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| 2:00–2:30 | Estimating geometric rates with strisk
Abstract:
Incidence rates are popular summary measures of the occurrence over time
of events of interest. They are also named mortality rates or failure
rates, depending on the context. The incidence rate is defined as the
ratio between total number of events and total follow-up time and can be
estimated with the strate command.
The incidence rate represents an
average count per unit time, for example, average number of bacteria
infections per year. It is an appropriate summary measure when the event
of interest can occur multiple times on any given subject, like
infections, but not for events that can occur only once, such as death.
An appropriate summary measure of the latter type of events is the
geometric rate, which represents a probability, or risk, per unit time,
for example, the risk of dying in one year. This talk presents the
strisk command for estimating geometric rates and illustrates its
use and interpretation through a data example.
Additional information: Matteo Bottai
Karolinska Institutet
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| 2:30–3:30 | Fitting Cox proportional hazards model for interval-censored event-time data in Stata
Abstract:
In survival analysis, interval-censored event-time data occur when the
event of interest is not always observed exactly but is known to lie
within some time interval. These types of data arise in many areas,
including medical, epidemiological, economic, financial, and
sociological studies. Ignoring interval-censoring will often lead to
biased estimates.
A semiparametric Cox proportional hazards regression
model is used routinely to analyze uncensored and right-censored
event-time data. It is also appealing for interval-censored data because
it does not require any parametric assumptions about the baseline hazard
function. Also, under the proportional-hazards assumption, the hazard
ratios are constant over time. Semiparametric estimation of
interval-censored event-time data is challenging because none of the
event times are observed exactly. Thus, "semiparametric" modeling of
these data often resorted to using spline methods or
piecewise-exponential models for the baseline hazard function. Genuine
semiparametric modeling of interval-censored event-time data was not
available until recent methodological advances, which are implemented in
the stintcox command.
In this presentation, I describe basic types of interval-censored data and demonstrate how to fit the semiparametric Cox proportional hazards model to these data using Stata's new stintcox command. I will also discuss how to interpret and plot results and how to graphically assess proportional-hazards assumptions.
Additional information: Xiao Yang
StataCorp
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| 3:30–4:00 | Modeling long-term survival after surgery for esophageal cancer with the mlexp command
Abstract:
The long-term survival after one year from surgery for esophageal cancer
was modeled considering the joint density function
$\Pi_{i=1}^n {f_0 (x_i^T)f_1(d_i \vert x_i^T)f_2(y_i \vert d_i,x_i^T)}$
where $i$ indicated the $i$th patient; $y$ was the numbers of years from one
year after study entry to the first of the $J$ events; $di$ was coded as $0$
for a censoring event, $1$ for event death by any other cause, or $2$ for
event cancer-specific death; and $x_i^T$
was a vector of covariates.
It was assumed that the distributions $f1$ and
$f2$ depend on a set of unknown parameters $\alpha$, $\eta$, $\phi$, $\gamma$, and $\rho$.
We used the command mlexp, which performs maximum likelihood estimation
of models that satisfy the linear-form restrictions as the one in our
study to estimate the five unknown parameters. By modeling directly the
likelihood function, we gained greater flexibility in statistical
modeling compared with standard statistical packages and easier
integration of problems involving time-to-event data, competing risks,
and truncated data.
Contributor:
Matteo Bottai
Karolinska Institutet
Additional information: Giola Santoni
Karolinska Institutet
|
| 4:15–4:45 | Exploring heterogeneity in dose–response meta-analysis
Abstract:
The aim of this talk is to explore the extent of heterogeneity across
studies in the framework of weighted mixed models applied to aggregated
data.
Limiting model complexity to a maximum of two fixed effects and
three variance–covariance components, I derived estimates of the
study-specific dose-response relationships using a common grid of dose
values and show the estimates graphically using a common referent.
Quantiles of the prediction interval for specific contrasts of
interest are used to describe the magnitude of heterogeneity.
Additional information: Nicola Orsini
Karolinska Institutet
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| 4:45–5:15 | Regression modeling for reliability/ICC in Stata
Abstract:
Reliability is assessing the degree of distinction despite the
measurement error. One way of assessing the reliability is by the
intraclass correlation. Because of the “black box”-like setup for intraclass
correlations (ICC), underlying assumptions are often ignored and
sometimes violated to different degrees.
With advanced methods like
mixed regressions in statistical packages, one can go back and
define underlying models that are more aligned with the actual
design. Using advanced methods as a base ICC estimation would lead to
better modeling patterns of reliability. Because the design of the study
comes into focus, it is easier to choose an appropriate model. This again
makes it easier to perform power calculations before and do model
control after the data collection. Finally, adjustments become a
possibility in the design and modeling. Based on an example, this
presentation shows what can be done in Stata and discusses future steps.
Additional information: Niels Henrik Bruun
Aalborg University Hospital
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| 5:15–6:15 | Custom estimation tables
Abstract:
In this presentation, I build custom tables from one or more estimation
commands. I demonstrate how to add custom labels for significant
coefficients and how to make targeted style edits to cells in the table.
I conclude with a simple workflow for you to build your own custom
tables from estimation commands.
Additional information: Jeff Pitblado
StataCorp
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| 4:00–4:30 | Open panel discussion with Stata developers
StataCorp
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Matteo Bottai Karolinska Institutet |
Nicola Orsini Karolinska Institutet |
The 2021 Northern European Stata Conference is jointly organized by Metrika Consulting, the official distributor of Stata for Russia and the Nordic and Baltic countries, and the Biostatistics Team at the Department of Public Health Sciences at the Karolinska Institutet.
View the proceedings of previous Stata Conferences and Users Group meetings.
