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* File      HALCyon cognitive syntax_Aberdeen1936.do
* BY        Rebecca Hardy, MRC Unit for Lifelong Health and Ageing at UCL
* FOR       HALCyon cross-cohort collaboration (funded by the New Dynamics of Ageing (RES-353-25-0001))
* DATASET   Aberdeen Birth Cohort 1936 (ABC1936)
* Date Created    07/04/2010
* PURPOSE         Recodes and derives cognitive variables for use in cross-cohort analyses
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#delimit ;
set more off ;

*To obtain age and sex variables, please first run HALCyon PC covariate syntax_Aberdeen1936.do


/*Creating break between newly recoded cognitive variables and variables in existing dataset with a blank variable */
gen cogrecodes_below=. ;

/*********************************************************************************/

/*need to generate age at waves 2 and 3 - already done for wave 1*/
gen double w2_date_st=dofC(w2_date);
gen double w3_date_st=dofC(w3_date);

label variable w2_date_st "Wave 2 collection date (stata format)" ;
label variable w3_date_st "Wave 3 collection date (stata format)" ;
/*Calculate age in days at wave 2/3 (subtracting dob_st as it is a negative number (because it is pre=1960)) */
gen aged_w2=w2_date_st-dob_st;
gen aged_w3=w3_date_st-dob_st;  
/*dividing by 365 for estimation of age in years */
gen age_w2=aged_w2/365 ;
gen age_w3=aged_w3/365 ;

label variable aged_w2 "Age(in days) at wave 2" ;
label variable age_w2 "Age(y) at wave 2" ;
label variable aged_w3 "Age(in days) at wave 3" ;
label variable age_w3 "Age(y) at wave 3" ;

/*Crystalilized cognitive ability*/
/*NART*/
gen nart_w1=nartcor ;
label variable nart_w1 "NART number correct at wave 1" ;
gen nart_w2=ncor2 ;
label variable nart_w2 "NART number correct at wave 2"; 
gen nart_w3=ncor3 ;
label variable nart_w3 "NART number correct at wave 3" ;
/*range 0-50 - delete scores above 50*/
/*note: there is also a score of 1 which you might want to exclude?*/
replace nart_w3=. if nart_w3>50 ;

/*standardise NART to whole sample*/
egen nartz_w1=std(nart_w1);
label variable nartz_w1 "standardised NART at wave 1" ;
egen nartz_w2=std(nart_w2) ;
label variable nartz_w2 "standardised NART at wave 2" ;
egen nartz_w3=std(nart_w3) ;
label variable nartz_w3 "standardised NART at wave 3" ;

/*standardise NART for men and women seprately*/
egen nartzm_w1=std(nart_w1) if sex==1;
egen nartzf_w1=std(nart_w1) if sex==2;
label variable nartzm_w1 "standardised NART for men at wave 1" ;
label variable nartzf_w1 "standardised NART  for women at wave 1" ;
egen nartzm_w2=std(nart_w2) if sex==1;
egen nartzf_w2=std(nart_w2) if sex==2;
label variable nartzm_w2 "standardised NART for men at wave 2" ;
label variable nartzf_w2 "standardised NART  for women at wave 2" ;
egen nartzm_w3=std(nart_w3) if sex==1;
egen nartzf_w3=std(nart_w3) if sex==2;
label variable nartzm_w3 "standardised NART for men at wave 3" ;
label variable nartzf_w3 "standardised NART  for women at wave 3" ;
/*combine in one variable*/
gen nartzs_w1=. ;
replace nartzs_w1=nartzm_w1 if sex==1;
replace nartzs_w1=nartzf_w1 if sex==2;
label variable nartzs_w1 "sex-standardised NART at wave 1" ;
gen nartzs_w2=. ;
replace nartzs_w2=nartzm_w2 if sex==1;
replace nartzs_w2=nartzf_w2 if sex==2;
label variable nartzs_w2 "sex-standardised NART at wave 2" ;
gen nartzs_w3=. ;
replace nartzs_w3=nartzm_w3 if sex==1;
replace nartzs_w3=nartzf_w3 if sex==2;
label variable nartzs_w3 "sex-standardised NART at wave 3" ;

