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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Regression Analysis01:11

Regression Analysis

5.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.5K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Correction: Somatic morbidity in bipolar disorders.

International journal of bipolar disorders·2026
Same author

Long-Term Opioid Therapy Tapering and Risk of Substance Use Disorder and Overdose: Differences by Opioid Dose and Duration.

Journal of general internal medicine·2026
Same author

Measured and Estimated Glomerular Filtration Rates and Risk of Adverse Health Outcomes.

JAMA·2026
Same author

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same author

Somatic morbidity in bipolar disorders.

International journal of bipolar disorders·2026
Same author

Evaluation of clinical utility in emulated clinical trials.

European journal of epidemiology·2026
Same journal

GLP-1 receptor agonist medications for weight loss: Sociodemographic patterns of awareness, use, and access in a U.S. national cohort.

Annals of epidemiology·2026
Same journal

Even low levels of physical activity are associated with lower all-cause mortality: A cohort study of 594,000 US adults.

Annals of epidemiology·2026
Same journal

Comment on "Distinct differences between COVID-19 vaccine refusers and vaccine hesitant in the United States: Results from a population-based survey and latent class analysis".

Annals of epidemiology·2026
Same journal

Translating caregiver-identified neighborhood features into quantitative variables for statistical analyses of effects on child health.

Annals of epidemiology·2026
Same journal

Associations between the built environment and body mass index among a residentially stable cohort of mid-older aged adults in Brisbane, Australia (2007-2016).

Annals of epidemiology·2026
Same journal

Unmeasured air pollution, adiposity measurement, and life-stage transitions in the HABITAT built environment-BMI analysis.

Annals of epidemiology·2026
See all related articles

Related Experiment Video

Updated: May 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

The next generation of regression standardization with the R package stdReg2.

Michael C Sachs1, Johan Sebastian Ohlendorff1, Adam Brand2

  • 1University of Copenhagen, Section of Biostatistics, Øster Farimagsgade 5, Copenhagen, Denmark.

Annals of Epidemiology
|April 10, 2025
PubMed
Summary
This summary is machine-generated.

The R package stdReg2 enhances causal inference in epidemiology with improved user-friendliness and new double-robust methods for average treatment effect estimation. This updated statistical software offers greater flexibility for researchers.

Keywords:
Average treatment effectCausal InferenceRegression standardizationStatistical software

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

185

Related Experiment Videos

Last Updated: May 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.0K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

185

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Software Development

Background:

  • Regression standardization is a key technique for causal inference in epidemiological studies.
  • Existing statistical software may lack user-friendliness or advanced functionalities.
  • There is a need for updated tools to facilitate robust causal analysis.

Purpose of the Study:

  • To introduce the upgraded R package stdReg2, enhancing causal inference capabilities.
  • To detail new features including a double-robust method and survival analysis standardization.
  • To provide guidance for researchers on utilizing the improved statistical software.

Main Methods:

  • Updating the R package stdReg to stdReg2.
  • Implementing a generalized linear model-based double-robust method.
  • Adding regression standardization for restricted mean survival time.

Main Results:

  • The stdReg2 package offers improved user-friendliness and flexibility over stdReg.
  • New functionalities enable advanced causal inference techniques.
  • The package supports both standard regression standardization and restricted mean survival analysis.

Conclusions:

  • stdReg2 is a valuable update for epidemiological research requiring causal inference.
  • The enhanced statistical software facilitates more robust and flexible analysis.
  • New and existing users are encouraged to adopt stdReg2 for their research needs.