Hello, i hope you can maybe help me.
I am trying to do a IPTW survival analysis adherent to the paper of Seisen et al. I use Stata/MP 14.0
https://www.researchgate.net/profile...-Data-Base.pdf
I first did a multiple imputation and subsequently, iptw. I created a graph with a comparison of values before and after weighting with the kernel density. After that, i basically did a cox regression test. My code is below. treatement in this case is "cn":
mi set flong
mi register imputed pt pn
mi impute chained (ologit) pt pn, add (15) replace augment noisily
mi xeq: logit cn; predict ps0, pr
mi xeq: logit cn age gender histo sarcomatoid pt pn mskcc; predict ps1 , pr
mi xeq: gen ipw= cn*(ps0/ps1) + ((1-cn)*((1-ps0)/(1-ps1)))
tempvar fw
gen `fw' = round((n*1000),1)
twoway (kdensity ps1 if cn==0 [fw=`fw'], bw(0.02) lpattern(solid) lwidth(medthick) lcolor("0 64 115")) (kdensity ps1 if cn==1 [fw=`fw'] , bw(0.02) lpattern(solid) lwidth(medthick) lcolor("220 169 31")), xtitle("Propensity Score", size (medlarge)) xlabel(0.4 0.6 0.8 1) ytitle("Kernel Density", size (medlarge)) ylabel(0 2 4 6 8 10, angle (horizontal)) legend(order (1 2) label(1 "No CN") label(2 "CN") cols (1)) legend(pos(2) ring(0)) graphregion(fcolor(white)) xtitle("propensity scores BEFORE matching") saving(before, replace)
tempvar fw
gen `fw' = round((ipw*1000),1)
twoway (kdensity ps1 if cn==0 [fw=`fw'], bw(0.02) lpattern(solid) lwidth(medthick) lcolor("0 64 115")) (kdensity ps1 if cn==1 [fw=`fw'] , bw(0.02) lpattern(solid) lwidth(medthick) lcolor("220 169 31")), xtitle("Propensity Score", size (medlarge)) xlabel(0.4 0.6 0.8 1) ytitle("Kernel Density", size (medlarge)) ylabel(0 2 4 6 8 10, angle (horizontal)) legend(order (1 2) label(1 "No CN") label(2 "CN") cols (1)) legend(pos(2) ring(0)) graphregion(fcolor(white)) xtitle("propensity scores AFTER matching") saving(after, replace)
graph combine before.gph after.gph, ycommon
mi stset timedeath [pweight = ipw], failure (css==1)
sts test cn
Problems:
1) Unfortunately, i have no idea how to do the rest of the graphs in the paper that my supervisor insists on. I looked everywhere, but can not find them. In paarticular, i dont know how to do the iptw-adjusted hazard ratio forrest plot, and Fig. A1 to depict the balancing through weighting.
2) I tried to do the standardised differences before and after Weighting. However, it does not seem to make a lot of difference:
Before:
. stddiff gender i.smoking histo sarcomatoid i.pt i.pn i.mskcc, by (cn) abs
------------------------------------------------------------------------------
| cn=0 | cn=1 |
| Mean or N SD or (%) | Mean or N SD or (%) | Std Diff
-------------+-------------------------+-------------------------+------------
gender | .2848 .45276 | .2308 .42337 | 0.12329
| | |
smoking | | |
1 | 19 (12.0) | 15 (14.4) | 0.20418
2 | 37 (23.4) | 19 (18.3) |
3 | 7 ( 4.4) | 2 ( 1.9) |
4 | 95 (60.1) | 68 (65.4) |
| | |
histo | .1772 .38306 | .1827 .38829 | 0.01420
sarcomatoid | .01899 .13691 | .1442 .35302 | 0.46778
| | |
pt | | |
0 | 43 (33.9) | 17 (16.5) | 0.61733
1 | 31 (24.4) | 13 (12.6) |
2 | 47 (37.0) | 63 (61.2) |
3 | 6 ( 4.7) | 10 ( 9.7) |
| | |
pn | | |
0 | 64 (52.5) | 27 (27.8) | 0.91122
1 | 48 (39.3) | 27 (27.8) |
2 | 10 ( 8.2) | 43 (44.3) |
| | |
mskcc | | |
0 | 48 (30.4) | 11 (10.6) | 0.56903
1 | 89 (56.3) | 64 (61.5) |
2 | 21 (13.3) | 29 (27.9) |
------------------------------------------------------------------------------
After:
------------------------------------------------------------------------------
| cn=0 | cn=1 |
| Mean or N SD or (%) | Mean or N SD or (%) | Std Diff
-------------+-------------------------+-------------------------+------------
