{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/steve/Documents/teaching/c2015/biostat208/labs/lab9/lab9.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 Mar 2015, 15:15:53
{txt}
{com}. 
. * smoking indicator
. gen smoke = ncig
{txt}
{com}. recode smoke 0 = 0 1/max = 1
{txt}(smoke: 1495 changes made)

{com}. label define smlab 0 "nonsmoker" 1 "smoker" 
{txt}
{com}. label val smoke smlab
{txt}
{com}. 
. * adjusted assoc. between CHD & type A behavior
. 
. * using cs
. cs chd69 dibpat, by(smoke) or

           {txt}smoke {c |}{col 26}OR{col 35}[95% Conf. Interval]   M-H Weight
{hline 17}{c +}{hline 49}
       nonsmoker {c |}   {res} 2.941176     1.882394   4.594855     12.10169{txt} (Cornfield)
          smoker {c |}   {res} 1.962697     1.385228   2.780702     23.66644{txt} (Cornfield)
{hline 17}{c +}{hline 49}
           Crude {c |}   {res} 2.372929     1.804034   3.121147{txt}{col 69}
    M-H combined {c |}   {res} 2.293753     1.741568   3.021016
{txt}{hline 17}{c BT}{hline 49}
Test of homogeneity (M-H)      chi2({res}1{txt}) ={res}    1.938{txt}  Pr>chi2 = {res}0.1639

                   {txt}Test that combined OR = 1:
                                Mantel-Haenszel chi2(1) ={res}     36.68
                                                {txt}Pr>chi2 ={res}    0.0000
{txt}
{com}. 
. * using logistic regression
. logistic chd69 i.dibpat i.smoke
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3154
{txt}{col 51}LR chi2({res}2{txt}){col 67}= {res}     60.51
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-860.36587{txt}{col 51}Pseudo R2{col 67}= {res}    0.0340

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}dibpat {c |}
{space 6}A1,A2  {c |}{col 14}{res}{space 2} 2.301319{col 26}{space 2} .3238094{col 37}{space 1}    5.92{col 46}{space 3}0.000{col 54}{space 4} 1.746657{col 67}{space 3} 3.032117
{txt}{space 12} {c |}
{space 7}smoke {c |}
{space 5}smoker  {c |}{col 14}{res}{space 2}   1.8002{col 26}{space 2} .2422866{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} 1.382798{col 67}{space 3} 2.343597
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0393656{col 26}{space 2} .0054842{col 37}{space 1}  -23.22{col 46}{space 3}0.000{col 54}{space 4} .0299593{col 67}{space 3} .0517251
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * binary version of age
. gen dage = age
{txt}
{com}. recode dage 39/49=0 50/59=1
{txt}(dage: 3154 changes made)

{com}. label define dagelab 0 "39-49" 1 "50-59" 
{txt}
{com}. label val dage dagelab
{txt}
{com}. 
. 
. * Assoc. between CHD, age & arcus
. cs chd69 arcus, or

{col 18}{txt}{c |} arcus senilis{col 43}{c |}
{col 18}{c |}   Exposed   Unexposed  {c |}      Total
{hline 17}{c +}{hline 24}{c +}{hline 12}
           Cases {c |} {res}      102         153{txt}  {c |}  {res}      255
        {txt}Noncases {c |} {res}      839        2058{txt}  {c |}  {res}     2897
{txt}{hline 17}{c +}{hline 24}{c +}{hline 12}
{col 12}Total {c |} {res}      941        2211{txt}  {c |}  {res}     3152
{txt}{col 18}{c |}{col 43}{c |}
            Risk {c |} {res} .1083953    .0691995{txt}  {c |}  {res}  .080901
{txt}{col 18}{c |}{col 43}{c |}
{col 18}{c |}      Point estimate    {c |}    [95% Conf. Interval]
{col 18}{c LT}{hline 24}{c +}{hline 24}
 Risk difference {c |} {res}{col 27} .0391959{txt}{col 43}{c |}   {res} .0166915    .0617003{txt} 
      Risk ratio {c |} {res}{col 27} 1.566419{txt}{col 43}{c |}   {res} 1.233865    1.988603{txt} 
 Attr. frac. ex. {c |} {res}{col 27} .3616011{txt}{col 43}{c |}   {res} .1895387    .4971343{txt} 
 Attr. frac. pop {c |} {res}{col 27} .1446404{txt}{col 43}{c |}
      Odds ratio {c |} {res}{col 27}  1.63528{txt}{col 43}{c |}   {res} 1.257732    2.126197{txt} (Cornfield)
{col 18}{c BLC}{hline 24}{c BT}{hline 24}
{col 22}          chi2(1) ={res}    13.64{txt}  Pr>chi2 ={res} 0.0002
{txt}
{com}. cs chd69 dage, or

