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The 13th Northern European Stata Conference takes place 12 October 2022 at the Oslo Cancer Cluster Innovation Park. There will be an optional postconference workshop on 13 October.

This conference will provide Stata users the opportunity to exchange ideas, experiences, and information on new applications of Stata. Representatives from StataCorp will attend and host an open-panel discussion, so you can share your questions and feedback directly with Stata developers. Anyone interested in using Stata is welcome. No level of sophistication is assumed for presenters or attendees.


All times are CEST (UTC +2)

9:00–9:05 Welcome
Bjarte Aagnes
Cancer Registry of Norway
Session chair: Ronnie Babigumira
9:05–9:30 Application of stpm2 to estimate relative survival for cancer patients in the Nordic countries Abstract: In this presentation, I will describe the benefits and challenges with comparing population-based survival across the Nordic countries using the relative survival framework.
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I used the NORDCAN database, which includes patients diagnosed with cancer from 1990 to 2016 from Denmark, Finland, Iceland, Norway, and Sweden. I adopted a model-based approach using flexible parametric survival models and compared them with nonparametric estimates. stpm2 and standsurv were used for obtaining parametric estimates, and strs for nonparametric estimates. I will discuss issues such as age standardization, model stability, winsorizing, and conditional survival.

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Frida Lundberg
Karolinska Institutet
9:30–10:15 Improving fitting and predictions for flexible parametric survival models Abstract: Flexible parametric survival models have been available in Stata since 2000 with Patrick Royston’s stpm command.
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I developed stpm2 in 2008, which added various extensions. However, the command is old and does not take advantage of some of the features Stata has added over the years. I will introduce stpm3, which has been completely rewritten and adds a number of useful features, including
  • full support for factor variables (including for time-dependent effects).
  • use of extended functions within a varlist. Incorporate various functions (splines, fractional polynomial functions, etc.) directly within a varlist. These also work when including interactions and time-dependent effects.
  • easier and more intuitive predictions. These fully synchronize with the extended functions, making predictions for complex models with multiple interactions/nonlinear effects incredibly simple. Make predictions for specific covariate patterns, and perform various types of contrasts.
  • directly saving predictions to one or more frames. This separates the data used to analyze the data for predictions.
  • obtaining various marginal estimates using standsurv. This synchronizes with stpm3 factor variables and extended functions, making marginal estimates much easier and less prone to user mistakes for complex models.
  • modeling on the log(hazard) scale. Do all the above for standard survival models, competing-risks models, multistate models, and relative survival models all within the same framework.

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Paul Lambert
University of Leicester and Karolinska Institutet
10:15–10:40 Survival by first-line treatment type and timing of progression among follicular lymphoma patients Abstract: In follicular lymphoma (FL), progression of disease within 24 months has emerged as a popular prognostic marker for overall survival (OS).
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While it has considerable clinical relevance, there are also inherent limitations in relation to the fixed time point of 24 months and potential variation by treatment type and choice of comparison group. In this presentation, we will highlight some of the methodical limitations and present the first results from a large population-based cohort of FL patients. National register-based information has been combined with detailed medical record data to create a unique cohort with detailed treatment and follow-up information. We allow progression to be time varying and estimate relative rates, as well as OS by first-line treatment and timing of progression using an illness-death modeling approach. Stata packages merlin and multistate were applied, and example code will be presented. Our findings show that progression is associated with worse survival beyond the 24-month time point, illustrating the need for individualized management by timing of progression for optimal care of patients with FL.

T. Wästerlid
B.E. Wahling
P.O. Andersson
S. Ekberg
S. Lockmer
G. Enblad
M. J. Crowther
E. Kimby
K. E. Smedby

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Caroline Weibull
Karolinska Institutet
10:40–11:00 Break
11:00–11:15 The devil is in the details ... and the data: Tutorial on preparing data for multistate modeling Abstract: Data preparation for multistate models is a foundation prior to estimation using parametric models or nonparametric approaches.
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In Stata, for example, the multistate packages command msset is a data-preparation tool to transform data in wide format into long format. However, msset is not optimal for multistate model settings with reversible transitions, that is, transitions that allow recovery from one state to another. In this case, correctly defining each transition’s risk time and event (status) without using a program may be preferred. This presentation aims to guide users to prepare data for a reversible multistate model from wide format to long format without using a data-preparation command and will provide tutorials with example data.