gender | .2848 .45141 | .2308 .42145 | 0.12375
| | |
smoking | | |
1 | 304 (12.0) | 240 (14.4) | 0.20418
2 | 592 (23.4) | 304 (18.3) |
3 | 112 ( 4.4) | 32 ( 1.9) |
4 | 1520 (60.1) | 1088 (65.4) |
| | |
histo | .1772 .38193 | .1827 .38653 | 0.01425
sarcomatoid | .01899 .13651 | .1442 .35143 | 0.46980
| | |
pt | | |
0 | 803 (32.2) | 276 (16.6) | 0.54292
1 | 573 (22.9) | 211 (12.7) |
2 | 983 (39.4) | 1016 (61.1) |
3 | 138 ( 5.5) | 160 ( 9.6) |
| | |
pn | | |
0 | 1236 (49.6) | 474 (28.6) | 0.74497
1 | 953 (38.2) | 470 (28.4) |
2 | 303 (12.2) | 713 (43.0) |
| | |
mskcc | | |
0 | 768 (30.4) | 176 (10.6) | 0.56903
1 | 1424 (56.3) | 1024 (61.5) |
2 | 336 (13.3) | 464 (27.9) |
------------------------------------------------------------------------------
Do i miss something? Do i have to do the std differently to properly assess the effect of iptw?
3) How does weighting affect the median survival exactly? SInce after iptw, sts test automatically swithes to a cox regression instead of a log rank, i was not able to calculate the statistical difference in median survival
(Code: sts test cn if r(p50), logrank) <-- "logrank" was deleted for the weighted values.
I know i have a lot of questions. I tried to read it up myself, but as a not formally trained statistician, without help i dont know where to start or where to turn to.
i hope you can help me and am already greatful for every bit of advice.
Kind regards,
Florian
I am trying to do a IPTW survival analysis adherent to the paper of Seisen et al. I use Stata/MP 14.0
https://www.researchgate.net/profile...-Data-Base.pdf
I first did a multiple imputation and subsequently, iptw. I created a graph with a comparison of values before and after weighting with the kernel density. After that, i basically did a cox regression test. My code is below. treatement in this case is "cn":
mi set flong
mi register imputed pt pn
mi impute chained (ologit) pt pn, add (15) replace augment noisily
mi xeq: logit cn; predict ps0, pr
mi xeq: logit cn age gender histo sarcomatoid pt pn mskcc; predict ps1 , pr
mi xeq: gen ipw= cn*(ps0/ps1) + ((1-cn)*((1-ps0)/(1-ps1)))
tempvar fw
gen `fw' = round((n*1000),1)
twoway (kdensity ps1 if cn==0 [fw=`fw'], bw(0.02) lpattern(solid) lwidth(medthick) lcolor("0 64 115")) (kdensity ps1 if cn==1 [fw=`fw'] , bw(0.02) lpattern(solid) lwidth(medthick) lcolor("220 169 31")), xtitle("Propensity Score", size (medlarge)) xlabel(0.4 0.6 0.8 1) ytitle("Kernel Density", size (medlarge)) ylabel(0 2 4 6 8 10, angle (horizontal)) legend(order (1 2) label(1 "No CN") label(2 "CN") cols (1)) legend(pos(2) ring(0)) graphregion(fcolor(white)) xtitle("propensity scores BEFORE matching") saving(before, replace)
tempvar fw
gen `fw' = round((ipw*1000),1)
twoway (kdensity ps1 if cn==0 [fw=`fw'], bw(0.02) lpattern(solid) lwidth(medthick) lcolor("0 64 115")) (kdensity ps1 if cn==1 [fw=`fw'] , bw(0.02) lpattern(solid) lwidth(medthick) lcolor("220 169 31")), xtitle("Propensity Score", size (medlarge)) xlabel(0.4 0.6 0.8 1) ytitle("Kernel Density", size (medlarge)) ylabel(0 2 4 6 8 10, angle (horizontal)) legend(order (1 2) label(1 "No CN") label(2 "CN") cols (1)) legend(pos(2) ring(0)) graphregion(fcolor(white)) xtitle("propensity scores AFTER matching") saving(after, replace)
graph combine before.gph after.gph, ycommon
mi stset timedeath [pweight = ipw], failure (css==1)
sts test cn
Problems:
1) Unfortunately, i have no idea how to do the rest of the graphs in the paper that my supervisor insists on. I looked everywhere, but can not find them. In paarticular, i dont know how to do the iptw-adjusted hazard ratio forrest plot, and Fig. A1 to depict the balancing through weighting.
