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From |
Eduardo Nunez <enunezb@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Biomarker with lower detection limits |

Date |
Wed, 18 Nov 2009 18:04:27 -0500 |

Thank you so much for your help. Even though I know the DL, no idea about the biomarker distribution. I may try all options suggested. However, I would like to know if Stata perform a hurdle model? Best wishes, Eduardo ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This sounds like an application for a two-part or hurdle model: You want to compare the frequency of below detection limit in two populations (or to a standard) and the continuous variable above the detection limit. My inclination is NOT to use imputation - you already know these are below the detection limit, so why impute something larger than that? I've been wary of Tobit models since I read somewhere (and I don't remember where, darn it) that they are quite sensitive to the normality assumption. Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 On Tue, Nov 17, 2009 at 1:24 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- On Tue, 17/11/09, Lachenbruch, Peter wrote: >> My inclination is NOT to use imputation - you already know >> these are below the detection limit, so why impute something >> larger than that? > > Alternatively, you could use multiple imputation, as long as > your imputation model respects this information you have > about your variable. This is the kind of problem Patrick > Royston seems to had in mind when writing this update to his > -ice- command: > > Patrick Royston (2007) Multiple imputation of missing values: > further update of ice, with an emphasis on interval censoring. > The Stata Journal, 7(4):445-464. > http://www.stata-journal.com/article.html?article=st0067_3 > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://www.maartenbuis.nl > -------------------------- On Tue, Nov 17, 2009 at 2:08 PM, Austin Nichols <austinnichols@gmail.com> wrote: > Eduardo Nunez <enunezb@gmail.com> : > An alternative approach to consider: make a dummy variable that is one > when your biomarker is nonzero. Then run a probit, logit, or > alternative model using the dummy for "above the detection limit" as > the outcome. You can also use that dummy as an explanatory variable in > stcox, possibly also with a second variable measuring values above the > detection limit. Do you know the detection limit? Do you know > details about the physical process that would tell you about the > distribution of these measurements conditional on some X? If so, you > can write a -ml- routine using that information. > > On Tue, Nov 17, 2009 at 9:01 AM, Eduardo Nunez <enunezb@gmail.com> wrote: >> Dear statalisters: >> >> I wonder if anyone can advise me on the best way to analyse continuous >> variables with lower detection limits (or left censored). >> In particular, I have data on a biomarker with 92% of values reported >> "undetectable" and I am trying to run 2 models: >> 1) a linear regression using it as dependent variables, and >> 2) stcox with mortality as outcome and the biomarker as the main exposure. >> >> >> . tab cpies_DNA_max, m >> >> cpies_DNA | >> _max | Freq. Percent Cum. >> ------------+----------------------------------- >> 0 | 121 91.67 91.67 >> 2.85 | 1 0.76 92.42 >> 4.721 | 1 0.76 93.18 >> 5.059 | 1 0.76 93.94 >> 5.165 | 1 0.76 94.70 >> 6.267 | 1 0.76 95.45 >> 8.009 | 1 0.76 96.21 >> 9.965 | 1 0.76 96.97 >> 30.538 | 1 0.76 97.73 >> 35.137 | 1 0.76 98.48 >> 50 | 1 0.76 99.24 >> 71.227 | 1 0.76 100.00 >> ------------+----------------------------------- >> Total | 132 100.00 >> >> >> Censored values occur in enviromental, metabolomics, proteomics data >> most commonly when the level of a biomarker in a sample is less than >> the limit of quantification of the machine; these values are generally >> reported as being less than detectable with the detection limit (DL) >> being specified (for instances "< than 2.5"). >> There has been proposed several solutions like to replaces those >> values with zeros, or DL, or DL/2 or a random value from a >> distribution over the range from zero to DL. However, any of them have >> been demonstrated to be optimal in simulation studies. >> What I don't want is to eliminate those values and run the analysis on >> complete cases. >> Is it possible to use multiple imputation for replacing those values? >> If this is an option, how can I tell the imputation method not to find >> values bove the DL? >> Is tobit an appropriate model for the fist analysis? because of marked >> skewness, should I normalize the variable by transforming only the >> values above DL? >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Biomarker with lower detection limits***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**References**:**st: Biomarker with lower detection limits***From:*Eduardo Nunez <enunezb@gmail.com>

**Re: st: Biomarker with lower detection limits***From:*Austin Nichols <austinnichols@gmail.com>

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