/*age and sex-standardised NART - standardised residuals from regression on age and sex*/
xi: regress nart_w1 age_w1 i.sex ;
predict nartzage_w1, rstandard ;
label variable nartzage_w1 "age and sex-standardised NART at wave 1" ;
xi: regress nart_w2 age_w2 i.sex ;
predict nartzage_w2, rstandard ;
label variable nartzage_w2 "age and sex-standardised NART at wave 2" ;
xi: regress nart_w3 age_w3 i.sex ;
predict nartzage_w3, rstandard ;
label variable nartzage_w3 "age and sex-standardised NART at wave 3" ;

/*Raven's Progressive Matrices*/
/*range 0-60*/
gen ravens_w1=ravncor ;
label variable ravens_w1 "Raven's Matrices number correct at wave 1" ;
gen ravens_w2=ravcor2 ;
label variable ravens_w2 "Raven's Matrices number correct at wave 2" ;
gen ravens_w3=ravcor3 ;
label variable ravens_w3 "Raven's Matrices number correct at wave 3" ;

/*standardise raven's*/
egen ravensz_w1=std(ravens_w1) ;
label variable ravensz_w1 "standardised Raven's Matrices score at wave 1" ;
egen ravensz_w2=std(ravens_w2) ;
label variable ravensz_w2 "standardised Raven's Matrices score at wave 2" ;
egen ravensz_w3=std(ravens_w3) ;
label variable ravensz_w3 "standardised Raven's Matrices score at wave 3" ;

/*standardise Raven's Matrices score for men and women seprately*/
egen ravenszm_w1=std(ravens_w1) if sex==1;
egen ravenszf_w1=std(ravens_w1) if sex==2;
label variable ravenszm_w1 "standardised Raven's Matrices score for men at wave 1" ;
label variable ravenszf_w1 "standardised Raven's Matrices score for women at wave 1" ;
egen ravenszm_w2=std(ravens_w2) if sex==1;
egen ravenszf_w2=std(ravens_w2) if sex==2;
label variable ravenszm_w2 "standardised Raven's Matrices score for men at wave 2" ;
label variable ravenszf_w2 "standardised Raven's Matrices score for women at wave 2" ;
egen ravenszm_w3=std(ravens_w3) if sex==1;
egen ravenszf_w3=std(ravens_w3) if sex==2;
label variable ravenszm_w3 "standardised Raven's Matrices score for men at wave 3" ;
label variable ravenszf_w3 "standardised Raven's Matrices score for women at wave 3" ;
/*combine in one variable*/
gen ravenszs_w1=. ;
replace ravenszs_w1=ravenszm_w1 if sex==1;
replace ravenszs_w1=ravenszf_w1 if sex==2;
label variable ravenszs_w1 "sex-standardised Raven's Matrices score at wave 1" ;
gen ravenszs_w2=.;
replace ravenszs_w2=ravenszm_w2 if sex==1;
replace ravenszs_w2=ravenszf_w2 if sex==2;
label variable ravenszs_w2 "sex-standardised Raven's Matrices score at wave 2" ;
gen ravenszs_w3=. ;
replace ravenszs_w3=ravenszm_w3 if sex==1;
replace ravenszs_w3=ravenszf_w3 if sex==2;
label variable ravenszs_w3 "sex-standardised Raven's Matrices score at wave 3" ;

/*age and sex-standardised Raven's Matrices score  - standardised residuals from regression on age and sex*/
xi: regress ravens_w1 age_w1 i.sex ;
predict ravenszage_w1, rstandard ;
label variable ravenszage_w1 "age and sex-standardised Raven's Matrices score at wave 1" ;
xi: regress ravens_w2 age_w2 i.sex ;
predict ravenszage_w2, rstandard ;
label variable ravenszage_w2 "age and sex-standardised Raven's Matrices score at wave 2" ;
xi: regress ravens_w3 age_w3 i.sex ;
predict ravenszage_w3, rstandard ;
label variable ravenszage_w3 "age and sex-standardised Raven's Matrices score at wave 3" ;

