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Survey and correlated data

Stata’s svy: prefix now works with

Twenty-five other commands also now support estimation with survey data.

You just declare the survey design for your data by using svyset, and then declare your data to be survival-time data by using stset. Here’s an example:

    . use http://www.stata-press.com/data/r10/nhefs
    . svyset psu2 [pw = swgt2], strata(strata2)
    . stset age_lung_cancer if age_lung_cancer < . [pw = swgt2], fail(lung_cancer)
    . svy: stcox former_smoker smoker male urban1 rural
svyset example

We could just as easily have fitted a parametric survival regression model simply by replacing svy:stcox with svy:streg.

Here’s a complete list of what’s new in statistics(survey)

  1. Stata’s svy: prefix now works with 48 estimators, 27 more than previously. Other commands with which svy: now works include

    biprobit bivariate probit regression
    clogit conditional (fixed effects) logistic regression
    cloglog complementary log-log regression
    cnreg censored-normal regression
    cnsreg constrained linear regression
    glm generalized linear models
    hetprob heteroskedastic probit regression
    ivregress instrumental-variables regression
    ivprobit probit model with endogenous regressors
    ivtobit tobit model with endogenous regressors
    mprobit multinomial probit regression
    nl nonlinear least-squares estimation
    scobit skewed logistic regression
    slogit stereotype logistic regression
    stcox Cox proportional hazards regression
    streg parametric survival regression (five estimators)
    tobit tobit regression
    treatreg treatment-effects model
    truncreg truncated regression
    zinb zero-inflated negative binomial regression
    zip zero-inflated Poisson regression
    ztnb zero-truncated negative binomial regression
    ztp zero-truncated Poisson regression

    See [SVY] svy estimation.
  2. svy: prefix now calculates the linearized variance estimator two to 100 times faster, the larger multiplier applying to large datasets with many sampling units; see [SVY] svy.
  3. svy: mean, svy: proportion, svy: ratio, and svy: total are considerably faster when the over() option identifies many subpopulations.
  4. svy:, svy: mean, svy: proportion, svy: ratio, and svy: total now take advantage of multiple processors in Stata/MP, making them even faster.
  5. Concerning svyset,

    1. New option singleunit(method) provides three methods for handling strata with one sampling unit. If not specified, the default in such cases is to report standard errors as missing value.
    2. New option fay(#) specifies that Fay’s adjustment be made to the BRR weights.
    See [SVY] svyset.
  6. estat has two new subcommands for use with svy estimation results:

    1. estat sd, used after svy: mean, reports subpopulation standard deviations.
    2. estat strata reports the number of singleton and certainty strata within each sampling stage.
    See [SVY] estat.
  7. svy: tabulate now allows string variables. See [SVY] svy: tabulate oneway and [SVY] svy: tabulate twoway.
  8. Existing command svydes has been renamed svydescribe; svydes continues to work.
    svydescribe now puts missing values in the generate(newvar) variable for observations outside the specified estimation sample. Previously, the variable would contain a zero for observations outside the estimation sample. See [SVY] svydescribe.
  9. The [SVY] manual has been reorganized. Stata’s survey estimation commands are now documented in [SVY] svy estimation. All model-specific information is now documented in the manual entry for the corresponding estimation command.

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