Related Experiment Video
Updated: Jun 14, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.0K
unmconf : an R package for Bayesian regression with unmeasured confounders
Ryan Hebdon1, James Stamey2, David Kahle2
1Department of Statistical Science, Baylor University, Waco, TX, USA. Ryan_Hebdon@baylor.edu.
BMC Medical Research Methodology
|September 7, 2024
Summary
Unmeasured confounding biases study results. The new R package unmconf offers probabilistic sensitivity analysis via Bayesian modeling, improving parameter estimates and uncertainty assessments in observational research.
Area of Science:
- Biostatistics
- Observational Studies
- Statistical Software Development
Background:
- Unmeasured confounding can significantly bias parameter estimates, invalidate uncertainty assessments, and lead to erroneous conclusions in observational studies.
- Sensitivity analysis is crucial for assessing unmeasured confounding, but accessible software for probabilistic methods has been lacking.
- Existing R packages primarily focus on deterministic sensitivity analysis, leaving a gap for probabilistic approaches.
Purpose of the Study:
- To introduce the R package `unmconf`, the first package designed for probabilistic sensitivity analysis of unmeasured confounding using a Bayesian approach.
- To provide a user-friendly tool for Bayesian modeling in the presence of unmeasured confounders, simplifying complex computations.
- To evaluate the performance and applicability of the `unmconf` package through simulation studies.
Main Methods:
- Development and implementation of the `unmconf` R package for Bayesian unmeasured confounding models.
- The package supports various response types (normal, binary, Poisson, gamma) and accommodates one or two unmeasured confounders.
- Simulation studies were conducted to assess the package's performance across different distributional families and validation data levels.
Main Results:
- The `unmconf` package enables probabilistic sensitivity analysis for unmeasured confounding.
- Modeling unmeasured confounders using `unmconf` led to credible intervals with near-nominal coverage probability.
- The package demonstrated a reduction in bias across various simulation scenarios, including different response and confounder distributions.
Conclusions:
- The `unmconf` R package provides a valuable and accessible tool for addressing unmeasured confounding in observational studies.
- Utilizing this package can improve the accuracy of parameter estimates and the reliability of uncertainty assessments.
- The findings support the use of `unmconf` for robust statistical inference when unmeasured confounding is a concern.
Related Concept Videos
01:27Confounding in Epidemiological Studies
151
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
151
01:18Introduction To Survival Analysis
197
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
197
01:15Assumptions of Survival Analysis
111
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
111
01:21Confidence Intervals
6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
A...
6.2K
01:26Distributions to Estimate Population Parameter
4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
01:25Statistical Methods for Analyzing Epidemiological Data
330
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:
330