{col 18}{txt}{c |} dage{col 43}{c |}
{col 18}{c |}   Exposed   Unexposed  {c |}      Total
{hline 17}{c +}{hline 24}{c +}{hline 12}
           Cases {c |} {res}      112         145{txt}  {c |}  {res}      257
        {txt}Noncases {c |} {res}      793        2104{txt}  {c |}  {res}     2897
{txt}{hline 17}{c +}{hline 24}{c +}{hline 12}
{col 12}Total {c |} {res}      905        2249{txt}  {c |}  {res}     3154
{txt}{col 18}{c |}{col 43}{c |}
            Risk {c |} {res} .1237569    .0644731{txt}  {c |}  {res} .0814838
{txt}{col 18}{c |}{col 43}{c |}
{col 18}{c |}      Point estimate    {c |}    [95% Conf. Interval]
{col 18}{c LT}{hline 24}{c +}{hline 24}
 Risk difference {c |} {res}{col 27} .0592838{txt}{col 43}{c |}   {res} .0355493    .0830183{txt} 
      Risk ratio {c |} {res}{col 27} 1.919512{txt}{col 43}{c |}   {res}  1.51876     2.42601{txt} 
 Attr. frac. ex. {c |} {res}{col 27} .4790343{txt}{col 43}{c |}   {res} .3415682    .5878006{txt} 
 Attr. frac. pop {c |} {res}{col 27}  .208762{txt}{col 43}{c |}
      Odds ratio {c |} {res}{col 27}  2.04938{txt}{col 43}{c |}   {res}  1.58146    2.655782{txt} (Cornfield)
{col 18}{c BLC}{hline 24}{c BT}{hline 24}
{col 22}          chi2(1) ={res}    30.30{txt}  Pr>chi2 ={res} 0.0000
{txt}
{com}. cs chd69 arcus, by(dage) or

            {txt}dage {c |}{col 26}OR{col 35}[95% Conf. Interval]   M-H Weight
{hline 17}{c +}{hline 49}
           39-49 {c |}   {res} 1.911643     1.348286   2.710533     20.34964{txt} (Cornfield)
           50-59 {c |}   {res}   1.0575     .7086192   1.578275     23.00885{txt} (Cornfield)
{hline 17}{c +}{hline 49}
           Crude {c |}   {res}  1.63528     1.257732   2.126197{txt}{col 69}
    M-H combined {c |}   {res} 1.458379     1.118748   1.901115
{txt}{hline 17}{c BT}{hline 49}
Test of homogeneity (M-H)      chi2({res}1{txt}) ={res}    4.742{txt}  Pr>chi2 = {res}0.0294

                   {txt}Test that combined OR = 1:
                                Mantel-Haenszel chi2(1) ={res}      8.05
                                                {txt}Pr>chi2 ={res}    0.0046
{txt}
{com}. cs chd69 dage, by(arcus) or

   {txt}arcus senilis {c |}{col 26}OR{col 35}[95% Conf. Interval]   M-H Weight
{hline 17}{c +}{hline 49}
          absent {c |}   {res}   2.4431     1.745663   3.419325     18.83853{txt} (Cornfield)
         present {c |}   {res} 1.351497     .8957664    2.03919     18.99575{txt} (Cornfield)
{hline 17}{c +}{hline 49}
           Crude {c |}   {res}  2.04938      1.58146   2.655782{txt}{col 69}
    M-H combined {c |}   {res}  1.89503     1.457833   2.463341
{txt}{hline 17}{c BT}{hline 49}
Test of homogeneity (M-H)      chi2({res}1{txt}) ={res}    4.747{txt}  Pr>chi2 = {res}0.0294

