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From |
"Stephen Soldz" <ssoldz@bgsp.edu> |

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

Subject |
RE: st: Missing outcome variables - how to deal with these? OR moels and reality |

Date |
Sun, 24 May 2009 09:01:59 -0400 |

cases we pretend that the data are MAR, even though we know that is unlikely. But, I think we have to realize that this is a RESEARCH question, not a statistical one. After all, our statistical models typically make all kinds of assumptions (random selection in many cases, normality, etc.), some of which are testable and some of which are not. Even when testable, the tests are often inadequate. Our stayistical models are just guides. The data analyses based upon these models are always flawed. The real question is not "what is the correct model?" But, rather, "is this model close enough to the real world phenomena of interest that I can reasonable trust the results?" The latter question is always one that goes beyond statistics. We apply various supplementary analyses, statistical "tests," knowledge about the likely nature of the data from other experiences, and other knowledge about the real-world phenomena to make such judgments. I believe that we are always doing this. However, we are often not very consciously aware of it. Our language and our publishing traditions militate against it. We talk of "correct models", as if such ever exist. Philosopher Nancy Cartright (How the Laws of Physics Lie) made a strong argument that correct models do not even exist in physics. Jan de Leeuw made related arguments in data analysis. Our publications also encourage talk of such things, despite our awareness that they are, at best, approximations. How many have assumed data is MAR, knowing full well that that was unlikely, but also knowing that publication depends upon that fiction? These issuess are why data analysis is always a craft. We implicitly look for signs that the analyst/researcher is savvy enough to have performed many checks along the way. For example, many sophisticated analysts using such techniques as Structural Equation Modelling or Multilevel Modeling, will usually examine simpler models that are known to be "incorrect," as a check upon the allegedly more "correct", but more complex model. Any major discrepancies are then examined. We then erase the evidence of our doubts from the finished product. So, all of the techniques proposed -- dummy variables for missing (despite known problems), assuming MAR, and using external data -- are potential ways of asssessing the nature of the phenemonon. After all, we are more interested in knowing about the world than bout our models per se. Stephen Soldz Director, Center for Research, Evaluation, and Program Development Boston Graduate School of Psychoanalysis 1581 Beacon St. Brookline, MA 02446 ssoldz@bgsp.edu Date: Sat, 23 May 2009 08:02:55 +0000 (GMT) From: Maarten buis <maartenbuis@yahoo.co.uk> Subject: RE: st: Missing outcome variables - how to deal with these? - --- On Fri, 22/5/09, Tomas M wrote: > For my data, I am quite certain that the data is not missing at random > (NMAR). I have reason to believe that my missing outcome data is > related to the outcome data itself. I do have a full set of > explanatory variables for all of my observations, however. > > Does this mean that I cannot use the typical remedies? What other > options are there for analyzing missing data that is non-ignorable? I have always stayed away from those NMAR models. The problem is that they just can't produce empirical estimates: They critically depend on something that can't be seen. I realise that there are questions out there that are so important that we must just give the best "guesstimate" we can, even though under normal circumstance that best guess would not be considered good enough. Till now I have been able to avoid those questions, so I don't know the answer to your question. - -- Maarten - ----------------------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://home.fsw.vu.nl/m.buis/ - ----------------------------------------- * * 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/ ------------------------------ Date: Sat, 23 May 2009 03:10:58 -0700 (PDT) From: Ana Gabriela Guerrero Serdan <ag_guerreroserdan@yahoo.com> Subject: RE: st: Missing outcome variables - how to deal with these? Dont know if this applies to your type of data but if you have survey data you can first see how much selection for those individuals where you have missing information. How different they are from the rest, compare the rest of the characteristics where you do have some information. So do a test to check this. One thing you would be able do if you have missing values for some of the explanatory variables (which is not your case) is to create a dummy =1 for those variables that you have missing values, in this way you dont loose the observations when you do your analysis, for example, if you do a regression and your outcome variable is education and you want to include an explanatory variable of education of the mother/father but you have missing values here, then you include the dummy that I was mentining before. hope it helps, regards, Gaby - --- On Sat, 5/23/09, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > From: Maarten buis <maartenbuis@yahoo.co.uk> > Subject: RE: st: Missing outcome variables - how to deal with these? > To: statalist@hsphsun2.harvard.edu > Date: Saturday, May 23, 2009, 3:02 AM > > --- On Fri, 22/5/09, Tomas M wrote: > > For my data, I am quite certain that the data is not missing at > > random (NMAR). I have reason to > believe > > that my missing outcome data is related to the outcome > data > > itself. I do have a full set of explanatory > variables > > for all of my observations, however. > > > > Does this mean that I cannot use the typical remedies? What other > > options are there for > analyzing > > missing data that is non-ignorable? > > I have always stayed away from those NMAR models. The problem is that > they just can't produce empirical estimates: They critically depend on > something that can't be seen. I realise that there are questions out > there that are so important that we must just give the best > "guesstimate" we can, even though under normal circumstance that best > guess would not be considered good enough. Till now I have been able > to avoid those questions, so I don't know the answer to your question. > > -- Maarten > > ----------------------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://home.fsw.vu.nl/m.buis/ > ----------------------------------------- ------------------------------ Date: Sat, 23 May 2009 12:36:53 +0200 From: "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it> Subject: R: st: Missing outcome variables - how to deal with these? Dear Tomas, just echoing Maarten's wise and at the same time discouraging remarks: - - even the reference textbook about this topic (Little RJA, Rubin DB. Statistical analysis with missing data. 2nd edition. Chichester: Wiley: 2002) allots few pages to NMAR mechanism (from Subject Index: 12, 13-15, 18-19. - - Hence, the only possible way to deal with NMAR is to rely upon external data sources for similar items (please, see: Ramsey S, Wilke R, Briggs A, et al. Best practices for economic evaluations alongside clinical trials: an ISPOR RCT_CEA Task Force report. Value Health 2005; 8: 521-33). However, the problem seems to go out from the door and come back through the window: it's again a matter of how good are those external sources for your research needs. A possible further advice is to perform some sensitivity analysis after filling in NMAR data and see what happens when you change MNAR guess estimates within a reasonable or customarily range relative to your research field. Kind Regards and enjoy your W-E, Carlo - -----Messaggio originale----- Da: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Maarten buis Inviato: sabato 23 maggio 2009 10.03 A: statalist@hsphsun2.harvard.edu Oggetto: RE: st: Missing outcome variables - how to deal with these? - --- On Fri, 22/5/09, Tomas M wrote: > For my data, I am quite certain that the data is not missing at random > (NMAR). I have reason to believe that my missing outcome data is > related to the outcome data itself. I do have a full set of > explanatory variables for all of my observations, however. > > Does this mean that I cannot use the typical remedies? What other > options are there for analyzing missing data that is non-ignorable? I have always stayed away from those NMAR models. The problem is that they just can't produce empirical estimates: They critically depend on something that can't be seen. I realise that there are questions out there that are so important that we must just give the best "guesstimate" we can, even though under normal circumstance that best guess would not be considered good enough. Till now I have been able to avoid those questions, so I don't know the answer to your question. - -- Maarten - ----------------------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://home.fsw.vu.nl/m.buis/ - ----------------------------------------- * * 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: Missing outcome variables - how to deal with these? OR moels and reality***From:*"jmpsouza" <jmpsouza@usp.br>

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