Predictor

∂ U_{B}  U_{A}/∂ x_{j}

SE

95%CI

x_{j}

∂ U_{B}  U_{A}/∂ x_{j}

SE

95%CI

x_{j}



Lives saved

Lifeyears 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

 ^Dollar values expressed in AUD100,000s.

^{†}Reference category is 'workingage adults'. First, second and fourth dummies denote 'young children', 'young adults' and 'olderage retirees', respectively. Here, ∂ U_{B}  U_{A}/∂ x_{j} is for discrete change from reference category to agegroup denoted by relevant dummy variable.

^{‡}Effect(B – A) gives the incremental effectiveness of profile B compared to profile A defined in terms of terms of lives saved for the 'livessaved' model and lifeyears saved for the 'lifeyears saved' model.

^{~} For dichotomous variables, ∂ U_{B}  U_{A}/∂ x_{j} is for discrete change in dummy variable from 0 to 1.