                   {txt}Test that combined OR = 1:
                                Mantel-Haenszel chi2(1) ={res}     23.99
                                                {txt}Pr>chi2 ={res}    0.0000
{txt}
{com}. 
. * Logistic model for age-arcus interaction
. 
. logistic chd69 i.arcus##i.dage, coef
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3152
{txt}{col 51}LR chi2({res}3{txt}){col 67}= {res}     40.33
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-865.43251{txt}{col 51}Pseudo R2{col 67}= {res}    0.0228

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         chd69{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}arcus {c |}
{space 6}present  {c |}{col 16}{res}{space 2} .6479628{col 28}{space 2} .1788637{col 39}{space 1}    3.62{col 48}{space 3}0.000{col 56}{space 4} .2973964{col 69}{space 3} .9985293
{txt}{space 14} {c |}
{space 10}dage {c |}
{space 8}50-59  {c |}{col 16}{res}{space 2} .8932677{col 28}{space 2} .1721239{col 39}{space 1}    5.19{col 48}{space 3}0.000{col 56}{space 4} .5559111{col 69}{space 3} 1.230624
{txt}{space 14} {c |}
{space 4}arcus#dage {c |}
present#50-59  {c |}{col 16}{res}{space 2}-.5920552{col 28}{space 2} .2722269{col 39}{space 1}   -2.17{col 48}{space 3}0.030{col 56}{space 4} -1.12561{col 69}{space 3}-.0585002
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}-2.882853{col 28}{space 2} .1089261{col 39}{space 1}  -26.47{col 48}{space 3}0.000{col 56}{space 4}-3.096344{col 69}{space 3}-2.669362
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * calculation of OR for arcus effect among the old
. display _b[_cons] + _b[1.arcus]*1 + _b[1.dage]*1 + _b[1.arcus#1.dage]*1
{res}-1.9336776
{txt}
{com}. display _b[_cons] + _b[1.arcus]*0 + _b[1.dage]*1 + _b[1.arcus#1.dage]*0
{res}-1.9895852
{txt}
{com}. * log OR
. display _b[1.arcus] + _b[1.arcus#1.dage]
{res}.05590763
{txt}
{com}. * OR
. display exp(_b[1.arcus] + _b[1.arcus#1.dage])
{res}1.0575
{txt}
{com}. * With lincom
. lincom 1.arcus + 1.arcus#1.dage

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + [chd69]1.arcus#1.dage = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}   1.0575{col 26}{space 2} .2170202{col 37}{space 1}    0.27{col 46}{space 3}0.785{col 54}{space 4} .7072887{col 67}{space 3} 1.581117
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.dage + 1.arcus#1.dage

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.dage + [chd69]1.arcus#1.dage = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.351497{col 26}{space 2} .2850372{col 37}{space 1}    1.43{col 46}{space 3}0.153{col 54}{space 4} .8939071{col 67}{space 3} 2.043325
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.arcus

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.911643{col 26}{space 2} .3419235{col 37}{space 1}    3.62{col 46}{space 3}0.000{col 54}{space 4} 1.346349{col 67}{space 3} 2.714287
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.dage

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.dage = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}   2.4431{col 26}{space 2} .4205159{col 37}{space 1}    5.19{col 46}{space 3}0.000{col 54}{space 4} 1.743529{col 67}{space 3} 3.423366
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.arcus + 1.dage + 1.arcus#1.dage

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + [chd69]1.dage + [chd69]1.arcus#1.dage = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 2.583578{col 26}{space 2} .4916842{col 37}{space 1}    4.99{col 46}{space 3}0.000{col 54}{space 4} 1.779215{col 67}{space 3} 3.751586
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * risk difference regression model
. binreg chd69 i.arcus##i.dage, rd

{txt}Iteration 1:{col 16}deviance = {res} 1730.865
{txt}Iteration 2:{col 16}deviance = {res} 1730.865

{txt}Generalized linear models{col 52}No. of obs{col 68}={col 70}{res}     3152
{txt}Optimization     : {res}MQL Fisher scoring{txt}{col 52}Residual df{col 68}={col 70}{res}     3148
{col 20}(IRLS EIM){txt}{col 52}Scale parameter{col 68}={col 70}{res}        1
{txt}Deviance{col 18}={res}{col 20} 1730.865012{txt}{col 52}(1/df) Deviance{col 68}={res}{col 70} .5498301
{txt}Pearson{col 18}={res}{col 20}        3152{txt}{col 52}(1/df) Pearson{col 68}={res}{col 70} 1.001271

