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From |
"Oliver Eger" <oliver.eger@yahoo.de> |

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

Subject |
st: Questions: 1. irregular interval censoring | 2. discrete data: ties/exactp/cox/cloglog/"flat region...r(430)"| 3. left censoring/ truncation/ others |

Date |
Tue, 25 May 2010 11:14:20 +0200 |

Dear Stata users, I ?am new to Stata and to the newsgroup. I would kindly ask for advice, concerning my work on survival analysis. I first want to post my questions. A more detailed description of my data and research follows below. Questions: 1.) My study time is 1900 till 2000. This is also the enrollment period. Due to my definitions (see below, summary), the onset of risk for each company is the year of first mentioning. This means, according to my opinion, that my data is neither left censored nor left truncated. But I assume that at least some companies existed before 1900, without having exact information about this. ==> Are there any alternatives to tread the left borders of my data instead of doing this by the definitions I do (see below, summary) ? 2.) My data are interval censored and interval truncated. But the intervals are of irregular period in calendar time. ==> Are there any methods to deal with this irregularities or at least to estimate their influence on my regressions? 3.) The discrete character of my data causes many ties. As mentioned below, Cox (efron or exactp) and cloglog models give very similar results. When I do regressions not only with parts but with my complete company data set, using stcox and the exactp option, I got an error message: ?flat region resulting in a missing likelihood r(43o)? ==> Are there any alternative algorithms/ ado files, calculating the coefficients/ hazardrates as the exactp option would do it? Summary: (i) - I collected company data on a particular submarket. Time span is about 100 years. My source only allowed company observation every two or three years (points of ?principle observability?). Also there are extra gaps due to war and source related reasons. So I have irregular intervals of observing in calendar time. - In addition, companies are sometimes unobservable at points of observability, that means, they are not mentioned in my source. The reasons for this are unknown. It could be due to a careless registration policy to the source, or maybe the company doesn?t work in my particular submarket for a certain time span, but only in the subordinated market. Nevertheless they could be observed in the next or one of the next periods. - My sources starts around 1900. I assume that at least some companies existed before, but I do not have exact information about this. It ends around 2000. On the background of the facts reported, I defined the following for my survival analysis: (ii) - For the survival analysis I put the data in the long or multi id form. - When I observed a company in one year, I defined it to exist the whole year, starting at 1st of January and ending 31st December. - Due to the long form, I have got as many observations per company (id) as years of mentions in my source, every observation starting at the 1st of January and ending on 31st December. - In the absence of more exact information (see (i)), I defined a company to enter the market/ survival analysis in the first year of mentioning in my source. Failure = exit occurs in the last year of mentioning. - This is done by: stset exit, failure(failure==1) id(id) origin(time entry) time0(entry) On this basis, I would like to estimate a Cox model with some independent variables influencing the survival time of my companies. Because of the discrete character of my data, the method of choice should be estimating a cloglog model (see the script of Jenkins or Hosmer/Lemeshow, Applied Survival Analysis, Chapter 7), but using Cox model in stata, efron or exactp option for ties and the stset command as above, stcox gives very similar results (see also Hosmer/ Lemeshow, Chapter 7) . ================================================= Oliver Eger Lehrstuhl für VWL - Innovationsökonomik Universität Augsburg Universität Hohenheim Prof. Dr. Horst Hanusch - Prof. Dr. Andreas Pyka ================================================= * * 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/

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