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Table 6 Marginal effects for population average models

From: Is the value of a life or life-year saved context specific? Further evidence from a discrete choice experiment

Predictor

∂ UB - UA/∂ xj

SE

95%CI

xj

∂ UB - UA/∂ xj

SE

95%CI

xj

 

Lives saved

Life-years saved

Cure(B – A)~

-0.2118

0.028

(-0.27,-0.16)

0

-0.2082

0.026

(-0.26,-0.16)

0

AgeGrp1(B – A)†

0.3222

0.037

(0.25, 0.39)

0

0.1862

0.036

(0.12, 0.26)

0

AgeGrp2(B – A)†

0.1484

0.034

(0.08, 0.22)

0

0.0750

0.033

(0.01, 0.14)

0

AgeGrp4(B – A)†

-0.0952

0.028

(-0.15,-0.04)

0

0.0047

0.032

(-0.06,0.07)

0

Evidence(B – A)~

0.1714

0.023

(0.13, 0.22)

0

0.1643

0.023

(0.12, 0.21)

0

Fault(B – A)~

-0.1455

0.024

(-0.19,-0.10)

0

-0.1640

0.026

(-0.21,-0.11)

0

$Private(B – A)^

-0.0014

0.001

(-0.00,-0.00)

0

-0.0019

0.001

(-0.00,-0.00)

0

Effect(B – A)‡

0.0085

0.001

(0.01, 0.01)

0

0.0002

0.000

(0.00, 0.00)

0

$Cost(B – A)^

-0.0015

0.000

(-0.00,-0.00)

0

-0.0014

0.000

(-0.00,-0.00)

0

HlthCard*Q~

-0.0114

0.005

(-0.02,-0.00)

0

-0.0113

0.005

(-0.02,-0.00)

0

(SIEFA_Econ*Q)/1000

0.0173

0.006

(0.01, 0.03)

5.4

0.0198

0.006

(0.01, 0.03)

5.4

  1. ^Dollar values expressed in AUD100,000s.
  2. †Reference category is 'working-age adults'. First, second and fourth dummies denote 'young children', 'young adults' and 'older-age retirees', respectively. Here, ∂ UB - UA/∂ xj is for discrete change from reference category to age-group denoted by relevant dummy variable.
  3. ‡Effect(B – A) gives the incremental effectiveness of profile B compared to profile A defined in terms of terms of lives saved for the 'lives-saved' model and life-years saved for the 'life-years saved' model.
  4. ~ For dichotomous variables, ∂ UB - UA/∂ xj is for discrete change in dummy variable from 0 to 1.