/* EQ-5D U.S. PREFERENCE-WEIGHTED INDEX */


/* Author:  James W. Shaw */
/* Date:  May 20, 2004 */


/* Program was written with Stata/SE 8.0 but is compatible with earlier versions of the application. */


/* The scoring algorithm presented below was taken from:  Shaw JW, Johnson JA, Coons SJ. 
U.S. Valuation of the EQ-5D Health States:  Development and Testing of the D1 Valuation Model. 
Medical Care. Submitted 2004. */
  

/* This program computes the U.S. preference-weighted index score using self-reported EQ-5D data.  
It is presumed that the data set includes the following five variables:

Dimension               Variable Name     Range
--------------------------------------------------
Mobility                MO                1-3
Self-care               SC                1-3
Usual activities        UA                1-3
Pain/discomfort         PD                1-3
Anxiety/depression      AD                1-3
--------------------------------------------------

where a 1 indicates no problems, a 2 indicates moderate problems, and a 3 indicates severe 
problems.  The variables containing responses for the five dimensions must be named as above 
(in capital letters).  Missing values should be left blank (i.e., a '.' should not be substituted 
for a missing value). The index score will not be generated when responses are missing for 1 or                                                                                                               
more of the five dimensions. */
 

/* Generate Dummy Variables for Levels 2 and 3 of Five Dimensions */

gen m1 =0
gen m2 =0
gen s1 =0
gen s2 =0
gen u1 =0
gen u2 =0
gen p1 =0
gen p2 =0
gen a1 =0
gen a2 =0

replace m1 = 1 if MO == 2
replace m2 = 1 if MO == 3
replace s1 = 1 if SC == 2
replace s2 = 1 if SC == 3
replace u1 = 1 if UA == 2
replace u2 = 1 if UA == 3
replace p1 = 1 if PD == 2
replace p2 = 1 if PD == 3
replace a1 = 1 if AD == 2
replace a2 = 1 if AD == 3


/* Generate Interaction Terms (I2, I2-squared, I3, I3-squared) */

gen m0 = 1 if m1 == 0 & m2 == 0
gen s0 = 1 if s1 == 0 & s2 == 0
gen u0 = 1 if u1 == 0 & u2 == 0
gen p0 = 1 if p1 == 0 & p2 == 0
gen a0 = 1 if a1 == 0 & a2 == 0

egen i2 = neqany(m1 s1 u1 p1 a1), values(1)
replace i2 = i2 - 1
replace i2 = 0 if i2<0
gen i22 = i2^2

egen i3 = neqany(m2 s2 u2 p2 a2), values(1)
replace i3 = i3 - 1
replace i3 = 0 if i3<0
gen i32 = i3^2


/* Generate D1 Term */

egen i1 = neqany(m0 s0 u0 p0 a0), values(1)
gen d1 = 4 - i1
replace d1 = 0 if d1<0


/* Generate Raw Index Score */

gen pred = .146016*m1 + .557685*m2 + .1753425*s1 + .4711896*s2 + .1397295*u1 + .3742594*u2 + .1728907*p1 + .5371011*p2 + .156223*a1 + .4501876*a2 + -.1395949*d1 + .0106868*i22  + -.1215579*i3 + -.0147963*i32
gen EQ_index = 1 - pred
replace EQ_index = . if MO == . | SC == . | UA == . | PD == . | AD == . 


/* Drop Variables Generated by Program */

drop m1-pred