************************************** ********* Analysis of PCTC *********** ******** Vu Dien - 17 Jul 2013 ******* ************************************** *========================================================================== ***** Independent variables ********** *-------------------------------------------------------------------------- * Variables Description Values *-------------------------------------------------------------------------- * shs secondhand smoking 1 Yes 0 No * cigs number of cigarettes smoked by fathers continuous * msmoke mother's smoking status 1 Yes 0 No * mcigs number of cigarettes smoked by mothers continuous *========================================================================== ***** Dependent variables ************ *-------------------------------------------------------------------------- * Variables Description Values *-------------------------------------------------------------------------- * delay delayed first tooth eruption 1 Yes 0 No * time time to eruption of the 1st tooth months * erupted an erupted tooth 1 Yes 0 No *========================================================================== ***** Potential confounding factors ** *-------------------------------------------------------------------------- * Variables Description Values *-------------------------------------------------------------------------- * mage mother's age continuous * medu mother's highest education level 1-->6 * alc mother's alcohol drinking status 1 Yes 0 No * income family's income continuous * sex child's gender 1 male * 2 female * bw birth weight continuous * ga gestational age at labor continuous * site study site 1 North * 2 Northeast * 3 Central * 4 South * 5 Bangkok *========================================================================== *============================================================= *Step 1: Find the code of variables in CRF files *============================================================= *--------------------------------------------------------------------------------------------------- * variable CRF file name in that file need to create new name *--------------------------------------------------------------------------------------------------- * shs ANT_B02B_ENG b02b_a HB22 (current) yes * cigs ANT_B02B_ENG b02b_a HB22A yes * msmoke ANT_B02A_ENG b02a_a B22 (current) yes * mcigs ANT_B02A_ENG b02a_a B22A yes * delay ANT_C08_EN (6 months) c08_1_a c85 (at 6 months) yes (d31=c85 if c85~=0 ???) * ANT_D03_EN (12months) d03_a d31 (at 12 months) yes * time ANT_C08_EN (6 months) c08_1_a c85 (at 6 months) yes * ANT_D03_EN (12months) d03_a d31 (at 12 months) yes * mage ANT_K02_ENG k02_a k21e1 yes * medu ANT_K02_ENG k02_a k21ig yes * alc ANT_B02A_ENG b02a_a B23 (yes/no) yes * income k02_b income no * sex aj_ja_nbl sex no * bw ANT_B05_ENG b05_a B53B yes * ga ANT_B04_ENG b04_a B42 yes * site ANT_B02B_ENG b02b_a idmot (the 1st char) yes *--------------------------------------------------------------------------------------------------- *================================================================== *Step 2: Convert the files which contain those variables into Stata *================================================================== *Using command INSHEET to convert .txt into .dta insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\b02b_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b02b_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\b02a_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b02a_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\c08_1_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\c08_1_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\d03_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\d03_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\k02_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\k02_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\k02_b.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\k02_b.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\aj_ja_nbl.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\aj_ja_nbl.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\b05_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b05_a.dta" insheet using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Dataset\PCTC\PCTC Data\b04_a.txt",clear save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b04_a.dta" *---------------------------------------------------------------------------------------------- *============================================================= *Step 3: Drop the variables which will not be used to analyze. * In other words, keep only interested variables *============================================================= *Using command KEEP to keep only the variables of interest use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b02b_a.dta",clear keep id idmot hb22 hb22a save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b02b_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b02a_a.dta",clear keep id idmot b22 b22a b23 save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b02a_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\c08_1_a.dta",clear keep id idchd c85 save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\c08_1_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\d03_a.dta",clear keep id idchd d31 save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\d03_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\k02_a.dta",clear keep id idmot k21e1 k21ig save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\k02_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\k02_b.dta",clear keep idmot income save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\k02_b.