## Abstracts

### Scenario comparisons: How much good can we do?

Applied scientists, especially public health scientists, frequently want to
know how much good can be caused by a proposed intervention. For instance,
they might want to estimate how much we could decrease the level of a
disease, in a dream scenario where the whole world stopped smoking, assuming
that a regression model fitted to a sample is true. Alternatively, they may
want to compare the same scenario between regression models fitted to
different datasets, as when disease rates in different subpopulations are
standardized to a common distribution of gender and age, using the same
logistic regression model with different parameters in each subpopulation.
In statistics, scenarios can be defined as alternative versions of a
dataset, with the same variables, but with different values in the
observations or even with noncorresponding observations. Using
regression methods, we may estimate the scenario means of a *Y*-variable in
scenarios with specified *X*-values and compare these scenario means. In
Stata Versions 11 and 12, the standard tool for estimating scenario means is
**margins**. A suite of packages is introduced for estimating scenario
means and their comparisons using **margins** together with **nlcom** to
implement Normalizing and variance–stabilizing transformations.
**margprev** estimates scenario prevalences for binary variables.
**marglmean** estimates scenario arithmetic means for non-negative valued
variables. **regpar** estimates two scenario prevalences, together with
their difference, the population attributable risk (PAR). **punaf**
estimates two scenario arithmetic means from cohort or cross-sectional data,
together with their ratio, the population unattributable fraction (PUF),
which is subtracted from 1 to give the population attributable fraction
(PAF). **punafcc** estimates an arithmetic mean between-scenario rate
ratio for cases or nonsurvivors in case–control or survival data,
respectively. This mean rate ratio, also known as a PUF, is also subtracted
from 1 to estimate a PAF. These packages use the log transformation for
arithmetic means and their ratios, the logit transformation for prevalences,
and the hyperbolic arctangent or Fisher’s *z* transformation for
differences between prevalences. Examples are presented for these packages.

**Additional materials:**

uk12_newson.pdf

### Robustness for dummies

In the robust statistics literature, a wide variety of models has been developed to cope with outliers in a rather large number of scenarios. Nevertheless, a recurrent problem for the empirical implementation of these estimators is that optimization algorithms generally do not perform well when dummy variables are present. What we propose in this paper is a simple solution to this involving the replacement of the subsampling step of the maximization procedures by a projection-based method. This allows us to propose robust estimators involving categorical variables, be they explanatory or dependent. Some Monte Carlo simulations are presented to illustrate the good behavior of the method.

**Additional materials:**

uk12_verardi_gassner_ugarte.pdf

### stgenreg: A Stata package for general parametric survival analysis

We present the Stata package **stgenreg** for the parametric analysis of
survival data. Any user-defined hazard or log hazard function can be
specified, with the model estimated using maximum likelihood utilizing
numerical quadrature. Standard parametric models (for example, the Weibull proportional
hazards model and generalized gamma accelerated failure time model) can be
fitted; however, the real advantage of the approach is the ability to fit
parametric models not available in Stata or other software. Examples will
include modeling the log hazard by using fractional polynomials and spline
functions, fitting complex time-dependent effects, a generalized gamma model
with proportional hazards, and generalized accelerated failure time models.
An extensive range of prediction tools are also described.

**Additional materials:**

uk12_crowther_lambert.pdf

### A Stata module for estimating dose response treatment models under (continuous) treatment endogeneity and heterogeneous response to observable confounders

Following in the footsteps of the Stata user-written command
**ivtreatreg**, recently proposed by the author (Cerulli, 2012), the paper
presents a new Stata routine—** contreatreg**—for estimating
a Dose Response Treatment Model under *continuous* treatment
endogeneity and heterogeneous response to confounders. Compared with similar
models—and in particular the one proposed by Hirano and Imbens (2004)
implemented in Stata by Bia and Mattei (2008)—this model does not need
the normality assumption; it is well suited when many individuals have a
zero-level of treatment, and it accounts for treatment endogeneity by
exploiting a two-step instrumental-variables (IV) estimation. The model
considers two groups: 1) untreated, whose level of the treatment (or
*dose*) is zero; and 2) treated, whose level of the treatment is
greater than zero. Treated units' outcome *y* responds to treatment by
a function *h(t)*, assumed to have a flexible polynomial form.
**contreatreg** estimates the model’s dose response function, which
is shown to be equal to the average treatment effect, given the level of
treatment *t* (that is, ATE(*t*)), along with other causal parameters
of interest, such as the ATE, ATET, ATENT, and ATE(*x; t*). An application
on real data will be provided along with the command’s ado and help files.

**Additional materials:**

uk12_cerulli.pdf

### Graphics (and numerics) for univariate distributions

How to plot (and summarize) univariate distributions is a staple of introductory data analysis. Graphical (and numerical) assessment of marginal and conditional distributions remains important for much statistical modeling. Research problems can easily evoke needs for many comparisons, across groups, across variables, across models, and so forth. Over several centuries, many methods have been suggested, and their relative merits are a source of lively ongoing debate. I offer a selective but also detailed review of Stata functionality for univariate distributions. The presentation ranges from official Stata commands through various user-written commands, including some new programs, to suggestions on how to code your own graphics commands when other sources fail. I also discuss both continuous and discrete distributions. The trade-off between showing detail and allowing broad comparisons is an underlying theme.

**Additional materials:**

uk12_cox.ppt

### Producing animated graphs from Stata without having to learn any specialized software

Much interest has been focused on animated graphical displays of data in
recent years, although this mostly involves some expertise with specialized
software and programming. There is a lack of simple tools for data analysts
to use to produce animations. In this presentation, I will show how movie
files can be produced as stop-frame animations using Stata graphs as the
building blocks. This approach is extremely flexible, and I will give some
examples, including morphing from start to finish locations and the (ab)use
of animation, color, and sound for emphasis. Some potential applications for
teaching will be discussed. The principle of creating a sequence of
transitional images through a loop, then calling the freeware, open-source
**ffmpeg** software via **winexec** or **shell** will be explained
with do-file examples. For repeated applications, the whole process can be
contained within an ado-file, which raises the possibility of interactive
websites with Stata, producing bespoke animations.

**Additional materials:**

uk12_grant.pptx

### How to get an edge with margins and marginsplot

Visualizing the true effect of a predictor over a range of values can be
difficult for models that are not parameterized in their natural metric,
such as for logistic or (even more so) probit models. Interaction terms in
such models cause even more fogginess. In this talk, I show how both the
**margins** and the **marginsplot** commands can make for much clearer
explanations of effects for both nonstatisticians and statisticians alike.

**Additional materials:**

uk12_rising.pdf

### pscore2: Stata module to enforce balancing score property in each covariate dimension

Propensity score matching has become a popular empirical method because of its
capability of reducing the dimensionality of finding comparable units to
conditioning on a scalar quantity. The validity of this approach relies on
the balancing property of the propensity score. In practice, this is
verified by using statistical tests along with subclassification. Within
Stata, this is implemented by the program **pscore** provided by Becker
and Ichino (2002). However, **pscore** is not constructive regarding
the correct specification of the propensity score model, nor does it
facilitate the actual requirement of covariate balance. The command
**pscore2** overcomes these drawbacks. It determines a set of intervals on the
respective scalar-dimensional support of the propensity score with respect
to the criterion that within each interval statistical similarity of
covariates for treated and control observations cannot be rejected for a
user-specified probability of a type-I error. Therefore, **pscore2**
implements a grid-search algorithm that updates the testing interval until
convergence to the largest subinterval where covariate balance holds is
achieved. The provided options allow for testing higher-order equivalence of
each of the marginal covariate distributions for treated and controls.
Furthermore, **pscore2** automatically distinguishes between continuous
and binary regressors and can handle nonvarying covariates.

**References**

Becker, S.O. and A. Ichino. 2002. Estimation of average treatment effects
based on propensity scores. *Stata
Journal* 2: 358–377.

**Additional materials:**

uk12_dorn.pdf

### Experiences with multiple propensity score matching

This presentation shows a somewhat complex automatization scheme in Stata that was developed during preparation of two papers using firm-level data and applying the propensity score matching techniques to distill the direct effects of the presence of foreign investors on various indicators from selection effects. The problem involved running multiple propensity score matching estimation procedures on different group of firms and on different efficiency measures. The solution involves 1) multiple nested loops to provide standardized output for several combinations of measures and groups; 2) correction of the standard PSM procedures to provide all required standard errors in the “returns”; and 3) use of postfiles to create user-friendly results sets that permit both reporting tables and generating publishable figures. The presentation also discusses alternative approaches that may be used to tackle similar problems.

**Additional materials:**

uk12_hagemejer_tyrowicz.ppt

### diff: Simplifying the causal inference analysis with difference-in-differences

Most of the microeconometrics studies are being based on the causal
inference analysis. **diff** provides to the researcher an easy-to-use
tool to perform the difference-in-differences estimation from a two-period
panel dataset designed for an impact evaluation. It combines the conditional
independence of the outcome and the treatment given unobservable
characteristics that do not vary over time with the use of observable
covariates. **diff** is endowed with four estimands: the single
diff-in-diff; diff-in-diff accounting for covariates; the kernel propensity
score diff-in-diff, which allows the selection of the bandwidth, the use of
probit or logit, the provision of the propensity score, and the estimation on
the common support; and the quantile diff-in-diff at the specified quantile,
which is also available for continuous outcomes and it is combinable with
the kernel option. Finally, option **test** performs the balancing test
of covariates between treatment and comparison groups in the base-line
period, generating a simple table based on Stata’s **ttest**.

