How to publish a research paper in a major biomedical journal

From Methods to Manuscript

The University of Zagreb – School of Medicine, Andrija Štampar School of Public Health and Charité – Universitätsmedizin Berlin, Institute of Public Health in affiliation with the Berlin School of Public Health is organizing a postgraduate continuing medical education course: "How to publish a research paper in a major biomedical journal."

Lecturers:

• Tobias Kurth (MD, ScD), Professor, Director of the Institute of Public, Health, Charité - Universitätsmedizin Berlin

• Kristina Fišter (MD, MSc, DSc), Assistant Professor, Andrija Štampar School of Public Health, University of Zagreb

• Editors of the British Medical Journal (BMJ) and the Canadian Medical Association Journal (CMAJ)

About the Short Course
Publishing visibly is closely linked with success in obtaining grants, yet competition for space is fierce, particularly in major biomedical journals. As research methods evolve and the publishing landscape changes, continuing education in the knowledge and skills needed to produce quality research articles is necessary. This is all the more relevant as capacity is strengthened for health data sciences and decisions are increasingly made based on solid evidence.
Participants will gain knowledge and develop multiple skills needed to produce a high-quality research paper. This includes posing an important and relevant research question, searching biomedical literature, choosing an appropriate study design, writing a research protocol, using electronic data capture solutions, conducting data analysis using the statistical program R and interface RStudio, critically interpreting the findings, and appropriate referencing of previous work. From methods to the manuscript, participants will develop the necessary skills to successfully produce a manuscript suitable for submission to a top biomedical journal.
The course is designed for medical doctors, students, and graduates of biomedical and health disciplines.

Location: Andrija Štampar School of Public Health, Zagreb, Croatia

Language: English

Credits: 3 ECTS points

To Apply
For more information and to apply for the course please email or visit https://mef.unizg.hr/en/how-to-publish-a-research-paper-in-a-major-biomedical-journal

Course Fees
400 €, 300 € for students.

Contact
E-mail: publish(at)mef.hr

Causal research and Prediction modeling

The intent of this intensive short course is to strengthen the methodological skills of the research community. Upon successful completion of the course, participants will have a deeper understanding of methods in causal and prediction research and increased confidence in how to apply these tools in their everyday research practice.

Lecturers:

• Rolf H.H. Groenwold, MD, PhD, Leiden University Medical Center (NL)
  Maarten van Smeden, PhD, University of Utrecht, Utrecht (NL)

Learning objectives:

By the end of this week, participants should be able to:

• Critically assess the results of epidemiological studies on causal relationships or prediction models

• Correctly define exposures and learn how to best represent them in models

• Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.

• Describe key assumptions of methods used to control for (time-varying) confounding.

• Describe key assumptions of methods used to handle missing observations.

• Understand the reasons for and consequences of overfitting prediction models

• Describe recent developments in the fields of causal research and prediction modelling

Prerequisites:

• Basic knowledge of epidemiology

• Familiarity with R statistical software (for a short introduction see http://www.r-tutorial.nl/)

Fees:

• 750 €

• 510 € for enrolled students (proof required)
   

3 ECTS

Registration Information:
Tanja Te Gude
Tel. +49 30 450 570 812

Mastering R for Epidemiologic Research

The course is for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, who want to learn modern tools in the R ecosystem, like the tidyverse and Shiny, or who want to get started writing software in R.

Lecturer: Malcolm Barrett, MPH
Malcolm Barrett is a PhD candidate in epidemiology at the University of Southern California. He studies vision loss and eye diseases that affect vision, like diabetic retinopathy. Specifically, he works on methods to improve the study of how vision impacts quality of life, including tools from psychometrics and causal inference, to make vision-specific quality of life analyses more accurate and more interpretable. In addition to applied research, Malcolm also develops R packages for epidemiologic and biostatistical methods, teaches R, and organizes the Los Angeles R Users Group. He regularly contributes to open source software, including favorite community projects like ggplot2 and R Markdown.

Learning objectives:

At the end of the week, participants will have:

• Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, to improve code readability, and to make analyses more reproducible.

• Written dynamic documents using best practices for reproducible research, including using R Markdown. R Markdown intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. R Markdown also has excellent support for citation management and formatting for journals.

• Modeled simple statistical and causal problems using both classical regression (linear, logistic) and G-methods.

• Written robust functions, programmed with functions, and created a basic R package.

• Built ready-to-share web applications entirely in R using the Shiny framework.

Pre-requisites:

Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp: https://www.datacamp.com/courses/free-introduction-to-r  

Materials:

R for Data Science (https://r4ds.had.co.nz/) and Advanced R, ed. 2 (https://adv-r.hadley.nz/), both free online.