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\aj_ja_nbl.dta",clear keep idchd sex save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\aj_ja_nbl.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b05_a.dta",clear keep idchd b53b save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b05_a.dta" use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC converting\b04_a.dta",clear keep id idmot b42 save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b04_a.dta" *------------------------------------------------------------------------------------------- *===================================================================================================== *Step 4: Create var IDMOT for the files which do not have IDMOT (c08_1_a, aj_ja_nbl, d03_a, and b05_a) *===================================================================================================== *Using command TRUNC(IDCHD/10) to delete the last digit of IDMOT use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\c08_1_a.dta", clear gen idmot=trunc(idchd/10) save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\c08_1_a.dta", replace use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\aj_ja_nbl.dta", clear gen idmot=trunc(idchd/10) save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\aj_ja_nbl.dta", replace use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\d03_a.dta", clear gen idmot=trunc(idchd/10) save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\d03_a.dta", replace use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b05_a.dta",clear gen idmot=trunc(idchd/10) save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b05_a.dta", replace *--------------------------------------------------------------------------------------------- *=========================================================================== *Step 5: Merge files altogether, using file "c08_1_a.dta" as the master file *=========================================================================== *Using command JOINBY with option unmatch(both) to merge files use "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\c08_1_a.dta" joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b02b_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta" joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b02a_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\d03_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\k02_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\k02_b.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\aj_ja_nbl.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b05_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b04_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace joinby idmot using "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC selecting vars\b02b_a.dta", unmatched(both) drop _merge save "D:\Hoc Hanh\KKU\YEAR II\TAKASILA\Assignment\PCTC joinby\c08_1_a.dta", replace *--------------------------------------------------------------------------------------------------------------- *=========================== *Step 6: Creat new variables *=========================== *Create var SHS: secondhand smoking (yes/no) gen shs=hb22 recode shs (-9=0) (-8=0) la def noyes 0 "No" 1 "Yes" /*Create label*/ la val shs noyes *Create var CIGS: number of cigarettes smoked by fathers (continuous) gen cigs=hb22a recode cigs (-9=0) (-8=0) *Create var MSMOKE: mother's smoking status (yes/no) gen msmoke=b22 recode msmoke (-9=0) (-8=0) la val msmoke noyes *Create var MCIGS: number of cigarettes smoked by mothers (continuous) gen mcigs=b22a recode mcigs (-9=0) (-8=0) *Create var MAGE, MEDU, ALC: age, education level, alcohol status of mothers gen mage=k21e1 recode mage (-9=.) gen medu=k21ig recode medu (-9=.) gen alc=b23 recode alc (-9=.) (2=0) la val alc noyes *Create intervals and new categorical variables for MAGE xtile mage3t = mage, nq(3) la def lmage3t 1 "13-24" 2 "25-30" 3 "31-48" /*Create label*/ la val mage3t lmage3t tabstat mage, stat (n min max) by(mage3t) *Create var BW, GA: birthweight and gestational age of infants gen bw=b53b gen ga=b42 *Create new categorical variable of Birthweight gen bwgroup=. replace bwgroup=0 if bw<2500 replace bwgroup=1 if bw>=2500 la def lblbw 0 "Normal BW" 1 "Low BW" la val bwgroup lblbw *Create new categorical variable of Gestational Age gen gagroup=. replace gagroup=0 if ga>=37 replace gagroup=1 if ga<37 *Create var SITE: the study sites gen site=trunc(idchd/10000000) la def lsite 1 "North" 2 "Northeast" 3 "Central" 4 "South" 5 "Bangkok" la val site lsite *Recode and Label var INCOME recode income (-9=.) xtile income3t = income, nq(3) tabstat income, stat (n min max) by(income3t) la def llincome3t 1 "Low <= 66000" 2 "Medium 66000 - 158000" 3 "High >= 158000" /*Create label*/ la val income3t llincome3t *Create variable TIME gen time = c85 replace time = "" if regexm(time, "^[0-9]") == 0 /*All data started with non-numeric was now missing value*/ replace time = "" if time == "0" /*Force changes due to impossible value*/ destring time, replace replace time = d31 if time == . & d31!