Template-Type: ReDIF-Paper 1.0
Title: Too much or too little? New tools for the CCE estimator
Abstract: This talk will cover new developments in the literature of common correlated effects (CCE) and their
implementation into Stata. First, I will discuss regularized CCE (Juodis, 2022, Journal of Applied Econometrics).
CCE is known to be sensitive to the selection of the number of cross-section averages. rCCE overcomes the
problem by regularizing the cross-section averages. Second, I will discuss the test for the rank condition based
on DeVos, Everaert, and Sarafidis (2024, Econometrics Reviews). If the rank condition fails, CCE will be
inconsistent, and therefore testing the condition is key for any empirical application. Finally, I will discuss the
selection of cross-section averages using the information criteria from Karabiyik, Urbain, and Westerlund (2019,
Journal of Applied Econometrics) and Margaritella and Westerlund (2023, Econometrics Journal).
Author-name: Jan Ditzen
Author-workplace-name: Freie Universität Bozen-Bolzano
Author-person: pdi434
File-URL: http://repec.org/neur2024/Northern_Europe24_Ditzen.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:01

Template-Type: ReDIF-Paper 1.0
Title: The SCCS design
Abstract: The SCCS design, in contrast to standard epidemiological observational designs like the cohort and case–
control design, offers a more time- and cost-efficient approach. This efficiency is due to the larger sample sizes
required by the standard designs. Further, the SCCS method automatically adjusts for known and unknown fixed
confounders. The latter can be a significant challenge in standard designs. The SCCS method splits an
observation period into one or more risk periods and one or more control periods. The risk periods are relative to
an exposure event, whereas the observation period is either fixed or relative to the exposure event. Often, one
adds time or age adjustments during the observation period. The basic idea is to compare incidence rates for
the risk periods with the control period while adjusting for time or age and cases. The SCCS design originates
from the desire to estimate the relative effect of vaccines, such as the MMR, on adverse events like meningitis.
Compared with the classical design, it is a matter of asking when instead of who. I will discuss the SCCS design
and present the Stata command sccsdta, which transforms datasets of times for events and exposures by
cases into datasets marked into risk and control periods as well as time or age periods. After the dataset
transformation, the analysis is simple, using fixed-effect Poisson regression.
Author-name: Niels Henrik Bruun
Author-workplace-name: Aalborg University Hospital
File-URL: http://repec.org/neur2024/Northern_Europe24_Bruun.pdf
File-Format: application/pdf
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Handle: RePEc:boc:neur24:02

Template-Type: ReDIF-Paper 1.0
Title: Improving the speed and accuracy when fitting flexible parametric survival models on the
log-hazard scale
Abstract: Flexible parametric survival models are an alternative to the Cox proportional hazards model and more standard
parametric models for the modeling of survival (time-to-event) data. They are flexible in that spline functions are
used to model the baseline and potentially complex time-dependent effects. In this talk, I will discuss using
splines on the log-hazard scale. Models on this scale have some computational challenges because numerical
integration is required to integrate the hazard function during estimation. The numerical integration is required
for all individuals and for each call to likelihood/gradient/Hessian functions and can therefore be slow in large
datasets. In addition, the models may have a singularity for the hazard function at t=0, which leads to precision
issues. I will describe two recent updates to the stpm3 command that make these models faster to fit in large
datasets and have improved accuracy for the numerical integration. First, the python option makes use of the
mlad optimizer, which calls python, leading to major speed gains in large datasets. Second, there are different
options for numerical integration of the hazard function, including tanh-sinh quadrature, which is now the default
when the hazard function has a singularity at t=0. This leads to more accurate estimates compared with the
more standard Gauss–Legendre quadrature. These speed and accuracy improvements make the use of these
models more feasible in large datasets.
Author-name: Paul Lambert
Author-workplace-name: Cancer Registry of Norway–Norwegian Institute of Public Health
Author-workplace-name: Karolinska Institutet 
File-URL: http://repec.org/neur2024/Northern_Europe24_Lambert.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:03

Template-Type: ReDIF-Paper 1.0
Title: Example of modeling survival with registry data to assist with clinical decision making
Abstract: The Cancer Registry of Norway contains several clinical registries with rich information on the diagnosis,
treatment, and follow up of cancer patients. Since 2013, the Clinical Registry for Gynecological Cancer has
collected information on residual disease (RD) diameter following ovarian cancer surgery, which is prognostic
for survival. Internationally, attaining 1cm or less RD is considered “adequate” debulking. This cutoff has been
widely used for making treatment decisions and is used to define high-risk patients in Norwegian treatment
guidelines.
However, few studies have evaluated ovarian cancer survival across continuous RD diameter. In flexible
parametric models, I compared excess mortality of stage III–IV ovarian cancer patients across continuous RD
diameter using restricted cubic splines. This presentation is an
Author-name: Cassie Trewin-Nybråten
Author-workplace-name: Cancer Registry of Norway–Norwegian Institute of Public Health
File-URL: http://repec.org/neur2024/Northern_Europe24_Trewin-Nybraten.pptx
File-Format: application/X-MS-Powerpoint
File-Function: presentation materials
Handle: RePEc:boc:neur24:04

