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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: right censoring of dependent and independent variable |

Date |
Fri, 30 May 2008 09:56:03 -0400 |

Steven Samuels <sjhsamuels@earthlink.net>: I'm afraid I don't understand this claim, since I don't agree with it. Are you saying that if you observe X=M (so X>=M but the true value is not observed) and more than 50% of observations on Y are uncensored at X=M, you can estimate the conditional median of Y given true (unobserved) X? On Fri, May 30, 2008 at 8:55 AM, Steven Samuels <sjhsamuels@earthlink.net> wrote: > > There is -some- information. If for X > M (censoring value), P% of > observations on Y are uncensored, you can estimate up to the P-th quantile > of Y for X>M. For anything more, you need very strong assumptions about the > distribution of X for X>M, about E(Y|X) for X>M, or both. > > > -Steve > > On May 30, 2008, at 4:52 AM, Garry Anderson wrote: > >> I was using fractional polynomials and so non-linearities are likely. It >> seems unfortunate that observations of predictors with right censored >> values need to be deleted from the analysis because I would think there >> is at least some information in the censored value. >> >> Best wishes, Garry >> >> -----Original Message----- >> From: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis >> Sent: Thursday, May 29, 2008 10:44 PM >> To: statalist@hsphsun2.harvard.edu >> Subject: Re: st: right censoring of dependent and independent variable >> >> --- Garry Anderson <g.anderson@unimelb.edu.au> wrote: >>> >>> I have data where both the dependent and independent variable are >>> right censored and continuous. Although intreg, cnreg or tobit can >>> handle censoring of the dependent variable, they do not seem to allow >>> censoring of the independent variable. Both the instruments can only >>> measure to a maximum value, 30% of values are right censored on the Y >>> variable and 15% on the X variable. >>> >>> I would welcome suggestions as to how to incorporate right censoring >>> of the independent variable? >> >> In the graph below you can see that selection on the x variable is much >> less of a problem than selection on the y variable. A part of the data >> is turned into influential outliers, by selecting on the y. In the >> example it is the upper right part of the observed values that pull the >> regression line down. However, the same does not happen when you select >> on x. With regression you model the mean of y conditional on x, the fact >> that you don't observe all values of x, is unfortunate (loss of >> power) but not disastrous. Things become obviously more complicated when >> you are interested in any non-linearities in the effect of x. >> >> Hope this helps, >> Maarten >> >> *----------------------- begin example ------------------------ clear >> set seed 12345 matrix C = (1, .5 \ .5, 1) drawnorm x y, n(1000) corr(C) >> twoway scatter y x if x < 1, aspect(1) xline(1) || /// >> scatter y x if x >= 1, msymbol(oh) mcolor(gs10) || /// >> lfit y x , lpattern(solid) lcolor(green) || /// >> lfit y x if x < 1, lpattern(solid) lcolor(red) /// >> title(selection on x) name(x, replace) /// >> legend(order( 1 "observed" /// >> 2 "censored" /// >> 3 "true" /// >> 4 "estimated") /// >> rows(2)) >> >> >> >> twoway scatter y x if y < 1, aspect(1) yline(1) || /// >> scatter y x if y >= 1, msymbol(oh) mcolor(gs10) || /// >> lfit y x , lpattern(solid) lcolor(green) || /// >> lfit y x if y < 1, lpattern(solid) lcolor(red) /// >> title(selection on y) name(y, replace) /// >> legend(order( 1 "observed" /// >> 2 "censored" /// >> 3 "true" /// >> 4 "estimated") /// >> rows(2) colfirst) >> >> >> grc1leg y x >> *------------------------ end example --------------------------- (For >> more on how to use examples I sent to the Statalist, see >> http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) >> >> For this example to run you need to download the -grc1leg- package, >> see: -findit grc1leg-. >> >> >> ----------------------------------------- >> Maarten L. Buis >> Department of Social Research Methodology Vrije Universiteit Amsterdam >> Boelelaan 1081 >> 1081 HV Amsterdam >> The Netherlands >> >> visiting address: >> Buitenveldertselaan 3 (Metropolitan), room Z434 >> >> +31 20 5986715 >> >> http://home.fsw.vu.nl/m.buis/ >> ----------------------------------------- >> >> >> __________________________________________________________ >> Sent from Yahoo! Mail. >> A Smarter Email http://uk.docs.yahoo.com/nowyoucan.html >> * >> * 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/ >> >> * >> * 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/ > > * > * 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/ > * * 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: right censoring of dependent and independent variable***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**st: right censoring of dependent and independent variable***From:*Garry Anderson <g.anderson@unimelb.edu.au>

**Re: st: right censoring of dependent and independent variable***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**RE: st: right censoring of dependent and independent variable***From:*Garry Anderson <g.anderson@unimelb.edu.au>

**Re: st: right censoring of dependent and independent variable***From:*Steven Samuels <sjhsamuels@earthlink.net>

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