=-9 /*Change to the month of eruption assessed by 12months (var d31)*/ *Create variable ERUPTED: tooth eruption status (1 yes, 0 no) gen erupted = 0 replace erupted = 1 if time != . *Create variable DELAY: delayed tooth eruption status (1 yes if erupted >=10 months, 0 no if erupted <10 months) gen delay = . replace delay = 1 if time >= 10 & time!=. replace delay = 0 if time < 10 & time!=. la val delay noyes *Recode variable SEX recode sex (2=0) la def l_sex 0 "female" 1 "male" la val sex l_sex *================================= *Step 7: Start to analyze the data *================================= *------------------------------------------------------------------------------------------------------ * Table 1: Demographic characteristics * For mothers tab mage3t site, row bysort site: tabstat mage, stats(n mean sd min max) tab medu site, row tab alc site, row tab income3t site, row * For infants tab sex site, row tab bwgroup site, row tab gagroup site, row *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Table 2: Percentage of SHS in pregnant women among 5 sites tab site shs, row tab shs *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Table 3: Crude hazard ratios (HR) of tooth eruption for each explanatory factor * Event = erupted stset time, failure(erupted) local listvar "shs cigs msmoke mcigs i.mage3t sex i.medu i.income3t bwgroup gagroup alc" foreach var of local listvar { stcox `var', strata(site) } *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Table 4: Adjusted HR of tooth eruption for each explanatory factor stcox i.shs cigs i.msmoke mcigs i.mage3t i.sex i.medu i.income3t i.bwgroup i.gagroup i.alc, strata(site) *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Table 5: Crude and Adjust HR, only show SHS stset time, failure(erupted) stsum stci /*Median time to tooth eruption*/ stcox shs /*Crude HR*/ stcox i.shs cigs i.msmoke mcigs i.mage3t i.sex i.medu i.income3t i.bwgroup i.gagroup i.alc, strata(site) * To see the equality of the survival function between SHS and non SHS group * H0: S[SHS](t) = S[non-SHS](t) (Survival is the same) * H1: S[SHS](t) # S[non-SHS](t) (Survival is not the same) sts test shs, strata(site) *------------------------------------------------------------------------------------------------------ * Table 6: Crude effect of each factor on DTE * Multiple logistic regression *====================================================================================================== ******************************************************************************************************* * Research question is that "Does SHS affect DTE?" * SHS is the "risk of interest" ******************************************************************************************************* *====================================================================================================== *==================================================================================== ***** Dependent and independent variables *------------------------------------------------------------------------------------ * Variables Description Values *------------------------------------------------------------------------------------ * delay delayed first tooth eruption 1 Yes 0 No * shs secondhand smoking 1 Yes 0 No *==================================================================================== ***** Potential confounding factors *------------------------------------------------------------------------------------ * Variables Description Values *------------------------------------------------------------------------------------ * msmoke mother's smoking status 1 Yes 0 No * mage3t mother's age 1 13-24 * 2 25-30 * 3 31-48 * medu mother's highest education level 1-->6 * alc mother's alcohol drinking status 1 Yes 0 No * income3t family's income 1 Low <=66k * 2 Medium 66k-158k * 3 High >=158k * sex child's gender 1 male * 0 female * bwgroup birth weight 1 Low BW * 0 Normal BW * gagroup gestational age at labor 1 < 37wks * 0 >=37 wks * site study site 1 North * 2 Northeast * 3 Central * 4 South * 5 Bangkok *===================================================================================== * Step 1: Exploring the data and univariate analysis list delay shs msmoke mage3t medu alc income sex bw ga site tab delay ci delay *------------------------------------------------------------------------------------- * Step 2: Bivariate (crude) analysis * Section 2.1 Crude effect of SHS on DELAY cs delay shs, or *Section 2.2 Crude effect of MSMOKE on DELAY cs delay msmoke, or /*p =0.72*/ *Section 2.3 Crude effect of MAGE3T on DELAY tab mage3t delay, row chi2 exact /*p =0.068*/ csi 248 305 1159 1325, or /*to see OR of group 2 compared to group 1, p=0.44*/ csi 285 305 1070 1325, or /*to see OR of group 3 compared to group 1, p=0.11*/ *Section 2.4 Crude effect of MEDU on DELAY tab medu delay, row chi2 