/*WAIS-R Digit symbol test*/
/*range 0-75*/
gen digits_w1=digits ;
label variable digits_w1 "WAIS-R Digit symbol test score at wave 1" ;
gen digits_w2=dig2 ;
label variable digits_w2 "WAIS-R Digit symbol test score at wave 2" ;
gen digits_w3=dig3 ;
label variable digits_w3 "WAIS-R Digit symbol test score at wave 3" ;

/*standardise digit symbol test*/
egen digitsz_w1=std(digits_w1) ;
label variable digitsz_w1 "standardised Digit symbol test score at wave 1" ;
egen digitsz_w2=std(digits_w2) ;
label variable digitsz_w2 "standardised Digit symbol test score at wave 2" ;
egen digitsz_w3=std(digits_w3) ;
label variable digitsz_w3 "standardised Digit symbol test score at wave 3" ;

/*standardise Digit symbol test score for men and women seprately*/
egen digitszm_w1=std(digits_w1) if sex==1;
egen digitszf_w1=std(digits_w1) if sex==2;
label variable digitszm_w1 "standardised Digit symbol test score for men at wave 1" ;
label variable digitszf_w1 "standardised Digit symbol test score for women at wave 1" ;
egen digitszm_w2=std(digits_w2) if sex==1;
egen digitszf_w2=std(digits_w2) if sex==2;
label variable digitszm_w2 "standardised Digit symbol test score for men at wave 2" ;
label variable digitszf_w2 "standardised Digit symbol test score for women at wave 2" ;
egen digitszm_w3=std(digits_w3) if sex==1;
egen digitszf_w3=std(digits_w3) if sex==2;
label variable digitszm_w3 "standardised Digit symbol test score for men at wave 3" ;
label variable digitszf_w3 "standardised Digit symbol test score for women at wave 3" ;
/*combine in one variable*/
gen digitszs_w1=. ;
replace digitszs_w1=digitszm_w1 if sex==1;
replace digitszs_w1=digitszf_w1 if sex==2;
label variable digitszs_w1 "sex-standardised Digit symbol test score at wave 1" ;
gen digitszs_w2=. ;
replace digitszs_w2=digitszm_w2 if sex==1;
replace digitszs_w2=digitszf_w2 if sex==2;
label variable digitszs_w2 "sex-standardised Digit symbol test score at wave 2" ;
gen digitszs_w3=. ;
replace digitszs_w3=digitszm_w3 if sex==1;
replace digitszs_w3=digitszf_w3 if sex==2;
label variable digitszs_w3 "sex-standardised Digit symbol test score at wave 3" ;

/*age and sex-standardised Digit symbol test score - standardised residuals from regression on age and sex*/
xi: regress digits_w1 age_w1 i.sex ;
predict digitszage_w1, rstandard ;
label variable digitszage_w1 "age and sex-standardised Digit symbol test score at wave 1" ;
xi: regress digits_w2 age_w2 i.sex ;
predict digitszage_w2, rstandard ;
label variable digitszage_w2 "age and sex-standardised Digit symbol test score at wave 2" ;
xi: regress digits_w3 age_w3 i.sex ;
predict digitszage_w3, rstandard ;
label variable digitszage_w3 "age and sex-standardised Digit symbol test score at wave 3" ;

/*WAIS-R Block design test*/
/*score 0-50*/
gen block_w1=blockde ;
label variable block_w1 "WAIS-R Block Design test score at wave 1" ;
gen block_w2=blo2 ;
label variable block_w2 "WAIS-R Block Design test score at wave 2" ;
gen block_w3=block3 ;
label variable block_w3 "WAIS-R Block Design test score at wave 3" ;

/*standardise Block design*/
egen blockz_w1=std(block_w1) ;
label variable blockz_w1 "standardised Block Design test score at wave 1" ;
egen blockz_w2=std(block_w2) ;
label variable blockz_w2 "standardised Block Design test score at wave 2" ;
egen blockz_w3=std(block_w3) ;
label variable blockz_w3 "standardised Block Design test score at wave 3" ;

