Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

COVID-19 disrupted patterns of cause-specific mortality in Switzerland.

International journal of public health·2026
Same author

Preoperative stress markers as predictors of postoperative neuropsychological disorders in cardiac surgery patients: protocol for a single-centre prospective observational study (CAVALIR).

BMJ open·2026
Same author

Supportive care for talquetamab-related dysgeusia in multiple myeloma: mixed-methods evidence, nutrition-focused flowchart, and digital companion concept.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Operative timing and surgical complexity in hand trauma: a multicenter analysis of replantation and non-replantation centers in Germany.

Archives of orthopaedic and trauma surgery·2026
Same author

Defining "regional" pain.

Pain·2026
Same author

Conditioned Pain Modulation Inter-Site Variability Study: Effect Sizes and Test-Retest Reliability of Two Models.

European journal of pain (London, England)·2026

Related Experiment Video

Updated: Jun 18, 2026

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research
04:50

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research

Published on: August 4, 2023

1.3K

A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

Heidi Seibold1,2,3,4, Severin Czerny1, Siona Decke1

  • 1Department of Statistics, LMU Munich, Munich, Germany.

Plos One
|June 21, 2021
PubMed
Summary

Reproducing computational research, especially complex longitudinal data analysis, is challenging without source code. The study found that open data policies alone are insufficient; open code policies are crucial for enhancing research reproducibility.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K
Author Spotlight: Exploring the Impact of Trauma on Cellular Aging
11:44

Author Spotlight: Exploring the Impact of Trauma on Cellular Aging

Published on: March 22, 2024

2.5K

Related Experiment Videos

Last Updated: Jun 18, 2026

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research
04:50

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research

Published on: August 4, 2023

1.3K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.6K
Author Spotlight: Exploring the Impact of Trauma on Cellular Aging
11:44

Author Spotlight: Exploring the Impact of Trauma on Cellular Aging

Published on: March 22, 2024

2.5K

Area of Science:

  • Computational statistics
  • Research methodology
  • Data science

Background:

  • Computational reproducibility is essential for credible scientific research.
  • Reproducing complex statistical analyses, particularly for longitudinal data, is often difficult, especially when source code is unavailable.
  • Existing open data policies may not fully address the challenges in ensuring research reproducibility.

Purpose of the Study:

  • To assess the reproducibility of data analyses from 11 PLOS ONE articles featuring longitudinal data.
  • To investigate the methods, software used, and the feasibility of reproducing analyses with open-source software.
  • To identify barriers to computational reproducibility and propose solutions.

Main Methods:

  • Selection of 11 PLOS ONE articles with available data and author consent for analysis reproduction.
  • Examination of statistical methods (e.g., Generalized Estimating Equations) and software employed in the selected articles.
  • Attempted replication of data analyses, including reverse engineering and author contact, using open-source tools where possible.

Main Results:

  • Most articles provided only summary tables and visualizations, with limited or no analysis code shared.
  • Generalized Estimating Equations were the predominant statistical models used.
  • Reproducing the analyses proved difficult, with significant reliance on author communication; three results were irreproducible, and two were only partially reproducible.

Conclusions:

  • The lack of shared source code presents a substantial barrier to computational reproducibility in scientific research.
  • While open data is beneficial, it is insufficient on its own to guarantee the reproducibility of complex analyses.
  • Implementing mandatory open code policies alongside open data policies is recommended to significantly improve research reproducibility.