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From |
Stas Kolenikov <[email protected]> |

To |
[email protected] |

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

Date |
Fri, 3 Dec 2010 10:50:47 -0600 |

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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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/

**Follow-Ups**:**Re: st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <[email protected]>

**References**:**st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <[email protected]>

**Re: st: Multiple imputation for longitudinal data***From:*Stas Kolenikov <[email protected]>

**Re: st: Multiple imputation for longitudinal data***From:*Eduardo Nunez <[email protected]>

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