VAT: Incl. excl.
Currency

 

 

 

Program and Abstracts, 2019 Northern European Stata User Group Meeting

 

The Northern European Stata Users Group meeting was held at Karolinska Institutet in Stockholm on August 30. The meeting was very well attended and the program and the presentations were excellent. 

 

Program and Abstracts

 

Modeling the probability of occurrence of events with the new -stpreg- command

Matteo Bottai, Andrea Discacciati, Giola Santoni. Karolinska Institutet, Stockholm, Sweden

Abstract: We introduce the new -stpreg- command, which can estimate flexible parametric models for the event-probability function, a measure of occurrence of an event of interest over time. The event-probability function is defined as the instantaneous probability of an event at a given time point conditional on having survived until that point. Unlike the hazard function, the event-probability function defines the instantaneous probability of the event. This talk describes its properties and interpretation along with convenient methods for modeling the possible effect of covariates on it, including flexible proportional-odds models and flexible power-probability models, which allow for censored and truncated observations. The talk compares these with other popular methods, and discusses the theoretical and computational aspects of parameter estimation through a real data example.

 

Marginal estimates through regression standardisation in competing risks and relative survival models
Paul C Lambert, University of Leicester

Abstract: In large disease registers there is often interest in mortality due a specific cause. Individuals are at risk of death from a variety of other causes, making this a competing risks situation. Disease registers are observational and comparisons between exposure groups are prone to confounding. I will introduce a general command, -standsurv-, for obtaining marginal effects and contrasts from a variety of survival models. In this talk I will focus on a marginal cause-specific cumulative incidence functions after fitting a number of cause-specific models. These models need to be combined in order to obtain the marginal predictions. If the models appropriately adjust for relevant confounders then contrasts between marginal estimates can be interpreted as causal effects. I will also describe a number of other useful measures including marginal estimates of the expected life years lost. Relative survival has a number of similarities to competing risks and I will demonstrate how many of the ideas for competing risks also apply in a relative survival framework.

 

Simple and complex survival analysis: New developments in -merlin-
Michael J. Crowther, University of Leicester

Abstract. At previous Stata conferences I’ve presented -survsim- for simulating survival data, -multistate- for multi-state parametric survival analysis, and -merlin- for fitting general mixed effects regression models for linear, non-linear, and user-defined distributions. In this talk, I’ll present some ongoing work which brings together the codebase of all three commands into one coherent framework. This will provide new features, such as,
1. simulating survival times from any survival model fitted using -merlin-
2. allowing -merlin- to be used as a transition model in a multistate survival analysis - this enables, for example, the modelling of multiple timescales
3. incorporating interval-censoring into standard and flexible parametric survival and cause-specific competing risks models, directly within -merlin-
To summarize, -merlin- can incorporate anything from the simplest parametric proportional hazards models to complex, non-linear, hierarchical survival models. Possibilities are endless in terms of accounting for a wide range of challenges arising in clinical applications.

 

A procedure to facilitate the analysis of time-varying covariates with survival data

Hugo Sjöqvist, Nicola Orsini, Karolinska Institutet

Abstract: Continuously recorded exposure data are increasingly available in predicting time-to-event outcomes in epidemiological research. To take full advantage of this type of data we introduce a new command, -sttde-, to facilitate statistical inference, visualization, and summary of exposure effects that may change along the time scale. The -sttde- command is designed to work with commonly used parametric and semi-parametric survival models. Applications of the -sttde- command will be illustrated using yearly recorded exposure arising from the Swedish Register data.

 

Meta-analysis in Stata.

Yulia Marchenko, StataCorp 

Abstract: Meta-analysis combines results of multiple similar studies to provide an estimate of the overall effect. This overall estimate may not always be representative of a true effect. Often, studies report results that vary in magnitude and even direction of the effect, which leads to between-study heterogeneity. And sometimes the actual studies selected in a meta-analysis are not representative of the population of interest, which happens, for instance, in the presence of publication bias. Meta-analysis provides the tools to investigate and address these complications. Stata has a long history of meta-analysis methods contributed by Stata researchers. In my presentation, I will introduce Stata's new suite of commands, meta, and demonstrate it using real-world examples.

 

Reproducible and automated reporting using Stata
Kristin MacDonald, StataCorp

Abstract: Whether you want to incorporate Stata results into a Word, Excel, HTML, or PDF document, you can use Stata's features for reproducible reports.
And for reports that need to be dynamic--reports that need to change as the data changes--Stata provides the tools to recreate reports and automatically update all graphs, summary statistics, regressions, and other results from Stata. In this talk I will give an overview of Stata's tools for reporting and demonstrate how to create HTML and Word documents using Markdown and how to create customized Word, Excel, and PDF documents.

 

Estimating long run coefficients and bootstrapping standard errors in large panels with cross-sectional dependence
Jan Ditzen, Heriot-Watt University

Abstract: This talk explains how to estimate long run coefficients and bootstrap standard errors in a dynamic panel with heterogeneous coefficients, common factors and a large number of observations over cross-sectional units and time periods. The common factors cause cross-sectional dependence, which is approximated by cross-sectional averages. Heterogeneity of the coefficients is accounted by taking the unweighted averages of the unit-specific estimates. Following Chudik, Mohaddes, Pesaran and Raissi (2016, Advances in Econometrics 36:85-135) I consider three different models to estimate long-run coefficients: a simple dynamic model (CS-DL), an error-correction model, and an ARDL model (CS-ARDL). I explain how to estimate all three models using the Stata community-contributed command -xtdcce2-. In a second step the non-parametric standard errors and bootstrapped standard errors are compared. The bootstrap follows on the lines of Goncalves and Perron (2016) and the user written command -boottest- (Roodman, Nielsen, Webb and Mackinnon, 2018). The challenges are to maintain the error structure across time and cross sectional units and to encompass the dynamic structure of the model.

