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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.
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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.

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Additional information:
Northern_Europe21_Bottai.pdf

Matteo Bottai
Karolinska Institutet
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.
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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.

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Additional information:
Northern_Europe21_Yang.pdf

Xiao Yang
StataCorp
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.
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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
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Additional information:
Northern_Europe21_Santoni.pdf

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.
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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.

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Additional information:
Northern_Europe21_Orsini.pdf

Nicola Orsini
Karolinska Institutet
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.
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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.

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Additional information:
Northern_Europe21_Bruun.pdf

Niels Henrik Bruun
Aalborg University Hospital
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:
Northern_Europe21_Pitblado.pdf

Jeff Pitblado
StataCorp
6:30–7:00
Open panel discussion with Stata developers
StataCorp

Scientific committee

Matteo Bottai
Karolinska Institutet
Nicola Orsini
Karolinska Institutet

Logistics organizer

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.

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