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

Biostatistics: Overview01:20

Biostatistics: Overview

1.0K
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
1.0K
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

5.5K
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
5.5K
Statgraphics01:10

Statgraphics

452
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
452
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.8K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

689
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
689
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.1K
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:
1.1K

You might also read

Related Articles

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

Sort by
Same author

Generating evidence using electronic alerts during routine care: a fully automated randomized controlled trial of oral fluid restriction in acute heart failure (THIRST alert trial).

European heart journal. Digital health·2026
Same author

Comparing risk factors in severe COVID-19 using machine learning and non-machine learning methods: analysis from 2 international randomized controlled trials.

JAMIA open·2026
Same author

PROTOCOL: Comparative Effectiveness of Tools for Communicating Health Research Results to Study Participants and Others With Relevant Lived Experience: A Living Systematic Review of Randomized Controlled Trials.

Campbell systematic reviews·2026
Same author

Reporting of Cohort and Routinely Collected Data in Randomised Controlled Trial Protocols (SPIRIT-ROUTINE): extension checklist with explanation and elaboration.

BMJ (Clinical research ed.)·2026
Same author

Language-related eligibility criteria in UK randomised trials: a systematic review of extended research reports.

Trials·2026
Same author

On-treatment serum prostate-specific antigen and overall survival in prostate cancer (STAMPEDE platform protocol): a post-hoc analysis of data from five phase 3 trials.

The Lancet. Oncology·2026

Related Experiment Video

Updated: Mar 18, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K

Data sharing and the evolving role of statisticians.

Nick Manamley1, Steve Mallett2, Matthew R Sydes3,4

  • 1Amgen Ltd., 240 Cambridge Science Park, Cambridge, CB4 0WD, UK. nick.manamley@amgen.com.

BMC Medical Research Methodology
|July 14, 2016
PubMed
Summary

Statisticians are becoming data facilitators, not gatekeepers, due to increased data sharing in clinical research. This evolution requires new skills in data privacy, analysis critique, and collaboration for pharmaceutical and academic sectors.

Keywords:
Academic researchBiotechnology researchClinical trialData sharingPharmaceutical researchStatisticianTransparency

More Related Videos

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.6K
A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

440

Related Experiment Videos

Last Updated: Mar 18, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.6K
A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

440

Area of Science:

  • Biostatistics
  • Pharmaceutical Research
  • Data Science

Background:

  • Growing demand for transparency and data sharing in clinical research.
  • Statisticians play a crucial role in facilitating data sharing discussions and policy shaping.
  • Data sharing initiatives are reshaping the required skill set for statisticians in various sectors.

Discussion:

  • Statisticians must evolve from data gatekeepers to data facilitators, embracing broader responsibilities.
  • New skills in data privacy, anonymization, and re-identification risk assessment are essential.
  • Enhanced consultancy skills are needed to defend statistical practices and critique analyses.

Key Insights:

  • The role of statisticians is expanding beyond traditional boundaries to manage data sharing challenges.
  • Understanding data transparency benefits and strategic advantages is crucial for statisticians.
  • Statisticians must be adept at interpreting external analyses and addressing discrepancies.

Outlook:

  • Statisticians will require expertise in data privacy regulations and anonymization techniques.
  • Developing strong scientific and statistical principles for reanalysis is vital.
  • Adapting to these changes will maximize the value of shared data and prevent misleading conclusions.