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From |
Clara Barata <maria_barata@mail.harvard.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multiple imputation for longitudinal data |

Date |
Fri, 3 Dec 2010 14:34:01 -0500 |

Hi Eduardo, A possible (albeit possibly limited) first approach would be to arrange the data in wide rather than stacked format and impute that way using Stata's MI. I think this is what Stas meant when he mentioned the need to first -reshape- your data to make it one line for each patient. Clara On Fri, Dec 3, 2010 at 12:20 PM, Eduardo Nunez <enunezb@gmail.com> wrote: > > Based on what you wrote, I imagine Stata hasn't implemented these > methods ( that utilize the > monotonicity of monotonicity). > Would you guide me to the software that has these estimation methods. > Do you know if it is implemented in R? > > > Best regards, > > Eduardo > > > > On Fri, Dec 3, 2010 at 11:50 AM, Stas Kolenikov <skolenik@gmail.com> wrote: > > > > What I am saying is that there are estimation methods that utilize the > > monotonicity of monotonicity. Of course you can the existing methods > > to produce something sensible. The monotone options, however, are > > designed to work across the data set, not along the data set, so you > > would want to -reshape- your data to make it one line for each > > patient. > > > > On Fri, Dec 3, 2010 at 10:34 AM, Eduardo Nunez <enunezb@gmail.com> wrote: > > > Thank you, Stas. > > > I can handle the monotone missing pattern either using ICE or MI > > > impute while specifying the monotone option. > > > But my question goes further on how to account for clustering on > > > patient ID (which is the cluster unit)?. > > > Should I impute data separately for each patient? or include pteID > > > variable in the imputation model? > > > > > > I appreciate any help. > > > > > > Eduardo > > > > > > > > > > > > > > > On Thu, Dec 2, 2010 at 6:39 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > > >> You have monotone missing data, and you would most likely be better > > >> off utilizing the methods for monotone missing data rather than > > >> bluntly rely on multiple imputation. Check Little and Rubin's book on > > >> missing data, chapter 7 (in the 2nd edition). > > >> > > >> On Thu, Dec 2, 2010 at 5:11 PM, Eduardo Nunez <enunezb@gmail.com> wrote: > > >>> Dear Statalisters, > > >>> > > >>> I have Stata 11.1 (MP - Parallel Edition). > > >>> > > >>> I am interested in performing multiple imputation on a longitudinal > > >>> data (on several variables with a percent of missing between 1-15%), > > >>> were subjects are the cluster units with few observations in time. > > >>> See below the data structure: > > >>> > > >>> xtdes, pattern(1000) > > >>> > > >>> pid: 1, 2, ..., 1438 n = 1432 > > >>> visit: 1, 2, ..., 12 T = 12 > > >>> Delta(visit) = 1 unit > > >>> Span(visit) = 12 periods > > >>> (pid*visit uniquely identifies each observation) > > >>> > > >>> Distribution of T_i: min 5% 25% 50% 75% 95% max > > >>> 1 1 1 2 3 6 12 > > >>> > > >>> Freq. Percent Cum. | Pattern > > >>> ---------------------------+-------------- > > >>> 650 45.39 45.39 | 1........... > > >>> 359 25.07 70.46 | 11.......... > > >>> 202 14.11 84.57 | 111......... > > >>> 91 6.35 90.92 | 1111........ > > >>> 52 3.63 94.55 | 11111....... > > >>> 44 3.07 97.63 | 111111...... > > >>> 11 0.77 98.39 | 1111111..... > > >>> 9 0.63 99.02 | 11111111.... > > >>> 6 0.42 99.44 | 111111111... > > >>> 4 0.28 99.72 | 1111111111.. > > >>> 3 0.21 99.93 | 11111111111. > > >>> 1 0.07 100.00 | 111111111111 > > >>> ---------------------------+-------------- > > >>> 1432 100.00 | XXXXXXXXXXXX > > >>> > > >>> The article included in Stata FAQ ("How can I account for clustering > > >>> when creating imputations with mi impute?") suggested using a > > >>> "multivariate > > >>> normal model to impute all clusters simultaneously" or strategy 3, > > >>> although mentioned that is best suited to balanced repeated-measures > > >>> data. > > >>> > > >>> Clearly, my data is not balanced. Moreover, the percent of data > > >>> missing increased as patient follow-up gets far from baseline. > > >>> > > >>> Is there any other method suited for this type of longitudinal data? > > >>> If not, how stringent is the limitation of not being balanced. > > >>> > > >>> Please, any help is welcome! > > >>> > > >>> > > >>> Eduardo > > >>> * > > >>> * 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/ > > >>> > > >> > > >> > > >> > > >> -- > > >> Stas Kolenikov, also found at http://stas.kolenikov.name > > >> Small print: I use this email account for mailing lists only. > > >> > > >> * > > >> * 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/ > > > > > > > > > > > -- > > Stas Kolenikov, also found at http://stas.kolenikov.name > > Small print: I use this email account for mailing lists only. > > > > * > > * 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/ * * 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/

**References**:**st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <enunezb@gmail.com>

**Re: st: Multiple imputation for longitudinal data***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <enunezb@gmail.com>

**Re: st: Multiple imputation for longitudinal data***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <enunezb@gmail.com>

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