 

State-Level Gun Policy Changes and Rate of Workplace Homicide in the United States
Erika Sabbath, Summer Sherburne Hawkins, Christopher F Baum, Boston College

Abstract: Nearly 40,000 people in the U.S. die from firearm-related causes annually. Of these, about 1% are intentionally shot and killed while at work; work-related homicides account for about 10% of all workplace fatalities. While firearm policies have remained essentially unchanged at the national level, there is greater variation in state-level gun control legislation. Moreover, the gun control landscape between and within states has changed considerably over the past ten years. Little recent work has focused on determinants or epidemiology of workplace homicide. The purpose of this study is to test whether changes in state-level gun control policies are associated with changes in state-level workplace homicide rates. Our analysis shows that stronger gun-control policies, particularly around concealed carry permitting, background checks, and domestic violence may be effective means of reducing work-related homicide.

 

Emagnification: a tool for estimating effect size magnification and performing design calculations in epidemiological studies
David J. Miller* James Nguyen* Matteo Bottai**
*United States Environmental Protection Agency
**Karolinska Institutet

Abstract. Artificial effect size magnification (ESM) may occur in underpowered studies, where effects are only reported because they or their associated p-value have passed some threshold. Ioannidis (2008) and Gelman and Carlin (2014) have suggested that the plausibility of findings for a specific study can be evaluated by computation of ESM, which requires statistical simulation. In this talk, we present a new Stata package called ?emagnification?that allows straightforward implementation of such simulations in Stata. The commands automate these simulations for epidemiological studies and enable the user to assess ESM on a routine basis for published studies using user-selected, study-specific inputs that are commonly-reported in published literature. The intention of the package is to allow a wider community to use ESMs as a tool for evaluating the reliability of reported effect sizes and to put an observed statistically significant effect size into a fuller context with respect to potential implications for study conclusions.

 

Visualising effect modifications

Niels Henrik Bruun, Aarhus Universit

Abstract. -margins- and –marginsplot- are Stata commands are excellent for e.g. visualising effects. However, when the functions modelled for –margins- are not simple polynomials but e.g. has to be modelled using cubic splines there is a need for an alternative. I present an easy to use prefix command –emc- for visualising the difference between two curves. One example could be the difference in weight or height development between boys and girls dependent of age. The –emc- command is about to be presented in the Stata Journal, but this presentation is quite different, based on another example.

 

Model selection in dose-response meta-analysis of summarized data
Nicola Orsini, Karolinska Institutet

Abstract: A linear-mixed effects model for the synthesis of multiple tables of summarized dose-response data has been recently proposed and implemented in the -drmeta- command. One the main advantages offered by this framework is the possibility to fit complex models avoiding exclusion of studies contrasting a limited number of doses. Aim of this presentation is to evaluate the ability of the Akaike’s information criterion (AIC) to suggest the true dose–response relationship. Statistical experiments are conducted under the assumption of either a linear (Shape 1) or non-linear (Shape 2) relationship between a quantitative dose and mean outcome. Tables of summarized data are generated upon categorization of the dose into quantiles. Every simulated dose-response meta-analysis is analyzed with a linear-mixed effects model using two commonly used strategies: linear function and splines. Accuracy of the AIC is assessed by calculating the proportion of times in a large number of experiments the Shape 1 and Shape 2 are correctly identified by choosing the lowest AIC among the two modelling strategies. We also explore how this accuracy may vary according to the distribution of the dose and the way it has been categorized.

 

Stata/SQL/Python integration to emulate prospective cohort studies from big register data
Matteo Marrazzo, Nicola Orsini, Karolinska Institutet

Abstract: The possibilities of using Stata to interrogate and analyze big data are not widely known among health researchers. However, the ability to meld different programming tools is becoming gradually more important with the increasing mainstream availability of big data sources. Aim of this presentation is to illustrate, using existing commands such as -odbc- and -python-, how to emulate and analyze large prospective cohorts from a collection of big national registers, harvesting the power of the different engines available (i.e. SQL to handle relational databases and the preprocess phase, Stata to easily perform advanced statistical analyses and python to implement well known modules and packages for data manipulation and plots). A case study in pharmaco-epidemiology is used to illustrate the potential of using Stata to both design and analyze such complex and large datasets.

 

Scientific committee

Matteo Bottai, matteo.bottai@ki.se, Unit of Biostatistics, National Institute of Environmental Medicine, Karolinska Institutet.

Paul Lambert, paul.lambert@le.ac.uk, Department of Health Sciences at the University of Leicester and Department of Medical Epidemiology and Biostatistics, Karolinska Institutet. Stockholm

Nicola Orsini, nicola.orsini@ki.se, Biostatistics Team, Department of Public Health Sciences, Karolinska Institutet.

 

Logistics organizers

The meeting is jointly organized by the Biostatistics Team at the Department of Public Health Sciences and Metrika Consulting.

Metrika is the distributor of Stata in Northern Europe -- the Nordic and Baltic regions, and Russia. For further information, please visit www.statanordic.com or contact us at metrika@metrika.se.

 

CUSTOMER TYPE:

FACULTY MEMBER

STUDENT

FACULTY OR STUDENT AT:

 

OTHER ×

CUSTOMER TYPE:

FACULTY MEMBER

STUDENT

FACULTY OR STUDENT AT:

 

OTHER ×

CUSTOMER TYPE:

FACULTY MEMBER

OTHER ×