Table 2

Bayesian beta regression results

Pat. design surrogateFamily (discussion)Family (voting)Leg. assigned surrogateStat. prediction rulePhysician
Intercept 9.45 (9.14 to 9.74) 9.41 (9.04 to 9.71) 8.69 (8.04 to 9.28) 7.92 (7 to 8.75) 3.06 (2.34 to 3.81) 3.05 (2.32 to 3.84)
Perspective (sur.) −0.79 (−1.26 to −0.34) 0.1 (−0.22 to 0.38)−0.14 (−0.77 to 0.49) −1.1 (−2.02 to −0.15) 0.65 (−0.2 to 1.54)0.36 (−0.45 to 1.23)
Sex (female) 0.28 (0.07 to 0.47) 0.14 (−0.15 to 0.43)0.31 (−0.27 to 0.83)0.41 (−0.41 to 1.16)0.23 (−0.52 to 1.01)0.59 (−0.29 to 1.53)
Age0 (−0.3 to 0.3)0 (−0.37 to 0.3)0.02 (−0.63 to 0.6)0.03 (−0.89 to 0.85)0.02 (−0.69 to 0.79)0.04 (−0.68 to 0.86)
Education0.01 (−0.3 to 0.35)−0.05 (−0.48 to 0.31)−0.06 (−0.77 to 0.63)−0.25 (−1.31 to 0.69)−0.57 (−1.19 to 0.09)−0.37 (−1.07 to 0.41)
Employed (y)0.09 (−0.14 to 0.32)−0.12 (−0.44 to 0.16)−0.15 (−0.7 to 0.37)−0.55 (−1.3 to 0.26) −0.67 (−1.1 to −0.18) −0.25 (−0.85 to 0.32)
Income0.08 (−0.2 to 0.37)0.02 (−0.34 to 0.35)−0.05 (−0.76 to 0.56)0.06 (−0.88 to 0.92)0.05 (−0.72 to 0.86)0.08 (−0.74 to 0.88)
Household size−0.04 (−0.36 to 0.28)0.03 (−0.31 to 0.35)0.04 (−0.61 to 0.64)0.28 (−0.6 to 1.12)0.21 (−0.54 to 1.03)−0.06 (−0.81 to 0.74)
City size0.05 (−0.24 to 0.36)−0.07 (−0.45 to 0.32)−0.05 (−0.74 to 0.6)0.2 (−0.67 to 1.11)0.02 (−0.76 to 0.82)0.22 (−0.68 to 1.07)
Country (Germany)−0.14 (−0.48 to 0.16)−0.35 (−0.75 to 0.06)−0.22 (−0.82 to 0.4)0.05 (−0.76 to 0.9)0.03 (−0.61 to 0.82) −0.84 (−1.31 to −0.33)
Living will (y)−0.02 (−0.44 to 0.37)−0.42 (−1.04 to 0.14)−0.69 (−1.67 to 0.2)−0.07 (−1.28 to 98)−0.65 (−1.37 to 0.16)−0.59 (−1.33 to 0.29)
Design. sur. (y)0.21 (−0.08 to 0.46)0.18 (−0.15 to 0.48)0.41 (−0.24 to 0.96) 1.34 (0.69 to 1.91) 0.78 (−0.19 to 1.86)−0.33 (−1.08 to 0.48)
Organ donor (y)0.12 (−0.19 to 0.41)0.03 (−0.38 to 0.4)−0.08 (−0.87 to 0.58)−0.57 (−1.66 to 0.46)−0.27 (−1.05 to 0.54)0.23 (−0.75 to 1.16)
  • Intercepts reflect the mean ratings in the reference levels (see below) on the response scale of 1–10. ‘Age’ and ‘household size’ were treated as continuous predictors and were mean centred. ‘Education’, ‘income’ and ‘city size’ were treated as ordinal predictors and implemented with sum contrasts, such that the models’ intercepts show the grand mean across all levels of the ordinal predictors (ie, for ‘education’ with three levels, the contrast weights −1, 0, 1 were used, for ‘income’ with four levels, the contrast weights −3, –1, 1, 3 were used and for ‘city size’ with five levels, the contrast weights −2, –1, 0, 1, 2 were used). All other predictors were treated as binary predictors with the indicated effect levels; coefficients denote changes from the intercept when a predictor’s value is changed from 0 to 1 (ie, for the binary predictors, a change from the reference category to the effect category, indicated in parentheses after the predictor variable’s name). Values in brackets are 95% HDIs. Coefficients with HDIs excluding 0 are printed in bold. The coding of the variables is specified in table 1.