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Re: st: Test for trend for SIR


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:
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>


-- 
-------------------------------------------------------
Raoul C. Reulen
Cancer Research UK Training Fellow
*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
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*   http://www.ats.ucla.edu/stat/stata/



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