/*standardise Block Design test score for men and women seprately*/
egen blockzm_w1=std(block_w1) if sex==1;
egen blockzf_w1=std(block_w1) if sex==2;
label variable blockzm_w1 "standardised Block Design test score for men at wave 1" ;
label variable blockzf_w1 "standardised Block Design test score for women at wave 1" ;
egen blockzm_w2=std(block_w2) if sex==1;
egen blockzf_w2=std(block_w2) if sex==2;
label variable blockzm_w2 "standardised Block Design test score for men at wave 2" ;
label variable blockzf_w2 "standardised Block Design test score for women at wave 2" ;
egen blockzm_w3=std(block_w3) if sex==1;
egen blockzf_w3=std(block_w3) if sex==2;
label variable blockzm_w3 "standardised Block Design test score for men at wave 3" ;
label variable blockzf_w3 "standardised Block Design test score for women at wave 3" ;
/*combine in one variable*/
gen blockzs_w1=. ;
replace blockzs_w1=blockzm_w1 if sex==1;
replace blockzs_w1=blockzf_w1 if sex==2;
label variable blockzs_w1 "sex-standardised Block Design test score at wave 1" ;
gen blockzs_w2=. ;
replace blockzs_w2=blockzm_w2 if sex==1;
replace blockzs_w2=blockzf_w2 if sex==2;
label variable blockzs_w2 "sex-standardised Block Design test score at wave 2" ;
gen blockzs_w3=. ;
replace blockzs_w3=blockzm_w3 if sex==1;
replace blockzs_w3=blockzf_w3 if sex==2;
label variable blockzs_w3 "sex-standardised Block Design test score at wave 3" ;

/*age and sex-standardised Block Design test score - standardised residuals from regression on age and sex*/
xi: regress block_w1 age_w1 i.sex ;
predict blockzage_w1, rstandard ;
label variable blockzage_w1 "age and sex-standardised Block Design test score at wave 1" ;
xi: regress block_w2 age_w2 i.sex ;
predict blockzage_w2, rstandard ;
label variable blockzage_w2 "age and sex-standardised Block Design test score at wave 2" ;
xi: regress block_w3 age_w3 i.sex ;
predict blockzage_w3, rstandard ;
label variable blockzage_w3 "age and sex-standardised Block Design test score at wave 3" ;

/*use of common objects*/
/*sum of all 9 components used here */ 
/*however not that subtotals (e.g. otco, otin, otpe) from original data set*/
/*are not quite the same as the sum of the 3 components (e.g. obco+occo+ofco) */
gen objects_w1=obco+occo+ofco+obin+ocin+ofin+obpe+ocpe+ofpe ;
gen objects_w2=o2bco+o2cco+o2fco+o2bin+o2cin+o2fin+o2bpe+o2cpe+o2fpe ;
gen objects_w3=o3bco+o3cco+o3fco+o3bin+o3cin+o3fin+o3bpe+o3cpe+o3fpe ;
label variable objects_w1 "uses of common objects score at wave 1" ;
label variable objects_w2 "uses of common objects score at wave 2" ;
label variable objects_w3 "uses of common objects score at wave 3" ;

/*standardise use of common objects score*/
egen objectsz_w1=std(objects_w1) ;
label variable objectsz_w1 "standardised use of common objects score at wave 1" ;
egen objectsz_w2=std(objects_w2) ;
label variable objectsz_w2 "standardised use of common objects score at wave 2" ;
egen objectsz_w3=std(objects_w3) ;
label variable objectsz_w3 "standardised use of common objects score at wave 3" ;

