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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Modelling the passage of time: OLS vs survival analysis |

Date |
Fri, 05 Oct 2007 11:36:17 +0100 |

In this situation, my guess is that you don't absolutely need the -st- commands, but they could be tried out. I am fuzzy about how bad it is to have some zeros. I can't see that OLS -- or rather anything based on an assumption of normal distributions -- is at all attractive. Manifestly your response is constrained to be zero or positive and right up against the limit of 0. Alternatively, you could try fitting some non-normal distribution directly, with and without your covariates. Although some might be queasy about it, given the nature of your data, -poisson- might be a reasonable thing to try. More generally, consider various kinds of -glm- or related commands. Absent any censoring, what is best depends on what kind of variability you have. Nick n.j.cox@durham.ac.uk Christer Thrane My dependent variable, gathered from a survey, is the variable "time (measured in weeks) from from one started thinking about "X" until one reached a decision about X." Also, everybody in the data reached this decision. The data structure looks like this, and time ranges between 0 and 52 weeks (mean = 5.2 weeks): id gender age time ... 1 0 30 7 ... 2 0 45 5 ... 3 1 36 0 ... 4 1 27 44 ... and so on. Q1: Is it theoretically meaningfull to think of this variable as suiteble for survival analysis (SA), or is this "a job" for OLS regression Q2: Provided that SA is meaningful, how should I set up the data in ths instance? * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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