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From |
"raoul reulen" <r.c.reulen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 13 Nov 2007 09:52:30 +0000 |

Hi Roland, I had the same problem with the command smrby. I changed the ado-file and now it works. I think the problem is to do with the weights, they need to be integer. I changed 'fweight' into 'iweight' and now it works. You don't need to collapse on the incidence variable, but you do need to calculate the expected number before you collapse the data. The model based option is based on a Wald test and should give you similar results. Hope this helps Raoul On 13/11/2007, roland andersson <rolandersson@gmail.com> wrote: > 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 > > > 2007/10/18, raoul reulen <r.c.reulen@gmail.com>: > > Hi > > > > I'm not exactly sure what you want to do, but the title of the message > > suggests you want to do a test for linear trend accros several SIRs. > > What I normally do is use smrby.ado because it includes a test for > > trend and heterogeneity. Alternatively, you might want to model the > > SIRs by using a GLM model with a Poisson error structure and then fit > > the factor of interest as a consecutive non-negative integer variable. > > Make sure you collapse your data before you model it. > > > > .collapse (sum) _d E pyrs, by(timeperiod) > > .xi, noomit:glm _d i.timeperiod if E!=0,fam(pois) lnoffset(E) eform noconstant > > > > gives you the SIRs > > > > .xi, noomit:glm _d timeperiod if E!=0,fam(pois) lnoffset(E) eform noconstant > > > > gives you the p-value for linear trend > > > > _d is the numner of events, E the number of expected. The p-value for > > timeperiod should give you the test for linear trend accross the > > groups. > > > > > > Hope it helps > > > > Raoul Reulen > > Cancer Research UK Graduate Training Fellow > > > > > > > > > > > > On 17/10/2007, roland andersson <rolandersson@gmail.com> wrote: > > > I have used strate.ado to calculate standardised incidence ratios with > > > standardisation for age, sex and timepriod. I want to make inferences > > > on the development of the SIRs over three timeperiods. Can you help me > > > with instruction how to do this? > > > * > > > * 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/ > > > > > > > > > -- > > ------------------------------------------------------- > > Raoul C. Reulen > > Cancer Research UK Training Fellow > > * > > * 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/ > > > * > * 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/ > -- ------------------------------------------------------- Raoul C. Reulen Cancer Research UK Training Fellow * * 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/

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

**RE: st: Test for trend for SIR***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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

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