[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Michael McCulloch <mm@pinest.org> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
st: dropped observations in bootstrap |

Date |
Sat, 6 Oct 2007 23:07:41 -0700 |

Hello, I'd appreciate some guidance on this problem.

I'm running a weighted Cox regression within a bootstrap program.

If I conduct the analysis program, step by step outside the bootstrap it runs just fine.

However, when I conduct the exact same analysis within the bootstrap program, I get the error message:

"Bootstrap replications (50)

----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 50

insufficient observations to compute bootstrap standard errors

no results will be saved

r(2000);"

What I'd like to learn is: how can I discover what happened to those observations during the bootstrap program? Note that these observations are not dropped during the manual analysis outside the bootstrap program.

I've illustrated my code below, using an adapted version of Stata's system data file cancer.dta.

Note: PART 1 I'm simply setting up cancer.dta for my analysis. My code in question is PART 2 below.

* PART 1

sysuse cancer, replace

ren died failed

tab drug

gen newdrug=0

replace newdrug=1 if drug==2|drug==3

drop drug

rename newdrug drug

tab drug

gen yeartreated=2007

gen monthtreated=10

gen daytreated=05

gen addyear=0

replace addyear=1 if studytime>=12 & studytime<24

replace addyear=2 if studytime>=24 & studytime<36

replace addyear=3 if studytime>=36

gen addmonths=0

replace addmonths=studytime-12 if studytime>=12 & studytime<24

replace addmonths=studytime-24 if studytime>=24 & studytime<36

replace addmonths=studytime-36 if studytime>36

gen yeardied=2007

replace yeardied= yeartreated + addyear if addyear!=0

list yeartreated yeardied addyear

gen monthdied=10

replace monthdied = monthtreated + addmonths if addmonths!=0

list monthtreated monthdied addmonths

replace yeardied=yeardied+1 if monthdied>=12 & monthdied<24

gen newmonthdied=monthdied

replace newmonthdied=monthdied-12 if monthdied>=12 & monthdied<24

replace newmonthdied=monthdied-24 if monthdied>=24

drop monthdied

gen monthdied = newmonthdied

drop newmonthdied

gen daydied=05

replace monthdied=monthdied+studytime if studytime<12

replace yeardied=yeardied+1 if monthdied>12

replace monthdied =monthdied-12 if monthdied>12

gen datedx = mdy(monthtreated, daytreated, yeartreated)

format datedx %d

gen datedied = mdy(monthdied, daydied, yeardied)

format datedied %d

sort datedied

list datedx datedied studytime

* PART 2

capture program drop msm

program define msm, rclass

* fit the full model used in Traditional Cox

logit drug age

*estimate probability of treatment

predict p_tcm_ALL

gen p_notcm_ALL = 1-p_tcm_ALL if drug==0 //want p(drug) for both users and nonusers

replace p_tcm_ALL =p_notcm_ALL if drug==0

*create stabilized weight==P(A)/P(A|W)

logit drug

predict p_ALL

gen p_noALL=1-p_ALL if drug==0

replace p_ALL = p_noALL if drug==0

gen wt_stab_ALL=p_ALL/p_tcm_ALL

* stset the data

stset datedied [iweight=wt_stab_ALL], failure(failed) origin(datedx) scale(30.4375) /*wt: stabilized IPTW*/

* cox

stcox drug

indeplist, local

foreach var of varlist `X' {

return scalar `var' = _b[`var']

}

end set seed 12358

bootstrap drug=r(drug), eform reps(50): msm

display exp(-1.995311)

*

* For searches and help try:

* http://www.stata.com/support/faqs/res/findit.html

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: dropped observations in bootstrap***From:*Maarten buis <maartenbuis@yahoo.co.uk>

- Prev by Date:
**Re: st: re: line patterns within Kaplan Maier** - Next by Date:
**Re: st: line patterns within Kaplan Meier** - Previous by thread:
**st: re: line patterns within Kaplan Maier** - Next by thread:
**Re: st: dropped observations in bootstrap** - Index(es):

© Copyright 1996–2014 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |