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Meta-analysis
In medical disciplines, such as oncology or cardiology, many studies of the
same disease or treatment are performed. Meta-analysis is the use of
statistical techniques to combine results from different studies, and many
user-written commands have been produced in this area, including commands
for
tests for heterogeneity,
cumulative pooled estimates,
meta-analysis regression,
tests for publication bias,
funnel plots,
forest plots, and
L’Abbe plots.
Treatment effects
Did participants in a training program obtain wages higher than their peers
who did not participate? Treatment-effects estimators are used to measure
the impact of an event, controlling for confounding factors such as age,
gender, or level of education. Because of their frequent use, particularly
in economics, many of these estimators are available in Stata via
user-written commands. Nearest-neighbor and propensity-score matching
commands exist, as do commands for evaluating the sensitivity of the
estimators to violations of various assumptions, and commands for extensions
of the basic model to multinomial treatments in which subjects could have
received one of several alternative treatments.
Output generation
Ultimately, we need to communicate our results to others, and researchers
typically do this by presenting tables of summary statistics and
estimation results. Different disciplines and journals have their own
styles, and an array of user-written commands for producing output exists
to satisfy virtually all tastes. Whether you write reports in Word or LaTeX,
or you want to transfer output to Excel spreadsheets, a user-written command
likely exists to fit your needs.
Limited dependent-variable models
Not all dependent variables are continuous. Some are binary. Some are
ordered. Some represent counts. Some are censored. Some are subject to
sample selection. While Stata includes a spectrum of commands to handle
such variables, the number of existing models is overwhelming and continues
to grow. Fortunately, Stata’s built-in capability for programming maximum
likelihood estimators makes implementing new models straightforward for
user-programmers. Scores of user-written commands for limited
dependent-variable models are now available for cross-sectional, panel, and
multilevel datasets.
Survival analysis
The focus of survival analysis is to model the amount of time required for
an event to occur. Stata’s built-in survival analysis commands are widely
recognized to be among the best in the industry, and practitioners have
written additional commands to round out Stata’s offerings. Many
user-written commands are available for cure and relative-risk models,
discrete-time proportional-hazards models, and
flexible parametric models.
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Data management
When starting a research project, the data are almost never in the form you
would like. Stata’s built-in data-management facilities are renowned,
but you may come across a dataset that requires a custom level of
manipulation beyond what you think Stata can do. Another Stata user has
probably faced the same problem already and has made available a command to
do the “heavy lifting”. Whether you need to convert data from a
GIS program, manipulate value labels in your dataset, apply a linear filter,
or create a complicated indicator variable, a user-written command is
probably available to help.
Multilevel and correlated data
Pupils are clustered within classrooms, which are clustered within schools,
which are clustered within school districts. Consumers are clustered within
neighborhoods, which are nested within towns, which are nested within
metropolitan areas. Many datasets have observations that are nested within
one or more larger groupings. Ignoring the correlations inherent in such
data can result in inefficient or biased results. In addition to Stata’s
built-in commands for multilevel mixed-effects linear regression, logit, and
Poisson models, many user-written commands exist for multilevel data.
Econometrics
Econometricians frequently develop new estimators and tests, which are then
implemented by Stata users. A variety of user-written commands is
available for instrumental-variables estimation, panel-data unit-root tests,
inequality measurement, and wage decompositions, to mention just a few areas
of development.
Statistical graphs
Stata’s flexible graphics engine has motivated users to develop a
variety of statistical graphs. Whether you need a specialized regression
diagnostic plot to analyze the fit of your model, a plot of the cumulative
distribution of a variable, a cycle plot to examine seasonality, a spine
plot of two-way categorical data, a Bland–Altman plot to compare two
assays, or a choropleth to map the spatial distribution of poverty, another
Stata user has probably written the command you need.
More
Spatial data visualization. Marginal effects. Univariate and multivariate
statistical tests. The range of user-written commands available is as
diverse as the people who use Stata. Regardless of your field of study,
there are user-written commands that will complement your Stata experience.
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