2) I tried to do the standardised differences before and after Weighting. However, it does not seem to make a lot of difference:
Before:
. stddiff gender i.smoking histo sarcomatoid i.pt i.pn i.mskcc, by (cn) abs
------------------------------------------------------------------------------
| cn=0 | cn=1 |
| Mean or N SD or (%) | Mean or N SD or (%) | Std Diff
-------------+-------------------------+-------------------------+------------
gender | .2848 .45276 | .2308 .42337 | 0.12329
| | |
smoking | | |
1 | 19 (12.0) | 15 (14.4) | 0.20418
2 | 37 (23.4) | 19 (18.3) |
3 | 7 ( 4.4) | 2 ( 1.9) |
4 | 95 (60.1) | 68 (65.4) |
| | |
histo | .1772 .38306 | .1827 .38829 | 0.01420
sarcomatoid | .01899 .13691 | .1442 .35302 | 0.46778
| | |
pt | | |
0 | 43 (33.9) | 17 (16.5) | 0.61733
1 | 31 (24.4) | 13 (12.6) |
2 | 47 (37.0) | 63 (61.2) |
3 | 6 ( 4.7) | 10 ( 9.7) |
| | |
pn | | |
0 | 64 (52.5) | 27 (27.8) | 0.91122
1 | 48 (39.3) | 27 (27.8) |
2 | 10 ( 8.2) | 43 (44.3) |
| | |
mskcc | | |
0 | 48 (30.4) | 11 (10.6) | 0.56903
1 | 89 (56.3) | 64 (61.5) |
2 | 21 (13.3) | 29 (27.9) |
------------------------------------------------------------------------------
After:
------------------------------------------------------------------------------
| cn=0 | cn=1 |
| Mean or N SD or (%) | Mean or N SD or (%) | Std Diff
-------------+-------------------------+-------------------------+------------
gender | .2848 .45141 | .2308 .42145 | 0.12375
| | |
smoking | | |
1 | 304 (12.0) | 240 (14.4) | 0.20418
2 | 592 (23.4) | 304 (18.3) |
3 | 112 ( 4.4) | 32 ( 1.9) |
4 | 1520 (60.1) | 1088 (65.4) |
| | |
histo | .1772 .38193 | .1827 .38653 | 0.01425
sarcomatoid | .01899 .13651 | .1442 .35143 | 0.46980
| | |
pt | | |
0 | 803 (32.2) | 276 (16.6) | 0.54292
1 | 573 (22.9) | 211 (12.7) |
2 | 983 (39.4) | 1016 (61.1) |
3 | 138 ( 5.5) | 160 ( 9.6) |
| | |
pn | | |
0 | 1236 (49.6) | 474 (28.6) | 0.74497
1 | 953 (38.2) | 470 (28.4) |
2 | 303 (12.2) | 713 (43.0) |
| | |
mskcc | | |
0 | 768 (30.4) | 176 (10.6) | 0.56903
1 | 1424 (56.3) | 1024 (61.5) |
2 | 336 (13.3) | 464 (27.9) |
------------------------------------------------------------------------------
Do i miss something? Do i have to do the std differently to properly assess the effect of iptw?
3) How does weighting affect the median survival exactly? SInce after iptw, sts test automatically swithes to a cox regression instead of a log rank, i was not able to calculate the statistical difference in median survival
(Code: sts test cn if r(p50), logrank) <-- "logrank" was deleted for the weighted values.
I know i have a lot of questions. I tried to read it up myself, but as a not formally trained statistician, without help i dont know where to start or where to turn to.
i hope you can help me and am already greatful for every bit of advice.
Kind regards,
Florian
