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From |
"Harland Austin" <hdaustin@bellsouth.net> |

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

Subject |
RE: st: Test for trend for SIR |

Date |
Tue, 13 Nov 2007 18:55:16 -0500 |

Roland, Rather than change the ado file, you can change E (the expected number of events) to an integer, - replace E= round(100*E) and then - smrby D E, by(timeperiod) trend -. The trend test is still appropriate since it depends on the relative, not the absolute, sizes of the timeperiod groupings. Hope this helps. I have individual data with multiple records per patient after I have split the follow up time depending on year and age and have merged a variable for the incidence rate per 100.000 personyears. With the command xi:strate i.timeperiod,smr(inc) per (100000) I get the following result +-----------------------------------------------------------+ timeperiod D E SMR Lower Upper ----------------------------------------------------------- 1 7 0.43 16.102 7.677 33.777 2 5 1.65 3.032 1.262 7.284 3 14 2.11 6.642 3.934 11.215 +-----------------------------------------------------------+ I want to test for the linear trend over the timeperiods. If I try the smrby on this result I get: . smrby D E, by(timeperiod) trend Observed Expected -- Poisson Exact -- timeperiod D E O/E (%) [95% Conf. Interval] 1 7 0.4300 1627.9*** 655 3354 2 5 1.6500 303.0 98 707 3 14 2.1100 663.5*** 363 1113 may not use noninteger frequency weights r(401); Why do I get this error message? According to your second choice I use the following model on this result. glm D timeperiod, family(poisson) lnoffset(E) eform Iteration 0: log likelihood = -9.6767233 Iteration 1: log likelihood = -9.5071336 Iteration 2: log likelihood = -9.5068971 Iteration 3: log likelihood = -9.5068971 Generalized linear models No. of obs = 3 Optimization : ML Residual df = 1 Scale parameter = 1 Deviance = 7.236772128 (1/df) Deviance = 7.236772 Pearson = 6.884656839 (1/df) Pearson = 6.884657 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 7.671265 Log likelihood = -9.506897131 BIC = 6.13816 OIM D IRR Std. Err. z P>z [95% Conf. Interval] timeperiod .7561818 .2108008 -1.00 0.316 .4378616 1.305917 E (exposure) ie there is no linear trend p=0.316 Is this a correct use of the glm model? Or can I use some other method on the original dataset? If I collapse the dataset what happens with the incidensvariable which should not be aggregated but stay the same. Or do I have to collapse the dataset and merge the incidensvariable after the collapse? Roland Andersson * * 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/

**References**:**Re: st: Test for trend for SIR***From:*"roland andersson" <rolandersson@gmail.com>

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