Template-Type: ReDIF-Paper 1.0
Title: Limitations and comparison of the DFA, PP, and KPSS unit-root tests: Evidence for laboral
market variables in Mexico
Abstract: Unit-root tests have represented a great contribution to time-series analysis by detecting when a variable is
stationary or not. However, they present limitations, which, although known, are still used, and it seems that
these limitations go unnoticed when applied in time-series studies. Examples of these limitations, mainly
Dickey–Fuller (DF) and Phillips–Perron (PP), are that they could be detecting the presence of a unit root when
the series does not have it. Consequently, this presentation includes some of the criticisms that have been made
to the unit-root tests to consequently execute in Stata the three best-known unit root tests (DFA, PP , and KPSS)
for the main macroeconomic variables of Mexico, this with the intention of analyzing, both graphically and
technically, whether the series are stationary or not. The main conclusion is that unit-root tests are often more
related to statistical than economic issues.
Author-name: Ricardo Rodolfo Retamoza Yocupicio
Author-workplace-name: The National Autonomous University of Mexico
File-URL: http://repec.org/neur2024/Northern_Europe24_Rodolfo.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:05

Template-Type: ReDIF-Paper 1.0
Title: Using Stata with many datasets, methods, and variables
Abstract: Complex data management and extensive analysis of data can be challenging in research projects. Compared
with a classical textbook example with one clean dataset and a few selected variables and models, medical
research projects often involve many datasets in different formats and use a range of statistical methods and
many variables and outcomes. Stata has features for keeping track of datasets, automating statistical analyses,
and summarizing results. Some experiences and practical tips with commands such as import, foreach,
putexcel, and dtable in combination with the use of macros will be presented. These can be helpful for efficiently
solving complex tasks, obtaining overviews of data and methods, and reporting statistical results to a
multidisciplinary research group.
Author-name: Are Hugo Pripp
Author-workplace-name: Oslo Centre for Biostatistics and Epidemiology (OCBE)
File-URL: http://repec.org/neur2024/Northern_Europe24_Pripp.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:06

Template-Type: ReDIF-Paper 1.0
Title: Maps in Stata
Abstract: This interactive talk will provide an introduction to the packages and code required for producing high-quality
maps in Stata. I will show how to import shapefiles, plot different layer types (points, lines, polygons), and
generate different types of choropleth and bivariate maps. Some basic customization options will also be
discussed.
Author-name: Asjad Naqvi
Author-workplace-name: Austrian Institute for Economic Research (WIFO) 
Author-person:  pna493
File-URL: http://repec.org/neur2024/Northern_Europe24_Naqvi1.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:07

Template-Type: ReDIF-Paper 1.0
Title: Causal inference with time-to-event outcomes under competing risk
Abstract: The occurrence of competing events often complicate the analysis of time-to-event outcomes. While there is a
rich literature in the area of survival analysis on methods for handling competing risk that goes back a long way,
there has also for a long time been some confusion regarding best approach and implementation when facing
competing events in applied research. Recent advances in the use of estimands in causal inference has led to
new developments and insights (and discussions) on how to best analyze time-to-event outcomes under
competing risk. The role of classical statistical estimands are now better understood, and new causal
estimands have been suggested for addressing more advanced causal questions. In this talk, I will briefly review
this development and the estimation of the most basic estimands and discuss some extensions, such as when
interest is in the effect of time-varying treatments.
Author-name: Jon Michael Gran
Author-workplace-name: Oslo Centre for Biostatistics and Epidemiology (OCBE)
File-URL: http://repec.org/neur2024/Northern_Europe24_Gran.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:08

Template-Type: ReDIF-Paper 1.0
Title: Extending standard reporting to improve communication of survival statistics
Abstract: Routine reporting of cancer patient survival is important, both to monitor the effectiveness of healthcare and to
inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving
different purposes and targeting different audiences. It is important that routine publications expand on current
practice and provide estimates on a wider range of survival measures. Using data from The Cancer Registry of
Norway, we examine the feasibility of automated production of such statistics.
Author-name: Tor Ã…ge Myklebust
Author-workplace-name: Cancer Registry of Norway–Norwegian Institute of Public Health 
File-URL: http://repec.org/neur2024/Northern_Europe24_Myklebust.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:09