**Additional materials:**

uk12_villa.pdf

### predcumi: A postestimation command for predicting and visualizing cumulative incidence estimates after Cox regression models

In the presence of competing risks, calculation of cumulative incidence
should provide a more realistic assessment of the probabilities of the event
of interest conditional on covariates than provided by either the
Kaplan–Meier failure probabilities or the event probabilities predicted directly
from the Cox regression model. Enzo Coviello has previously provided
user-written Stata programs that calculate either crude cumulative incidence
estimates over time (**stcompet**) or cumulative incidence estimates over
time adjusted to some user-specified values of covariates
(**stcompadj**), which are useful for making between-group comparisons
but have limitations for evaluating the individual risk predictions.

I will describe the motivation behind a new postestimation command
(**predcumi**) that facilitates the calculation and visualization of
cumulative incidence estimates after Cox regression models, calculated based
on each individual’s covariate patterns or optionally with flexible
adjustment of covariates to user-specified values or means or percentiles of
the covariate distribution. The most recently fitted Cox model is assumed to
be for the event of interest, and given the user’s specification of the
competing event, the cumulative incidence calculations are based on
cause-specific hazards estimated from Cox regressions. Examples will be
provided and comparisons made with the previous user-written programs and
Stata’s official implementation of competing risks models based on the Fine
and Gray model formulation (**stcrreg**).

**Additional materials:**

uk12_kaptoage.ppt

### Numerical integration with an application in sample size reestimation

We introduce a new **integrate()** function for Mata that evaluates
single-dimensional integrals. This function uses three different Gaussian
quadrature algorithms: Gauss–Hermite and Gauss–Laguerre for
indefinite integrals; and Gauss–Legendre for definite integrals. The
algorithms were implemented using the methods of Golub and Welsch (1968).
The user can specify any integrand by defining a new function in the Mata
language. The integrand function is allowed to have two arguments: the first
is the variable of integration, and the second is a real scalar. Thus the
**integrate()** function can be used in combination with
**optimise()** to solve for the value of *x* in the following
expression:

Such calculations are used in the sample size re-estimation methodology introduced by Li, Shih, and Xie (2002). We apply these methods to a clinical trial where a single interim analysis is carried out, and the analysis is used to reevaluate the sample size.

**References**

Golub, G.H. and J. H. Welsch. 1969. Calculation of Gauss quadrature rules.
*Mathematics of Computation* 23: 221–230.

Li, G., W. Shih, and T. Xie. 2002.
A sample size adjustment procedure for clinical trials based on conditional power.
*Biostatistics* 3: 277–287.

**Additional materials:**

uk12_mander_bowden.pdf

### A more versatile sample size calculator

I present a new Stata command, **simsam**, that uses simulation to
determine the sample size required to achieve given power for any method of
analysis under any probability model that can be programmed in Stata
(**simsam** assumes that code for generating a single dataset and
analyzing it can be found in a separate program). Thus **simsam** extends
Stata’s **sampsi** command. It is straightforward to estimate the
power of a statistical analysis for a given sample size by simulation: you
simply run the analysis repeatedly on simulated data and see how often the
result is statistically significant. Determining the sample size that
achieves given power is slightly harder, requiring power to be assessed at
different sample sizes in order to find the one at which the target power is
attained. **simsam** uses a novel iterative algorithm that is more
efficient than stepping consecutively through every possible sample size.
The user specifies the precision of the final estimate of power, but
initially the algorithm uses less precision in order to make more rapid
progress. The algorithm aims for the smallest sample size (or the smallest
multiple of 5, or 10, or other user-specified increment) such that the
power, estimated to within the specified precision, exceeds the target
power. The power is reported with a 99% confidence interval, and the final
sample size obtained is reliable in the sense that if the **simsam**
command is repeated (by the same user or by a reviewer checking the
calculation), it will, nearly every time, give a sample size no more than
one increment away.

**Additional materials:**

uk12_hooper.pdf

### Life on the inside: Experiences as a StataCorp intern

I report briefly—and without giving away any Stata (or state) secrets—on my experiences as an intern at StataCorp earlier in 2012.

**Additional materials:**

uk12_crowther.pdf

### Handling interactions in Stata, especially with continuous predictors

In an era in which doctors and patients aspire to personalized medicine and more sophisticated risk estimation, detecting and modeling interactions between covariates or between covariates and treatment is increasingly important. In observational studies (for example, in epidemiology), interactions are known as effect modifiers; their presence can substantially change the understanding of how a risk factor impacts the outcome. However, modeling interactions in an appropriate and interpretable way is not straightforward.

In our talk, we consider two related topics. The first topic is modeling
interactions in observational studies that involve at least one continuous
covariate, an area that practitioners apparently find difficult. We
introduce a new Stata program, **mfpigen**, for detecting and modeling
such interactions using fractional polynomials, adjusting for confounders if
necessary. The second topic is modeling interactions between treatment
and continuous covariates in randomized controlled trials. We outline a
Stata program, **mfpi**, designed for this purpose. Key themes of our talk
are the vital role played by graphical displays of interactions and the
importance of applying simple plausibility checks.

**Additional materials:**

uk12_royston_sauerbrei.pdf

### rego: Stata module for decomposing goodness of fit according to Owen and Shapley values

Decomposition of the goodness of fit to (groups of) regressor variables can
be a useful diagnostic tool to quickly assess “relative importance”.
Owen and Shapley values, two closely related solutional concepts in
cooperative game theory, provide unique solutions to the decomposition
exercise on the basis of a sound set of assumptions. At this stage, the new
command **rego** implements decomposition of *R*-squared in OLS regression,
based on the covariance matrix of the data for fast computation in the Mata
environment. It also allows for bootstrapping the outcomes. Inclusion of
other measures of fit and classes of models is planned for future
extensions.

**Additional materials:**

uk12_sunder.pdf

### Enhancing the application of extreme-bounds analysis in Stata

This presentation shows and applies a new user-written Stata command,
**enhancedeba**, that facilitates the extreme bounds analysis (EBA)
methodologies proposed by Leamer (1983, 1985) and Sala-i-Martin (1997).
This command is useful for robustness checks and determining whether
relationships between variables are strong. Many works have used the
EBA methodology first presented in Leamer (1983,
1985), for example, Levine and Renelt (1992). Sala-i-Martin (1997) modified
Leamer’s approach, and his approach can be found even more frequently
in many empirical studies. However, to our knowledge, no program has been
developed that can efficiently execute any type of extreme bounds analysis.
Although Gregorio Impavido wrote a program in 1998 that uses Leamer’s
EBA approach (simply called **eba**), no program has been developed for
the use of Sala-i-Martin’s method. Furthermore, while being useful for
simple regression tasks, Impavido’s program is limited in scope. The new
program, **enhancedeba**, presented here seeks to enhance the currently
available method. It can be used for any type of cross-sectional or panel
regression, can do any number of variable combinations, and can be used
for both the Leamer method and the Sala-i-Martin method.

**Additional materials:**

uk12_hadamovsky.pdf

### Instrumental variables estimation using heteroskedasticity-based instruments

In a 2012 article in the *Journal of Business & Economic
Statistics*, Arthur Lewbel presents the theory of allowing the identification
and estimation of “mismeasured and endogenous regressor models” by
exploiting heteroskedasticity. These models include linear regression models
customarily estimated with instrumental variables (IV) or IV-GMM techniques.
Lewbel’s method, under suitable conditions, can provide instruments where no
conventional instruments are available or augment standard instruments to
enable tests of overidentification in the context of an exactly identified
model. In this talk, I discuss the rationale for Lewbel’s methodology and
illustrate its implementation in a variant of Baum, Schaffer, and Stillman’s
**ivreg2** routine, **ivreg2h**.

**Additional materials:**

uk12_baum_lewbel_schaffer_talavera.pdf

### Estimating random coefficients logit demand models using aggregate data

Discrete choice demand models are popular in applied analysis and can be estimated using market-level data on product shares and characteristics. The random parameters logit model is an extension to the traditional specification and can accommodate heterogeneity in consumer preferences and rich patterns of substitution over a large number of products. The purpose of this presentation is to set out a Stata program that estimates the parameters of this model by using the algorithm proposed by Berry, Levinsohn, and Pakes (1995) and that can also address the potential issues of price endogeneity. The estimator is coded in Mata and involves an inner-loop contraction mapping to invert the market shares, followed by an outer loop search over the parameters that minimizes a GMM objective function. The estimator allows the user to specify the variables that have random parameters and contains an additional option to generate a matrix of own and cross-price elasticities of demand.

**References**

Berry, S., J. Levinsohn, and A. Pakes. 1995. Automobile
prices in market equilibrium. *Econometrica* 63: 841–890.

**Additional materials:**

uk12_vincent.pdf