{txt}Variance function: {res}V(u) = {col 27}u*(1-u){col 52}{txt}[{res}Bernoulli{txt}]
Link function    : {res}g(u) = {col 27}u{col 52}{txt}[{res}Identity{txt}]

{col 52}BIC{col 68}={res}{col 70}-23628.77

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}      EIM
{col 1}         chd69{col 16}{c |} Risk Diff.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}arcus {c |}
{space 6}present  {c |}{col 16}{res}{space 2} .0436531{col 28}{space 2} .0135409{col 39}{space 1}    3.22{col 48}{space 3}0.001{col 56}{space 4} .0171134{col 69}{space 3} .0701927
{txt}{space 14} {c |}
{space 10}dage {c |}
{space 8}50-59  {c |}{col 16}{res}{space 2}  .067293{col 28}{space 2} .0151269{col 39}{space 1}    4.45{col 48}{space 3}0.000{col 56}{space 4} .0376448{col 69}{space 3} .0969412
{txt}{space 14} {c |}
{space 4}arcus#dage {c |}
present#50-59  {c |}{col 16}{res}{space 2}-.0376097{col 28}{space 2} .0260577{col 39}{space 1}   -1.44{col 48}{space 3}0.149{col 56}{space 4}-.0886819{col 69}{space 3} .0134625
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} .0530077{col 28}{space 2} .0054679{col 39}{space 1}    9.69{col 48}{space 3}0.000{col 56}{space 4} .0422909{col 69}{space 3} .0637246
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. * interaction model with age continuous (and linear)
. logistic chd69 i.arcus##c.age, coef
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3152
{txt}{col 51}LR chi2({res}3{txt}){col 67}= {res}     53.33
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-858.93362{txt}{col 51}Pseudo R2{col 67}= {res}    0.0301

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}arcus {c |}
{space 4}present  {c |}{col 14}{res}{space 2} 2.754185{col 26}{space 2} 1.140118{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .5195952{col 67}{space 3} 4.988774
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .089647{col 26}{space 2} .0148904{col 37}{space 1}    6.02{col 46}{space 3}0.000{col 54}{space 4} .0604623{col 67}{space 3} .1188317
{txt}{space 12} {c |}
{space 1}arcus#c.age {c |}
{space 4}present  {c |}{col 14}{res}{space 2}-.0498298{col 26}{space 2} .0233431{col 37}{space 1}   -2.13{col 46}{space 3}0.033{col 54}{space 4}-.0955814{col 67}{space 3}-.0040782
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-6.788086{col 26}{space 2} .7179977{col 37}{space 1}   -9.45{col 46}{space 3}0.000{col 54}{space 4}-8.195335{col 67}{space 3}-5.380836
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * OR for the effect of arcus in 55 year-olds
. lincom _cons + c.age*55 + 1.arcus + 1.arcus#c.age*55

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + 55{res}*{res}[chd69]age + 55{res}*{res}[chd69]1.arcus#c.age + [chd69]_cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1581902{col 26}{space 2} .0239989{col 37}{space 1}  -12.15{col 46}{space 3}0.000{col 54}{space 4} .1175018{col 67}{space 3} .2129682
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom _cons + c.age*55 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}55{res}*{res}[chd69]age + [chd69]_cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1560621{col 26}{space 2} .0211577{col 37}{space 1}  -13.70{col 46}{space 3}0.000{col 54}{space 4} .1196459{col 67}{space 3} .2035621
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.arcus + 1.arcus#c.age*55

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + 55{res}*{res}[chd69]1.arcus#c.age = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.013637{col 26}{space 2} .2062336{col 37}{space 1}    0.07{col 46}{space 3}0.947{col 54}{space 4} .6802954{col 67}{space 3} 1.510313
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * OR for the effect of arcus in 40 year-olds
. lincom _cons + c.age*40 + 1.arcus + 1.arcus#c.age*40

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + 40{res}*{res}[chd69]age + 40{res}*{res}[chd69]1.arcus#c.age + [chd69]_cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0870551{col 26}{space 2}  .016688{col 37}{space 1}  -12.73{col 46}{space 3}0.000{col 54}{space 4} .0597893{col 67}{space 3} .1267549
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom _cons + c.age*40 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}40{res}*{res}[chd69]age + [chd69]_cons = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0406724{col 26}{space 2}  .005884{col 37}{space 1}  -22.13{col 46}{space 3}0.000{col 54}{space 4} .0306307{col 67}{space 3}  .054006
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lincom 1.arcus + 1.arcus#c.age*40