Template-Type: ReDIF-Paper 1.0
Title: Bayesian estimation of disclosure risks for synthetic time-to-event data
Abstract: Introduction: Generation of synthetic patient records can preserve the structure and statistical properties of the
original data while maintaining privacy, providing access to high-quality data for research and innovation. Few
synthesization methods account for the censoring mechanisms in time-to-event data, and formal privacy
evaluations are often lacking. Improvements in synthetic data utility come with increased risks of privacy
disclosure, necessitating a careful evaluation to obtain the proper balance.
 Methods: We generate synthetic time-to-event data based on colon cancer data from the Cancer Registry of
Norway, using a sequence of conditional regression models and flexible parametric modeling of event times.
Different levels of model complexity are used to investigate the impact on data utility and disclosure risk. The
privacy risk is evaluated using Bayesian estimation of disclosure risks, which form the basis for a differential
privacy audit.
 Results: Including more interaction terms and increasing degrees of freedom improves synthetic data utility and
elevates privacy risks. While certain interactions substantially improve utility, others reduce privacy without
much utility gain. The most complex model displays near-optimal utility scores.
 Conclusions: The results demonstrated a clear tradeoff between synthetic data utility and privacy risks.
Interestingly, the relationship is nonlinear, because certain modeling choices increase synthetic data utility with
little privacy loss, and vice versa.
Author-name: Sigrid Leithe
Author-workplace-name: Cancer Registry of Norway–Norwegian Institute of Public Health 
File-URL: http://repec.org/neur2024/Northern_Europe24_Leithe.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:10

Template-Type: ReDIF-Paper 1.0
Title: How can Stata enable federated computing for decentralized data analysis?
Abstract: Federated computing offers a transformative approach to data analysis, enabling the processing of distributed
datasets without the need for centralization, thus aiming to preserve privacy and security. In this talk, I will
explore how these principles can be applied within the Stata environment to address the growing challenges of
data sharing and computational limits. I will highlight the current features in Stata that make federated
computing possible and the challenges and future directions, setting the stage for innovation in decentralized
data analysis. By integrating federated computing with Stata, researchers can perform complex analyses on
sensitive, geographically dispersed data while maintaining the software's robust statistical capabilities.
Author-name: Narasimha Raghavan
Author-workplace-name: Cancer Registry of Norway–Norwegian Institute of Public Health
File-URL: http://repec.org/neur2024/Northern_Europe24_Raghavan.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:11

Template-Type: ReDIF-Paper 1.0
Title: Causal mediation
Abstract: Causal inference aims to identify and quantify a causal effect. With traditional causal inference methods, we can
estimate the overall effect of a treatment on an outcome. When we want to better understand a causal effect,
we can use causal mediation analysis to decompose the effect into a direct effect of the treatment on the
outcome and an indirect effect through another variable, the mediator. Causal mediation analysis can be
performed in many situations—the outcome and mediator variables may be continuous, binary, or count, and the
treatment variable may be binary, multivalued, or continuous. In this talk, I will introduce the framework for
causal mediation analysis and demonstrate how to perform this analysis with the mediate command, which was
introduced in Stata 18. Examples will include various combinations outcome, mediator, and treatment types.
Author-name: Kristin MacDonald
Author-workplace-name: StataCorp LLC 
File-URL: http://repec.org/neur2024/Northern_Europe24_MacDonald.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:12

Template-Type: ReDIF-Paper 1.0
Title: Multivariate random-effects meta-analysis for sparse data using smvmeta
Abstract: Multivariate meta-analysis is used to synthesize estimates of multiple quantities (“effect sizes”), such as risk
factors or treatment effects, accounting for correlation and typically also heterogeneity. In the most general
case, estimation can be intractable if data are sparse (for example, many risk factors but few studies) because
the number of model parameters that must be estimated scales quadratically with the number of effect sizes. I
will present a new meta-analysis model and Stata command, smvmeta, that make estimation tractable by
modeling correlation and heterogeneity in a low-dimensional space via random projection and that provide more
precise estimates than meta-regression (a reasonable alternative model that could be used when data are
sparse). I will explain how to use smvmeta to analyze data from a recent meta-analysis of 23 risk factors for
pain after total knee arthroplasty.
Author-name: Chris Rose
Author-workplace-name: Norwegian Institute of Public Health 
File-URL: http://repec.org/neur2024/Northern_Europe24_Rose.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:13

Template-Type: ReDIF-Paper 1.0
Title: Advanced data visualizations with Stata, part VI: Visualizing more than two variables
Abstract: The presentation will showcase how Stata can be utilized for visualizing data with more than two dimensions.
The presentation will introduce extensions to existing visualization packages and will also launch two new
packages.
Author-name: Asjad Naqvi
Author-workplace-name: Austrian Institute for Economic Research (WIFO) 
Author-person:  pna493
File-URL: http://repec.org/neur2024/Northern_Europe24_Naqvi2.pdf
File-Format: application/pdf
File-Function: presentation materials
Handle: RePEc:boc:neur24:14