Course Information:

• Course language: English

• ECTS points: 3

• Course Fee: 510€ for students, 750€ for other participants

Registration:

Please send an e-mail to Tanja Te Gude

Tel. +49 30 450 570 812

Linking Research to Evidence-Based Action for Patients, Providers and Policy Decision Makers

Conference Co-Chairs: Beate Jahn, PhD, Silke Siebert, MD, Tobias Kurth, MD, MSc, ScD, and Uwe Siebert, MD, MPH, MSc, ScD

Call for Abstracts & Short Courses opens December 2019. 

Registration opens April 2020.

A modern researcher's toolbox: from methods to manuscripts

During this course, participants will in the first part develop a solid foundation of applied principles and methods of epidemiology; use these to evaluate relevant clinical and public health questions; and refine their ability to critically analyze the epidemiologic, clinical, and public health literature. In the second part, participants will learn about paper and proposal writing, writing styles and the editorial process. The format will include lectures, group exercises, and seminars. It is possible to book part 1 and 2 separately.

Lecturers:

• Julie E. Buring, ScD, Professor of Medicine, Harvard Medical School and Professor of Epidemiology, Harvard T.H. Chan School of Public Health

• Pamela M. Rist, ScD, Assistant Professor of Medicine, Harvard Medical School and Assistant Professor of Epidemiology, Harvard T.H. Chan School of Public Health

• Bill Miller, PhD, Professor at Ohio State University, USA

Part 1 23.03.- 24.03. (Julie Buring and Pamela Rist, Harvard University, Boston, MA)

• Introductions and Objectives of the Short Course, Critical Thinking in Epidemiology: Examples from the Literature

• Types of Epidemiologic Studies in the Literature (Descriptive, Analytic)

• Measures of Disease Frequency, Measures of Association and their Scales

• Interpretation of the Findings: Association versus Causation

• Critical Evaluation of the Medical Literature

• Principles of Randomized Trials; Strengths and Weaknesses

• Sample size and power, P-values and confidence intervals Screening

Please click here for a detailed schedule.

Part 2, 25.03.-27.03. (Bill Miller, Ohio State University)
Writing styles, manuscript format, paper and proposal writing: overview, writing process, scientific writing exercises, editorial process & reviewing, …

ECTS:
Students can take Parts 1&2 for 3 ECTS, only Part 1 for 1.25 ECTS or only Part 2 for 1.75 ECTS

Fees:
750 € (375 € for one part only)
510 € (255 € for one part only) for enrolled students (proof required)

Venue:
Charité Campus Mitte
Hans-Virchow-Hörsaal
Wilhelm-Waldeyer-Haus
Philippstraße 11
10115 Berlin

Registration:
Please send an e-mail to Tanja Te Gude
Tel. +49 30 450 570 812

The course introduces students to a general framework for the assessment of comparative effectiveness and safety, with an emphasis of the use of routinely collected data in healthcare settings. The framework relies on the specification and emulation of a hypothetical randomized trial: the target trial. The course explores key challenges for causal inference and critically reviews methods proposed to overcome those challenges. The methods are presented in the context of several case studies for cancer, cardiovascular, and renal diseases.

Course objectives: To learn how to determine “what works” using data from observational and randomized studies.

After successful completion of this course, students will be able to:

• Formulate sufficiently well-defined causal questions for comparative effectiveness research

• Specify the protocol of the target trial

• Design analyses of observational data that emulate the protocol of the target trial

• Identify key assumptions for a correct emulation of the target trial

Pre-course reading: Chapters 1-3 of the book Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, forthcoming. The book can be downloaded (for free) from http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

1.25 ECTS

Fees:

• 312,50 €

• 212,50 € for enrolled students (proof required)

Registration:

Please send an e-mail to Tanja Te Gude
Tel. +49 30 450 570 812

This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project.

The leading course in Neuroepidemiology with renown international faculty from France, Germany, Italy, and the US.

Program click here.

To register, click here.

The course is for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, who want to learn modern tools in the R ecosystem, like the tidyverse and Shiny, or who want to get started writing software in R.

Date: 02.09. - 06.09.2019

Lecturer: Malcolm Barrett, MPH

Malcolm Barrett is a PhD candidate in epidemiology at the University of Southern California. He studies vision loss and eye diseases that affect vision, like diabetic retinopathy. Specifically, he works on methods to improve the study of how vision impacts quality of life, including tools from psychometrics and causal inference, to make vision-specific quality of life analyses more accurate and more interpretable. In addition to applied research, Malcolm also develops R packages for epidemiologic and biostatistical methods, teaches R, and organizes the Los Angeles R Users Group. He regularly contributes to open source software, including favorite community projects like ggplot2 and R Markdown.

Learning objectives:

At the end of the week, participants will have:

• Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, to improve code readability, and to make analyses more reproducible.

• Written dynamic documents using best practices for reproducible research, including using R Markdown. R Markdown intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. R Markdown also has excellent support for citation management and formatting for journals.