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Enoch Yi-Tung Chen
Karolinska Institutet
11:15–11:40 Establishing upper reference limits for left-censored and contaminated data Abstract: When one establishes reference interval limits, measurements can sometimes be characterized by either A) being left-censored or B) being contaminated in the upper end.
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Although solutions to both characteristics have been described separately, no one to our knowledge has been handling the case when both characteristics are present. Left-censored data (A) are often wrongfully handled simply by using limit of detection (LOD), which leads to high mean estimates and too-low standard deviation estimates and hence incorrect cutoffs. Ignoring the characteristic (B), researchers often use transformations to handle the observed non-Gaussianity, which leads to too-high cutoffs and an increased proportion of false negatives (type 2 error). We propose a method based on normal quantile plots and OLS regressions to find the upper limit of a reference interval for measurements in a case characterized by A) and B). We also demonstrate how our method can be used to identify whether B) is present in a dataset. We demonstrate our proposed method using real data in two cases. Based on joint work by Niels Henrik Bruun, Stine Linding Andersen, Nanna Maria Uldall Torp, and Peter Astrup Christensen.

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Niels Henrik Bruun
Aalborg University Hospital
11:40–11:55 Securing Stata in a secure environment. Data access and logging. Abstract: At Cancer Registry of Norway, we have developed a secure environment for using Stata. A short description of this work is given describing data access, logging of data extraction (Java plugins+JDBC), and logging of Stata commands.

Bjarte Aagnes
Cancer Registry of Norway
12:00–1:00 Lunch
Session chair: Tor Åge Myklebust
1:00–1:25 Quantile regression in Stata: Performance, precision, and power Abstract: Quantile regression (command qreg) estimates quantiles of the outcome variable, conditional on the values of the independent variables, with median regression as the default form.
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Quantile regression can be used for several purposes: to estimate medians instead of means as a measure of central tendency—for instance, when data are markedly skewed; to estimate a particular quantile that may be of interest, such as the 10th quantile of birthweight to find predictors of low birthweight; or to study how the effects of independent variables vary over different quantiles of the dependent variable. Specifying the variance—covariance estimator for quantile regression is not straightforward. qreg offers both independent and identically distributed (i.i.d.) and robust estimators. The density estimation technique (DET) can be fitted, residual (i.i.d. only), or kernel. Three different bandwidth methods are available with the fitted and residual DETs, and eight kernel functions are available for the kernel DET. There is also a bootstrap option, which puts the total number of methods at 26. A natural question arises: which one to use? The aim of this presentation is to explore the performance of the methods and to arrive at some overall recommendations for which methods to use.

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Morten Wang Fagerland
Oslo University Hospital
1:25–1:50 Visualizations of marginal and conditional quantiles based on weighted mixed-effects models Abstract: Dose–response meta-analysis is widely used in a variety of fields to answer research questions based on multiple studies.
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A challenge in such applications is presenting the magnitude of uncertainty emerging from the data in light of the assumed statistical model. The aim of this talk is to illustrate a visualization tool that follows the command drmeta to graph marginal and conditional quantiles of the predicted dose–response relationships based on weighted mixed-effects models estimated on tables of aggregated data. The developed postestimation command works with different study designs, dose transformations, and outcome measures; it allows the investigator to derive any quantile (0.01 to 0.99) of the pointwise dose–response relationship; it allows the investigator to define a fine grid of dose values and to choose a referent; it shades quantiles to help distinguishing common versus extreme quantiles; it allows the user to overlay the study-specific BLUPs; it returns both static images for research articles and interactive html visualizations for web dissemination; it is based on Plotly Graphing Library taking advantage of the Stata/Python integration. Real and simulated data will be used to illustrate the use of the postestimation command.

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Nicola Orsini
Karolinska Institutet
1:50–2:50 Econometrics strikes back: GMM and two-way fixed effects Abstract: Two-way fixed effects is not a broken methodology. As Wooldridge (2021) shows, the estimator can be used to obtain heterogeneous treatment effects.
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I illustrate how to obtain these treatment effects using GMM. Additionally, I show how some other proposed estimators for heterogeneous treatment effects can be fit using GMM.

