Re: st: svyset when looking at children of respondents

 From Steven Samuels To statalist@hsphsun2.harvard.edu Subject Re: st: svyset when looking at children of respondents Date Sat, 25 Oct 2008 18:26:16 -0400

```On Oct 25, 2008, at 5:11 PM, Ergo, Alex wrote:

```
```Thanks a lot for your advice, Steve! It's extremely helpful.
```
There's just one point in your response I'm not sure I understand. What do you mean when you write that each child will inherit the probability weight of his/her mother? Do I not need to adjust the weights to account for the fact that the number of children included in the analysis will vary across women?
No. The survey programs will automatically take the number of children into account. If you were selecting a child at random from each woman's birth history you might want to re-weight.
Also, would you agree that, in your first suggested approach, I should keep women without children in the dataset and use the 'subpop' option with the svyset command?
```
```
I disagree. I would keep only the women with children in the data set, and I would not use -subpop-. I -subpop- only for descriptive studies; your study has an analytic purpose. (See WE Deming, Some Theory of Sampling", Dover Reprint, 1984). In essence, you are conditioning on the population of women with births and, also, on their covariate values.
```
```
```Once again, many thanks for your help.
Alex
```
You are welcome. You haven't told us much about your study question. However, if I were doing your analysis, I would first ignore the survey design and fit multi-level models with -xtmelogit-. I would then use results from these analyses as initial parameter estimates in -gllamm-. With -svy: logistic- approach, you will get no information about between-woman variation in the probability of an infant death. (This might be available from other sources). You run the risk that the number of births is related to the probability of infant death. Sample sizes should be uninformative, but pregnancy outcomes may not be.
```
```
I also withdraw my suggestion to include prior birth outcomes as covariates. Higher numbers may be predictive of greater risk solely because they reflect the mother's inherently greater risk. Also, the effects of exposures may be may be changed. If an exposure has already increased the risk of a prior infant death, its *additional* effect on a current pregnancy will be reduced. This is a tricky area to interpret: Is risk related to birth order? To the number of children already in the house? Perhaps. But,these predictors may themselves be outcomes related to infant death. Luckily, JHSPH has fine reproductive epidemiologists and statisticians to advise you.
```
-Steve
```
```________________________________________
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From: owner-statalist@hsphsun2.harvard.edu [owner- statalist@hsphsun2.harvard.edu] On Behalf Of Steven Samuels [sjhsamuels@earthlink.net]
```Sent: Friday, October 24, 2008 2:21 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: svyset when looking at children of respondents

Alex:

Too start off,  each child will inherit the probability weight,
stratum, and cluster assignments of its mother.

I see two ways of accounting for clustering by mother:

1. Add mother as a last stage of "sampling" in your -svyset- command,
but with no finite population correction. See the -svyset- help or
the Stata Survey manual.  Use Stata's -svy: logit- command.

2. Fit a multilevel logistic model (level 1: child, level 2: mother)
ucbbiostat/paper160). -gllamm- accepts probability weights and a
cluster unit above than the highest level in your model. -gllamm- is
not a -svy- enabled program, so you cannot use the stratum
information in the survey design. However you can use stratum-level
covariates. Stas Kolenikov has a demo at http://www.unc.edu/~skolenik/
stata/gllamm-demo.html

If you are interested in conditional, rather than marginal,
predictions, you might choose to ignore the survey sampling weights
altogether.

I recommend the -glamm- option. With -glamm- you will be able to
model woman-level effects as fixed and random.

You should be aware of a potential bias in selecting the births for
your study data. Women may prefer to end their pregnancies with a
successful one (in some places, perhaps, with a successful male
birth). If this is the case, you should exclude a woman's last birth
from your data.   To guard against this problem, you may also include
as a covariate the outcomes of prior pregnancies and births. I would
not recommend this if you are interested in marginal, rather than
conditional, prediction. (-gllamm- will do both kinds.)

If you want to use Stata's -svy- commands, and you are combining
multiple surveys, there are other issues. I suggest that you create
"super-strata" which cross countries or survey periods with the
within-survey strata.

Good luck!

-Steve

On Oct 24, 2008, at 12:44 PM, Ergo, Alex wrote:

```
```Dear A
I'm working with large population surveys. The individuals
interviewed are women of
reproductive age. Among many other things, the respondents provide
information relating
to their children. All this information is stored in the
respondent's record.
I would like to run some logistic regressions with infant mortality
as dependent
variable (1 if child died within the month following birth; 0
otherwise). In order
to create this dependent variable, I need to reshape the dataset
from wide to long
so as to have one live birth per record. I do not consider all the
children for
which information is available, but only those born up to 10 years
before the
mother's interview date.
In this situation, what is the most appropriate approach to account
for the complex
survey design? I thought of using the svyset command, but I'm not
sure how.  More
particularly, should I account for the clustering of live births at
the level of the
respondent and for the fact that respondents who did not have any
live birth in the
last 10 years are omitted from the regression analysis? If so, how?
the sample weights when more than one child is from the same mother?
I'm using STATA 9.2.
I hope someone can help me with this.  Thanks in advance!
Alex

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