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From |
Steven Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Artificial censoring in survival analysis |

Date |
Thu, 4 Aug 2011 17:22:51 -0400 |

- I am answering your second question about -hshaz-. There are examples of two and three mass points at the end of the -help-. The mixture model for heterogeneity means that the unobserved log hazard is at one of those points, with locations and probabilities to be estimated. For your earlier question. I don't see a good reason for censoring individuals at 12 months because of problems in observing other individuals. However until you describe your data more fully, then I really don't know. • What kind of study generated the data. A prospective cohort?. A cross-section with retrospective recall? • Was the study a complex sample, so that there are weights and clusters (PSUs)? • What is the purpose of YOUR analysis? • What was the larger data set, if any, from which you took your specific data. What criteria did you use for inclusions? • What is month "1"? a calendar month, a month of an interview? The first month of unemployment? • Did unemployment start before month "1" for everybody or some people? After month 1? • For those who started before month "1", do you know how long they had been unemployed? • What do you mean people were "younger" to experience the event? Did you mean "too young" to qualify as unemployed at the start? • Why do you have information on some people for more than 12 months but not for others? How did observation end. • Have you information on people who were employed but became unemployed during the study period (perhaps not in the data set you describe below. In short we need a complete description of the study design and the beginning and endinfg of observation. Dear statalisters, I am doing a project on duration of unemployment. I want to compare models with and without unobserved heterogeneity. I want to use -hshaz- module to estimate a mixture model but I couldn't find example on how to do that. I will appreciate any help where to find examples. Thanks, Melaku On Aug 2, 2011, at 3:25 AM, melaku.fekadu@gmail.com wrote: Hello statalisters, I analyze employment data using survival method for a length of 12 months. I decided to do so because some of my observations are younger to experience the event (in this case exiting unemployment) for more than 12 months; that is I observe them only for 12 months. To overcome this problem I imposed a 12 months period of analysis for all of my observations. That is all observations have equal length of 12 months to experience the event. I did so by artificially censoring those observations for whom I have data for more than 12 months and did not experience the event within 12 months. These are old individuals. I did censor even though I see some of these observations experience the event later, after the 12 months period. My questions: 1. Should I include in the analysis those observations that I censored? 2. Is the sample data presented below appropriate for survival analysis? Note that all of observations experience the event except those I censored at the 12 month. Below is a small representation of my data. The failure variable 'Failure' is cross-tabulated with the variable 'studytime' which is the number of months until experiencing the event. Failure 0 | 1 ------ 1 0 | 200 2 0 | 89 3 0 | 70 5 0 | 68 6 0 | 58 7 0 | 50 8 0 | 51 10 0 | 45 11 0 | 30 12 150 | 0 Thanks, Melaku * * 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: Artificial censoring in survival analysis***From:*Melaku Fekadu <melaku.fekadu@gmail.com>

**References**:**st: Artificial censoring in survival analysis***From:*melaku.fekadu@gmail.com

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