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Enrique Pinzon
2:55–3:15 Break
3:15–3:40 Recursive bivariate copula estimation and decomposition of marginal effects Abstract: This presentation describes a new Stata command, rbicopula, for fitting copula-based maximum-likelihood estimation of recursive bivariate models that enable a flexible residual distribution and differ from bivariate copula or probit models in allowing the first dependent variable to appear on the right-hand side of the second dependent variable.
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The new command provides various copulas, allowing the user to choose a copula that best captures the dependence features of the data caused by the presence of common unobserved heterogeneity. Although the estimation of model parameters does not differ from the bivariate case, the existing community-contributed command bicop does not consider the structural model's recursive nature for predictions and doesn't enable margins as a postestimation command. rbicopula estimates the model parameters, computes treatment effects of the first dependent variable, and gives the marginal effects of independent variables. In addition, marginal effects can be decomposed into direct and indirect effects if covariates appear in both equations. Moreover, the postestimation commands incorporate two goodness-of-fit tests. Dependent variables of the recursive bivariate model may be binary, ordinal, or a mixture of both. I present and explain the rbicopula command and the available postestimation commands using simulated data and data from the Stata website.

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Mustafa Coban
Institute for Employment Research (IAB)
3:40–4:05 Illuminating the factor and dependence structure in large panel models Abstract: In panel models, a precise understanding about the number of common factors and dependence across the cross-sectional dimension is key for any applied work.
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This presentation will give an overview about how to estimate the number of common factors and how to test for cross-sectional dependence. It does so by presenting two community-contributed commands: xtnumfac and xtcd2. xtnumfac implements 10 different methods to estimate the number of factors, among them the popular methods by Bai and Ng (2002) and Ahn and Horenstein (2013). The degree of cross-section dependence is investigated using xtcd2. xtcd2 allows implements three different tests for cross-section dependence, based on Pesaran (2015), Juodis and Reese (2021), and Pesaran and Xie (2021). The presentation includes a review of the theory, a discussion of the commands, and empirical examples.

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Jan Ditzen
Free University of Bozen-Bolzano
4:05–4:30 sttex: A new dynamic document command for Stata and LaTeX Abstract: In this presentation, I will introduce a new command for processing a dynamic LaTeX document in Stata, for example, a document containing both LaTeX paragraphs and Stata code.
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A key feature of the new command is that it tracks changes in the Stata code and executes the code only when needed, allowing for an efficient workflow. The command is useful for creating automated statistical reports, writing articles with data analysis, preparing slides for a methods course or a conference talk, or even writing a complete textbook with examples of applications.

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Ben Jann
University of Bern
4:30–4:55 Estimating adjusted absolute risks in a cross-sectional register-based study with logit and margins Abstract: In epidemiology, we are often interested in quantifying effects of exposures not only on the relative scale (for example, risk ratios and odds ratios) but also on the absolute scale (for example, risks and risk differences).
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In a cross-sectional study using data from the Swedish Medical Birth Register, we used logit to estimate odds ratios of adverse obstetric outcomes for different exposures. Adjusted absolute risks and absolute risks differences were estimated using the postestimation command margins. We will also discuss possibilities to extend these methods to matched cross-sectional data.

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Anna Johansson
Karolinska Institutet and Cancer Registry of Norway
4:55–5:30 Open panel discussion with Stata developers
Contribute to the Stata community by sharing your feedback with StataCorp's developers. From feature improvements to bug fixes and new ways to analyze data, we want to hear how Stata can be made better for our users.

Workshop: Multilevel mixed-effects survival analysis


Michael J. Crowther, PhD


13 October 2022


This workshop is designed for statisticians and researchers with a good working knowledge of the principles and practice of survival analysis, including modeling of survival data.

It aims to provide an overview of multilevel mixed-effects survival analysis, including recurrent event analysis and joint recurrent-terminal event models, as well as introduce and illustrate tools in Stata for conducting multilevel survival analysis, including both modeling and prediction, with a focus on calculating clinically useful predictions.

Visit the official course page for more information.

Scientific committee

Tor Åge Myklebust – Chair
Cancer Registry of Norway
Morten W. Fagerland
Oslo University Hospital
Arne Risa Hole
Universitat Jaume I
Peter Hedström
Linköping University
Anna L.V. Johansson
Karolinska Institutet & Cancer Registry of Norway

General chair

Bjarte Aagnes
Cancer Registry of Norway


The conference is free, but registration is required. To register for the conference, please email your name, affiliation, and contact details.


Visit the official conference page for more information.

Logistics organizer

The 2022 Northern European Stata Conference is jointly organized by Metrika Consulting AB, the official distributor of Stata for Russia and the Nordic and Baltic countries, and the Cancer Registry of Norway—Institute of Population-based Cancer Research.

View the proceedings of previous Stata Conferences and Users Group meetings.





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