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]1.arcus + 40{res}*{res}[chd69]1.arcus#c.age = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 2.140398{col 26}{space 2} .5140334{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} 1.336816{col 67}{space 3} 3.427025
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * risk difference model
. binreg chd69 i.arcus##c.age, rd

{txt}Iteration 1:{col 16}deviance = {res} 1721.917
{txt}Iteration 2:{col 16}deviance = {res} 1720.976
{txt}Iteration 3:{col 16}deviance = {res} 1720.929
{txt}Iteration 4:{col 16}deviance = {res} 1720.927
{txt}Iteration 5:{col 16}deviance = {res} 1720.927
{txt}Iteration 6:{col 16}deviance = {res} 1720.927
{txt}Iteration 7:{col 16}deviance = {res} 1720.927

{txt}Generalized linear models{col 52}No. of obs{col 68}={col 70}{res}     3152
{txt}Optimization     : {res}MQL Fisher scoring{txt}{col 52}Residual df{col 68}={col 70}{res}     3148
{col 20}(IRLS EIM){txt}{col 52}Scale parameter{col 68}={col 70}{res}        1
{txt}Deviance{col 18}={res}{col 20}  1720.92654{txt}{col 52}(1/df) Deviance{col 68}={res}{col 70}  .546673
{txt}Pearson{col 18}={res}{col 20} 3151.956739{txt}{col 52}(1/df) Pearson{col 68}={res}{col 70} 1.001257

{txt}Variance function: {res}V(u) = {col 27}u*(1-u){col 52}{txt}[{res}Bernoulli{txt}]
Link function    : {res}g(u) = {col 27}u{col 52}{txt}[{res}Identity{txt}]

{col 52}BIC{col 68}={res}{col 70}-23638.71

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}      EIM
{col 1}       chd69{col 14}{c |} Risk Diff.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}arcus {c |}
{space 4}present  {c |}{col 14}{res}{space 2} .0951563{col 26}{space 2} .0945146{col 37}{space 1}    1.01{col 46}{space 3}0.314{col 54}{space 4}-.0900888{col 67}{space 3} .2804015
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .005478{col 26}{space 2} .0010767{col 37}{space 1}    5.09{col 46}{space 3}0.000{col 54}{space 4} .0033677{col 67}{space 3} .0075883
{txt}{space 12} {c |}
{space 1}arcus#c.age {c |}
{space 4}present  {c |}{col 14}{res}{space 2}-.0014309{col 26}{space 2} .0020431{col 37}{space 1}   -0.70{col 46}{space 3}0.484{col 54}{space 4}-.0054354{col 67}{space 3} .0025735
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} -.180477{col 26}{space 2} .0474075{col 37}{space 1}   -3.81{col 46}{space 3}0.000{col 54}{space 4} -.273394{col 67}{space 3}-.0875599
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * RERI
. 
. * likelihood ratio test example
. logistic chd69 chol sbp age i.smoke bmi i.behpat
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3142
{txt}{col 51}LR chi2({res}8{txt}){col 67}= {res}    189.40
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-794.89999{txt}{col 51}Pseudo R2{col 67}= {res}    0.1065

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}chol {c |}{col 14}{res}{space 2} 1.010798{col 26}{space 2} .0015221{col 37}{space 1}    7.13{col 46}{space 3}0.000{col 54}{space 4} 1.007819{col 67}{space 3} 1.013785
{txt}{space 9}sbp {c |}{col 14}{res}{space 2} 1.018203{col 26}{space 2} .0042049{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} 1.009995{col 67}{space 3} 1.026478
{txt}{space 9}age {c |}{col 14}{res}{space 2} 1.062438{col 26}{space 2} .0127373{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} 1.037764{col 67}{space 3} 1.087698
{txt}{space 12} {c |}
{space 7}smoke {c |}
{space 5}smoker  {c |}{col 14}{res}{space 2} 1.828109{col 26}{space 2}  .257801{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} 1.386645{col 67}{space 3} 2.410122
{txt}{space 9}bmi {c |}{col 14}{res}{space 2} 1.057105{col 26}{space 2} .0280768{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} 1.003483{col 67}{space 3} 1.113592
{txt}{space 12} {c |}
{space 6}behpat {c |}
{space 9}A2  {c |}{col 14}{res}{space 2} 1.069375{col 26}{space 2} .2365875{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4} .6931243{col 67}{space 3} 1.649868
{txt}{space 9}B3  {c |}{col 14}{res}{space 2} .5142782{col 26}{space 2} .1246099{col 37}{space 1}   -2.74{col 46}{space 3}0.006{col 54}{space 4} .3198538{col 67}{space 3} .8268844
{txt}{space 9}B4  {c |}{col 14}{res}{space 2}  .572301{col 26}{space 2} .1827124{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}  .306105{col 67}{space 3} 1.069987
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 8.55e-06{col 26}{space 2} 8.65e-06{col 37}{space 1}  -11.53{col 46}{space 3}0.000{col 54}{space 4} 1.18e-06{col 67}{space 3} .0000622
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. gen behpat2 = 5 - behpat
{txt}
{com}. tab behpat behpat2