/*standardise use of common objects score for men and women seprately*/
egen objectszm_w1=std(objects_w1) if sex==1;
egen objectszf_w1=std(objects_w1) if sex==2;
label variable objectszm_w1 "standardised use of common objects score for men at wave 1" ;
label variable objectszf_w1 "standardised use of common objects score for women at wave 1" ;
egen objectszm_w2=std(objects_w2) if sex==1;
egen objectszf_w2=std(objects_w2) if sex==2;
label variable objectszm_w2 "standardised use of common objects score for men at wave 2" ;
label variable objectszf_w2 "standardised use of common objects score for women at wave 2" ;
egen objectszm_w3=std(objects_w3) if sex==1;
egen objectszf_w3=std(objects_w3) if sex==2;
label variable objectszm_w3 "standardised use of common objects score for men at wave 3" ;
label variable objectszf_w3 "standardised use of common objects score for women at wave 3" ;
/*combine in one variable*/
gen objectszs_w1=. ;
replace objectszs_w1=objectszm_w1 if sex==1;
replace objectszs_w1=objectszf_w1 if sex==2;
label variable objectszs_w1 "sex-standardised use of common objects score at wave 1" ;
gen objectszs_w2=. ;
replace objectszs_w2=objectszm_w2 if sex==1;
replace objectszs_w2=objectszf_w2 if sex==2;
label variable objectszs_w2 "sex-standardised use of common objects score at wave 2" ;
gen objectszs_w3=. ;
replace objectszs_w3=objectszm_w3 if sex==1;
replace objectszs_w3=objectszf_w3 if sex==2;
label variable objectszs_w3 "sex-standardised use of common objects score at wave 3" ;

/*age and sex-standardised use of common objects score - standardised residuals from regression on age and sex*/
xi: regress objects_w1 age_w1 i.sex ;
predict objectszage_w1, rstandard ;
label variable objectszage_w1 "age and sex-standardised use of common objects score at wave 1" ;
xi: regress objects_w2 age_w2 i.sex ;
predict objectszage_w2, rstandard ;
label variable objectszage_w2 "age and sex-standardised use of common objects score at wave 2" ;
xi: regress objects_w3 age_w3 i.sex ;
predict objectszage_w3, rstandard ;
label variable objectszage_w3 "age and sex-standardised use of common objects score at wave 3" ;

/*AVLT*/
/*derive totals*/
/*range 0-105 (0-15 in each of 7 tests)*/
/*note: avlttot_w3 is similar to avltot3 already in dataset - but has 8 more people and 2 discrepant scores*/
gen avlttot_w1=avlt1+avlt2+avlt3+avlt4+avlt5+avltb+avltiv ;
label variable avlttot_w1 "total auditory verbal learning score at wave 1" ;
gen avlttot_w2=avlt2t1+avlt2t2+avlt2t3+avlt2t4+avlt2t5+avlt2tb+avlt2tiv ;
label variable avlttot_w2 "total auditory verbal learning score at wave 2" ;
gen avlttot_w3=avlt3t1+avlt3t2+avlt3t3+avlt3t4+avlt3t5+avlt3tb+avlt3tvi ;
label variable avlttot_w3 "total auditory verbal learning score at wave 3" ;

/*standardise AVLT*/
egen avlttotz_w1=std(avlttot_w1) ;
label variable avlttotz_w1 "standardised auditory verbal learning score at wave 1" ;
egen avlttotz_w2=std(avlttot_w2) ;
label variable avlttotz_w2 "standardised auditory verbal learning score at wave 2" ;
egen avlttotz_w3=std(avlttot_w3) ;
label variable avlttotz_w3 "standardised auditory verbal learning score at wave 3" ;

