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Re: st: Test for trend for SIR


From   "roland andersson" <rolandersson@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Test for trend for SIR
Date   Tue, 13 Nov 2007 16:48:47 +0100

Thanks Raoul.

This works and gives almost the same result as the modelbased aproach
- p=0.314 with smrby and p=0.316 with the modelbased.

Roland

2007/11/13, raoul reulen <r.c.reulen@gmail.com>:
> 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
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> > > >
> > >
> > >
> > > --
> > > -------------------------------------------------------
> > > Raoul C. Reulen
> > > Cancer Research UK Training Fellow
> > > *
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> > > *   http://www.ats.ucla.edu/stat/stata/
> > >
> > *
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> >
>
>
> --
> -------------------------------------------------------
> Raoul C. Reulen
> Cancer Research UK Training Fellow
> *
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>
*
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