{txt}behavioral {c |}
pattern (4 {c |}                   behpat2
    level) {c |}         1          2          3          4 {c |}     Total
{hline 11}{c +}{hline 44}{c +}{hline 10}
        A1 {c |}{res}         0          0          0        264 {txt}{c |}{res}       264 
{txt}        A2 {c |}{res}         0          0      1,325          0 {txt}{c |}{res}     1,325 
{txt}        B3 {c |}{res}         0      1,216          0          0 {txt}{c |}{res}     1,216 
{txt}        B4 {c |}{res}       349          0          0          0 {txt}{c |}{res}       349 
{txt}{hline 11}{c +}{hline 44}{c +}{hline 10}
     Total {c |}{res}       349      1,216      1,325        264 {txt}{c |}{res}     3,154 

{txt}
{com}. logistic chd69 chol sbp age i.smoke bmi i.behpat2
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3142
{txt}{col 51}LR chi2({res}8{txt}){col 67}= {res}    189.40
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-794.89999{txt}{col 51}Pseudo R2{col 67}= {res}    0.1065

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}chol {c |}{col 14}{res}{space 2} 1.010798{col 26}{space 2} .0015221{col 37}{space 1}    7.13{col 46}{space 3}0.000{col 54}{space 4} 1.007819{col 67}{space 3} 1.013785
{txt}{space 9}sbp {c |}{col 14}{res}{space 2} 1.018203{col 26}{space 2} .0042049{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} 1.009995{col 67}{space 3} 1.026478
{txt}{space 9}age {c |}{col 14}{res}{space 2} 1.062438{col 26}{space 2} .0127373{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} 1.037764{col 67}{space 3} 1.087698
{txt}{space 12} {c |}
{space 7}smoke {c |}
{space 5}smoker  {c |}{col 14}{res}{space 2} 1.828109{col 26}{space 2}  .257801{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} 1.386645{col 67}{space 3} 2.410122
{txt}{space 9}bmi {c |}{col 14}{res}{space 2} 1.057105{col 26}{space 2} .0280768{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} 1.003483{col 67}{space 3} 1.113592
{txt}{space 12} {c |}
{space 5}behpat2 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  .898615{col 26}{space 2}  .252463{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4} .5181212{col 67}{space 3} 1.558533
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.868554{col 26}{space 2} .4926845{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} 1.114469{col 67}{space 3} 3.132877
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.747332{col 26}{space 2} .5578522{col 37}{space 1}    1.75{col 46}{space 3}0.080{col 54}{space 4} .9345907{col 67}{space 3} 3.266853
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 4.89e-06{col 26}{space 2} 4.89e-06{col 37}{space 1}  -12.23{col 46}{space 3}0.000{col 54}{space 4} 6.89e-07{col 67}{space 3} .0000347
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store A1 
{txt}
{com}. logistic chd69 chol sbp age i.smoke bmi
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3142
{txt}{col 51}LR chi2({res}5{txt}){col 67}= {res}    164.59
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-807.30676{txt}{col 51}Pseudo R2{col 67}= {res}    0.0925

