−0,0150,0210,038100010.000
Sex [male]−0,3120,2950,003−0,773−0,3310,121100010.000
Disease [1]−0,2150,2110,015−0,328−0,2310,174100010.000
Weight−0,13110,01110,001−0,151−0,1417−0,1317100010.000
Comp (yes)0,0870,00950,0020,0650,0810,093100010.000
Test (1)0,1370,1310,021−0,0210,1360,141100010.000
Sps−0,0160,0030,003−0,6171−0,015−0,0139100010.000
Pul−0,01210,0020,0001−0,0729−0,012−0,011100010.000
OSAT−0,1170,0110,0002−0,018−0,0130,011100010.000

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