/*standardise auditory verbal learning score for men and women seprately*/
egen avlttotzm_w1=std(avlttot_w1) if sex==1;
egen avlttotzf_w1=std(avlttot_w1) if sex==2;
label variable avlttotzm_w1 "standardised auditory verbal learning score for men at wave 1" ;
label variable avlttotzf_w1 "standardised auditory verbal learning score for women at wave 1" ;
egen avlttotzm_w2=std(avlttot_w2) if sex==1;
egen avlttotzf_w2=std(avlttot_w2) if sex==2;
label variable avlttotzm_w2 "standardised auditory verbal learning score for men at wave 2" ;
label variable avlttotzf_w2 "standardised auditory verbal learning score for women at wave 2" ;
egen avlttotzm_w3=std(avlttot_w3) if sex==1;
egen avlttotzf_w3=std(avlttot_w3) if sex==2;
label variable avlttotzm_w3 "standardised auditory verbal learning score for men at wave 3" ;
label variable avlttotzf_w3 "standardised auditory verbal learning score for women at wave 3" ;
/*combine in one variable*/
gen avlttotzs_w1=. ;
replace avlttotzs_w1=avlttotzm_w1 if sex==1;
replace avlttotzs_w1=avlttotzf_w1 if sex==2;
label variable avlttotzs_w1 "sex-standardised auditory verbal learning score at wave 1" ;
gen avlttotzs_w2=. ;
replace avlttotzs_w2=avlttotzm_w2 if sex==1;
replace avlttotzs_w2=avlttotzf_w2 if sex==2;
label variable avlttotzs_w2 "sex-standardised auditory verbal learning score at wave 2" ;
gen avlttotzs_w3=.;
replace avlttotzs_w3=avlttotzm_w3 if sex==1;
replace avlttotzs_w3=avlttotzf_w3 if sex==2;
label variable avlttotzs_w3 "sex-standardised auditory verbal learning score at wave 3" ;

/*age and sex-standardised auditory verbal learning score - standardised residuals from regression on age and sex*/
xi: regress avlttot_w1 age_w1 i.sex ;
predict avlttotzage_w1, rstandard ;
label variable avlttotzage_w1 "age and sex-standardised auditory verbal learning score at wave 1" ;
xi: regress avlttot_w2 age_w2 i.sex ;
predict avlttotzage_w2, rstandard ;
label variable avlttotzage_w2 "age and sex-standardised auditory verbal learning score at wave 2" ;
xi: regress avlttot_w3 age_w3 i.sex ;
predict avlttotzage_w3, rstandard ;
label variable avlttotzage_w3 "age and sex-standardised auditory verbal learning score at wave 3" ;

/*derive fluid cognitive ability - Gf*/
/*overall z-scores*/
factor avlttotz_w1 digitsz_w1 ravensz_w1 objectsz_w1 blockz_w1, factors(1) pcf ;
predict Gf_w1 ;
label variable Gf_w1 "Gf at wave 1" ;
factor avlttotz_w2 digitsz_w2 ravensz_w2 objectsz_w2 blockz_w2, factors(1) pcf ;
predict Gf_w2 ;
label variable Gf_w2 "Gf at wave 2" ; 
factor avlttotz_w3 digitsz_w3 ravensz_w3 objectsz_w3 blockz_w3, factors(1) pcf ;
predict Gf_w3 ;
label variable Gf_w3 "Gf at wave 3" ;
/*age and sex-standardised scores*/
factor avlttotzage_w1 digitszage_w1 ravenszage_w1 objectszage_w1 blockzage_w1, factors(1) pcf ;
predict Gfage_w1 ;
label variable Gfage_w1 "Gf (age and sex adjusted) at wave 1" ;
factor avlttotzage_w2 digitszage_w2 ravenszage_w2 objectszage_w2 blockzage_w2, factors(1) pcf ;
predict Gfage_w2 ;
label variable Gf_w2 "Gf (age and sex adjusted) at wave 2" ; 
factor avlttotzage_w3 digitszage_w3 ravenszage_w3 objectszage_w3 blockzage_w3, factors(1) pcf ;
predict Gfage_w3 ;
label variable Gf_w3 "Gf (age and sex adjusted) at wave 3" ;

/*Other cognitive tests*/
/*MMSE scores*/
/*Note: MMSE scores are highly skewed*/
gen mmse_w1=mmse ;
label variable mmse_w1 "MMSE at wave 1" ;
gen mmse_w2=mmse2 ;
label variable mmse_w2 "MMSE at wave 2" ;
gen mmse_w3=mmse3 ;
label variable mmse_w3 "MMSE at wave 3" ;