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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 30 May 2008 10:44:40 -0400 |

Right. I don't see how to use those cases. On May 30, 2008, at 10:21 AM, Austin Nichols wrote:

Oh, okay. Now it makes sense. But if you were running a parametric model, you would want to exclude topcoded cases (X=M observed), right? On Fri, May 30, 2008 at 10:12 AM, Steven Samuels <sjhsamuels@earthlink.net> wrote:No, I meant that you can estimate the median of Y for that part of the X,Y

population with X > M. That's not much.

On May 30, 2008, at 9:56 AM, Austin Nichols wrote:

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:

In the graph below you can see that selection on the x variable is much

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?

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

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**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>

**Re: st: right censoring of dependent and independent variable***From:*"Austin Nichols" <austinnichols@gmail.com>

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

**Re: st: right censoring of dependent and independent variable***From:*"Austin Nichols" <austinnichols@gmail.com>

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