exact /*p=0.01*/ replace medu=5 if medu==6 /*collapsed two categories because of small number of category 6*/ tab medu delay, row chi2 exact /*p=0.005*/ csi 443 37 1694 190, or csi 239 37 989 190, or csi 52 37 303 190, or csi 68 37 390 190, or *or can use this command logistic delay i.medu *Section 2.5 Crude effect of ALC on DELAY cs delay alc, or /*p =0.79*/ *Section 2.6 Crude effect of INCOME3T on DELAY tab income3t delay, row chi2 exact /*p<0.001*/ csi 254 328 1188 1116, or csi 249 328 1191 1116, or *Section 2.7 Crude effect of SEX on DELAY cs delay sex, or /*p<0.001*/ *Section 2.8 Crude effect of BWGROUP on DELAY cs delay bwgroup, or /*p<0.001*/ *Section 2.9 Crude effect of GAGROUP on DELAY cs delay gagroup, or /*p=0.02*/ *Section 2.10 Crude effect of SITE on DELAY tab site delay, row chi2 /*p<0.001*/ csi 319 125 829 697, or csi 154 125 760 697, or csi 139 125 695 697, or csi 103 125 582 697, or *or can use this command logistic delay i.site *------------------------------------------------------------------------------------- * Step 3: Stratified analysis *Section 3.1 Effect of BWGROUP on the association between SHS and DELAY cc delay shs, by(bwgroup) /*Test of homogeneity (M-H) p = 0.24*/ *Section 3.2 Effect of GAGROUP on the association between SHS and DELAY cc delay shs, by(gagroup) /*Test of homogeneity (M-H) p = 0.009*/ *Section 3.3 Effect of SITE on the association between SHS and DELAY cc delay shs, by(site) /*Test of homogeneity (M-H) p = 0.0934*/ *Section 3.4 Effect of SEX on the association between SHS and DELAY cc delay shs, by(sex) /*Test of homogeneity (M-H) p < 0.001*/ *Section 3.5 Effect of INCOME on the association between SHS and DELAY cc delay shs, by(income3t) /*Test of homogeneity (M-H) p < 0.725*/ *Section 3.6 Effect of MAGE on the association between SHS and DELAY cc delay shs, by(mage3t) /*Test of homogeneity (M-H) p < 0.0134*/ *Section 3.7 Effect of MEDU on the association between SHS and DELAY cc delay shs, by(medu) /*Test of homogeneity (M-H) p < 0.185*/ * We select SHS*SEX, SHS*MAGE3T, and SHS*GAGROUP *------------------------------------------------------------------------------------- * Step 4: Multivariable analysis : Logistic regression * Create interaction variables gen s_sex = shs * sex gen s_mage3t = shs * mage3t gen s_gagr = shs * gagroup * Section 4.1. The initial model – the full model xi: logit delay shs i.mage3t i.medu i.income3t sex bwgroup gagroup i.site s_sex s_mage3t s_gagr est store full * Section 4.2. Model without INCOME as _Iincome3t_3 has highest p value of 0.890 xi: logit delay shs i.mage3t i.medu sex bwgroup gagroup i.site s_sex s_mage3t s_gagr lrtest full, force /* p<0.001, so need to keep INCOME in the model*/ * Section 4.3. Model without SITE as _Isite_3 has highest p value of 0.854 xi: logit delay shs i.mage3t i.medu i.income3t sex bwgroup gagroup s_sex s_mage3t s_gagr lrtest full, force /* p<0.001, so need to keep SITE in the model*/ * Section 4.4. Model without EDU as _Imedu_4 has highest p value of 0.723 xi: logit delay shs i.mage3t i.income3t sex bwgroup gagroup i.site s_sex s_mage3t s_gagr lrtest full, force /* p<0.0177, so need to keep EDU in the model*/ * Section 4.5. Model without MAGE3T as _Image3t_2 has highest p value of 0.445 xi: logit delay shs i.medu i.income3t sex bwgroup gagroup i.site s_sex s_mage3t s_gagr lrtest full, force /* p<0.001, so need to keep MAGE3T in the model*/ /* After removing MEDU, lrtest cannot run because observations differ: 4066 vs. 4070 */ *------------------------------------------------------------------------------------------ * I tried this one: * Backward Stepwise: xi: sw logistic delay shs i.mage3t i.medu i.income3t sex bwgroup gagroup i.site s_sex s_mage3t s_gagr, pr(0.2) est store full * Step 5: Assessing model adequacy: test for goodness of fit of the model estat gof /*goodness-of-fit test*/ * Step 6: Obtaining measure of associations from the model *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Table 7: Model of Association between the number of cigarettes smoked by the fathers and the time of first tooth eruption corr cigs time * then I see no correlation between cigs and time regress time cigs mage bw ga income alc * Draw a regression line with 95% CI twoway lfitci time cigs, stdf || scatter time cigs /*stdf: SE for the forecast*/ *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Figure 3: Difference in the probability of erupted tooth between SHS group and non-SHS group * Event = erupted stset time, failure(erupted) sts graph, by(shs) gwood *------------------------------------------------------------------------------------------------------ *------------------------------------------------------------------------------------------------------ * Figure 4: Difference in the probability of erupted tooth between female and male sts graph, by(sex) gwood *------------------------------------------------------------------------------------------------------ *The End *------------------------------------------------------------------------------------------------------ * Thinking about imputation for missing data mi set mlong mi register imputed mage mi impute regress mage shs cigs bw, add(200) rseed(10394) mi estimate: stcox shs age