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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
st: RE: Identifying coherent periods of events with irregular reoccurrence from a time sequence |

Date |
Wed, 25 Feb 2009 14:49:35 -0000 |

Gap calculation is easy enough, especially once you have -tsset- data (in this case with a pseudo-time variable which is just number in sequence for each patient). In meteorology and climatology periods of similar conditions are often known as spells, as may be familiar from media reports, and that terminology likes behind 1. -tsspell- on SSC and 2. SJ-7-2 dm0029 . . . . . . . . . . . . . . Speaking Stata: Identifying spells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox Q2/07 SJ 7(2):249--265 (no commands) shows how to handle spells with complete control over spell specification -- which, surprising though it may seem, are quite disjoint. That is, I started out with a vague intention to write an article about -tsspell-, but explaining the principles seemed much more important and in the end I did not get to it. A quite different approach is possible using -group1d- on SSC. Your data get chopped up like this: . group1d day, max(6) Partitions of 12 data up to 6 groups 1 group: sum of squares 3.2e+05 Group Size First Last Mean SD 1 12 1 0 12 461 241.50 163.74 2 groups: sum of squares 32480.23 Group Size First Last Mean SD 2 7 6 293 12 461 372.71 58.77 1 5 1 0 5 114 57.80 40.75 3 groups: sum of squares 12761.55 Group Size First Last Mean SD 3 3 10 407 12 461 434.00 22.05 2 4 6 293 9 363 326.75 27.39 1 5 1 0 5 114 57.80 40.75 4 groups: sum of squares 6395.92 Group Size First Last Mean SD 4 3 10 407 12 461 434.00 22.05 3 4 6 293 9 363 326.75 27.39 2 2 4 89 5 114 101.50 12.50 1 3 1 0 3 57 28.67 23.27 5 groups: sum of squares 3743.67 Group Size First Last Mean SD 5 3 10 407 12 461 434.00 22.05 4 2 8 342 9 363 352.50 10.50 3 2 6 293 7 309 301.00 8.00 2 2 4 89 5 114 101.50 12.50 1 3 1 0 3 57 28.67 23.27 6 groups: sum of squares 2511.00 Group Size First Last Mean SD 6 3 10 407 12 461 434.00 22.05 5 2 8 342 9 363 352.50 10.50 4 2 6 293 7 309 301.00 8.00 3 2 4 89 5 114 101.50 12.50 2 2 2 29 3 57 43.00 14.00 1 1 1 0 1 0 0.00 0.00 Groups Sums of squares 1 321729.00 2 32480.23 3 12761.55 4 6395.92 5 3743.67 6 2511.00 The help gives a detailed explanation. You need to spell out, pun intended, your definitions of coherent and incoherent for those like me who are unfamiliar with drug studies. Nick n.j.cox@durham.ac.uk Jakob Petersen I have a problem identifying compliant vs. non-compliant periods in patients' prescription history. I assume that this type of problems is known from other areas with temporal sequences (climate, transactional, reoffending, etc.). How would it be possible to flag and number a) coherent periods of prescriptions; b) gaps; c) beginning dates; and d) end dates to each period? Example (with two clearly separated periods): order day 1 0 2 29 3 57 4 89 5 114 6 293 7 309 8 342 9 363 10 407 11 434 12 461 * * 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: Identifying coherent periods of events with irregular reoccurrence from a time sequence***From:*"Jakob Petersen" <j.petersen@ucl.ac.uk>

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