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Related Concept Videos

The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Standard Error of the Mean01:13

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
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Errors In Hypothesis Tests

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Updated: May 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

On computing standard errors for marginal structural Cox models.

R Ayesha Ali1, M Adnan Ali, Zhe Wei

  • 1Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada, aali@uoguelph.ca.

Lifetime Data Analysis
|April 19, 2013
PubMed
Summary
This summary is machine-generated.

This study demonstrates that R

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Related Experiment Videos

Last Updated: May 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Marginal structural models (MSMs) are crucial for handling time-dependent confounders in longitudinal studies.
  • Time-dependent weighting is a key technique within MSMs.
  • Standard software packages face challenges computing standard errors for Cox models with time-dependent weights.

Purpose of the Study:

  • To extend Cox model calculations for time-dependent weights.
  • To validate the use of R's coxph procedure for robust standard errors.
  • To introduce and evaluate a Cox score bootstrap for standard error computation.

Main Methods:

  • Extension of Cox model calculations to accommodate time-dependent weights.
  • Simulation study to assess the performance of robust standard errors.
  • Development and application of a Cox score bootstrap procedure.

Main Results:

  • R's coxph procedure can compute asymptotic robust standard errors for Cox models with time-dependent weights.
  • Robust standard errors are conservative, but confidence intervals show good coverage.
  • The Cox score bootstrap is efficient and performs well in small samples, outperforming non-parametric bootstrap.

Conclusions:

  • The R coxph procedure is a viable tool for MSMs with time-dependent weights.
  • The Cox score bootstrap offers an efficient alternative for standard error estimation in these models.
  • These methods improve the practical application of MSMs in statistical and epidemiological research.