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}chol {c |}{col 14}{res}{space 2} 1.010898{col 26}{space 2} .0015077{col 37}{space 1}    7.27{col 46}{space 3}0.000{col 54}{space 4} 1.007947{col 67}{space 3} 1.013857
{txt}{space 9}sbp {c |}{col 14}{res}{space 2} 1.019492{col 26}{space 2} .0041708{col 37}{space 1}    4.72{col 46}{space 3}0.000{col 54}{space 4} 1.011351{col 67}{space 3}   1.0277
{txt}{space 9}age {c |}{col 14}{res}{space 2} 1.066472{col 26}{space 2} .0126965{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} 1.041875{col 67}{space 3} 1.091649
{txt}{space 12} {c |}
{space 7}smoke {c |}
{space 5}smoker  {c |}{col 14}{res}{space 2} 1.882673{col 26}{space 2} .2637221{col 37}{space 1}    4.52{col 46}{space 3}0.000{col 54}{space 4} 1.430671{col 67}{space 3} 2.477479
{txt}{space 9}bmi {c |}{col 14}{res}{space 2} 1.059366{col 26}{space 2} .0279143{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} 1.006044{col 67}{space 3} 1.115514
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.39e-06{col 26}{space 2} 4.28e-06{col 37}{space 1}  -12.65{col 46}{space 3}0.000{col 54}{space 4} 6.49e-07{col 67}{space 3} .0000297
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lrtest A1

{txt}Likelihood-ratio test{col 55}LR chi2({res}3{txt}){col 67}={res}     24.81
{txt}(Assumption: {res}{stata est replay .:.}{txt} nested in {res}{stata est replay A1:A1}{txt}){col 55}Prob > chi2 = {res}   0.0000
{txt}
{com}. * using testparm
. quietly logistic chd69 chol sbp age i.smoke bmi i.behpat2
{txt}
{com}. testparm i.behpat2

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[chd69]2.behpat2 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [chd69]3.behpat2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [chd69]4.behpat2 = 0{p_end}

{txt}{col 12}chi2(  3) ={res}   23.51
{txt}{col 10}Prob > chi2 =  {res}  0.0000
{txt}
{com}. # evaluating dichotmizing behavior pattern
{err}Unknown #command
{com}. logistic chd69 chol sbp age 1.smoke bmi 1.dibpat
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      3142
{txt}{col 51}LR chi2({res}6{txt}){col 67}= {res}    189.16
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-795.01767{txt}{col 51}Pseudo R2{col 67}= {res}    0.1063

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       chd69{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}chol {c |}{col 14}{res}{space 2} 1.010779{col 26}{space 2} .0015212{col 37}{space 1}    7.12{col 46}{space 3}0.000{col 54}{space 4} 1.007802{col 67}{space 3} 1.013765
{txt}{space 9}sbp {c |}{col 14}{res}{space 2} 1.018241{col 26}{space 2} .0041957{col 37}{space 1}    4.39{col 46}{space 3}0.000{col 54}{space 4} 1.010051{col 67}{space 3} 1.026498
{txt}{space 9}age {c |}{col 14}{res}{space 2}  1.06223{col 26}{space 2}  .012712{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} 1.037605{col 67}{space 3}  1.08744
{txt}{space 12} {c |}
{space 7}smoke {c |}
{space 5}smoker  {c |}{col 14}{res}{space 2} 1.826545{col 26}{space 2} .2575497{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} 1.385504{col 67}{space 3} 2.407981
{txt}{space 9}bmi {c |}{col 14}{res}{space 2} 1.056673{col 26}{space 2} .0280322{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} 1.003135{col 67}{space 3} 1.113069
{txt}{space 12} {c |}
{space 6}dibpat {c |}
{space 6}A1,A2  {c |}{col 14}{res}{space 2} 2.008081{col 26}{space 2} .2899056{col 37}{space 1}    4.83{col 46}{space 3}0.000{col 54}{space 4} 1.513191{col 67}{space 3} 2.664826
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.59e-06{col 26}{space 2} 4.50e-06{col 37}{space 1}  -12.54{col 46}{space 3}0.000{col 54}{space 4} 6.72e-07{col 67}{space 3} .0000313
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. lrtest A1

{txt}Likelihood-ratio test{col 55}LR chi2({res}2{txt}){col 67}={res}      0.24
{txt}(Assumption: {res}{stata est replay .:.}{txt} nested in {res}{stata est replay A1:A1}{txt}){col 55}Prob > chi2 = {res}   0.8890
{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/steve/Documents/teaching/c2015/biostat208/labs/lab9/lab9.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 Mar 2015, 15:15:54
{txt}{.-}
{smcl}
{txt}{sf}{ul off}