• Modeled simple statistical and causal problems using both classical regression (linear, logistic) and G-methods.

• Written robust functions, programmed with functions, and created a basic R package.

• Built ready-to-share web applications entirely in R using the Shiny framework.

Pre-requisits:

Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp: https://www.datacamp.com/courses/free-introduction-to-r  

Materials:

R for Data Science (https://r4ds.had.co.nz/) and Advanced R, ed. 2 (https://adv-r.hadley.nz/), both free online.

Course Information:

• Course language: English

• ECTS points: 3

• Course Fee: 510€ for students, 750€ for other participants

Contact: tanja.tegude@charite.de

Lecturers:

• Dr. Dipl.-Vw. Josef Schepers, Berlin Institute of Health

• Prof. Dr. Sylvia Thun, Berlin Institute of Health

• Prof. Dr. Dr. Felix Balzer and team, Charité - Universitätsmedizin Berlin

• Prof. Dr. Igor M. Sauer, Charité - Universitätsmedizin Berlin

• Prof. Dr. Bert Arnrich, Hasso-Plattner-Institute

• Dr. Kai Heitmann, HL7 Deutschland

• Prof. Dr. Markus Feufel, Technical University Berlin

Topics:

• Interoperability and standards

• Connected Health

• Digital Surgery - Extended Reality (XR) and Robotics in Visceral Surgery

• Human factors (tbd)

• Telemedicine/Remote Patient Monitoring

• Data management and registries

Prerequisites:

• Basic analytic background (statistics, epidemiology), basic computing skills

Fees:

• 750 €

  510 € for enrolled students (proof required)

Examination: Several multiple choice quizzes

3 ECTS

Contact: tanja.tegude@charite.de

Lecturer:

Matthew Fox, Professor in the Department of Global Health and Epidemiology at Boston University School of Public Health

Learning objectives:

By the end of this week, participants should be able to:

• Use the sufficient cause model, counterfactual susceptibility type model, and a causal graph to assist with the design or analysis of an epidemiologic study.

• Calculate adjusted measures of effect and select those that, when collapsible, correspond to the no-confounding condition. Use the adjusted measures of effect to estimate the direction and magnitude of confounding.

• Distinguish effect measure modification, interdependence, and statistical interaction from one another as separate - but related - concepts of interaction.

• Identify the likely magnitude and direction of bias due to misclassification of exposure, outcomes, confounders and modifiers. Weigh the advantages and disadvantages of significance testing.

• Compare the advantages and disadvantages of frequentist and Bayesian approaches to analysis of a single study, to eveidence, and to changing your mind.

Prerequisites:

• Basic knowledge of epidemiology and biostatistics

Fees:

• 750 €

• 510 € for enrolled students (proof required)
  
  

3 ECTS

Contact: tanja.tegude@charite.de

Lecturers:

• Prof. Dr. Sylvia Thun, Berlin Institute of Health

• Prof. Dr. Dr. Felix Balzer, Charité - Universitätsmedizin Berlin

• Dipl.-Ing. Andreas Kofler, Charité - Universitätsmedizin Berlin

• Prof. Dr. Tim Conrad, Free University Berlin & Zuse Institute Berlin

• Dr. Martin Haase, Technical University Berlin

Topics:

• Introduction to Health Information Technology

• Information Extraction from Electronic Health Records

• Terminologies and Ontologies

• Introduction to programming

• Medical imaging, pattern recognition

• Analysis of large data-sets

• Data protection and regulatory aspects

Prerequisites:

• Basic analytic background (statistics, epidemiology), basic computing skills

Fees:

• 750 €

• 510 € for enrolled students (proof required)

Examination: Several multiple choice quizzes

• 3 ECTS

Description
This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project. HSPH Course Prerequisite(s): EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently.

Prerequisite(s)
HSPH Prerequisite: EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently. Students outside of HSPH must request instructor permission to enroll in this course

Lecturers: Rolf H.H. Groenwold, MD, PhD, Leiden University Medical Center (NL) & 
Maarten van Smeden, PhD, Leiden University Medical Center (NL)

By the end of this week, participants should be able to:

• Critically assess the results of epidemiological studies on causal relationships or prediction models

• Correctly define exposures and learn how to best represent them in models

• Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.

• Describe key assumptions of methods used to control for (time-varying) confounding.

• Describe key assumptions of methods used to handle missing observations.

• Understand the reasons for and consequences of overfitting prediction models

• Describe recent developments in the fields of causal research and prediction modelling

Prerequisites:

• Basic knowledge of epidemiology

• Familiarity with R statistical software (for a short introduction see http://www.r-tutorial.nl/)

Fees:

• 750 €

• 510 € for enrolled students (proof required)

3 ECTS

Registration Information:
Tanja Te Gude
Tel. +49 30 450 570 812