Related Experiment Video
Updated: Aug 13, 2026
10:38
A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
Published on: July 16, 2015
Sensitivity analysis for contamination in egocentric-network randomized trials with interference
1Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, 6997801, Israel.
Biostatistics (Oxford, England)
|August 12, 2026
Summary
Egocentric-network randomized trials (ENRTs) can be biased by network contamination. This study introduces bias-corrected estimators and sensitivity analysis to improve causal effect estimation in ENRTs.
Area of Science:
- Epidemiology
- Biostatistics
- Social Network Analysis
Background:
- Egocentric-network randomized trials (ENRTs) are valuable for causal inference in complex networks when full network data is unavailable.
- ENRTs involve sampling individuals (egos) and their connections (alters), with treatments randomized at the ego level.
- Potential contamination exists due to unobserved connections between sampled ego-networks in the broader population.
Purpose of the Study:
- To investigate the bias in Horvitz-Thompson estimators for direct and indirect effects in ENRTs due to network contamination.
- To develop and validate bias-corrected estimators for causal effects in the presence of contamination.
- To introduce a novel sensitivity analysis framework for assessing the robustness of ENRT causal estimates.
Main Methods:
- Derived bias-corrected Horvitz-Thompson estimators for direct and indirect effects.
- Developed a sensitivity analysis framework using parameters for missing edges (probability or expected number).
- Implemented the framework through grid sensitivity analysis and probabilistic bias analysis.
Main Results:
- Demonstrated that network contamination biases standard Horvitz-Thompson estimators in ENRTs.
- The proposed bias-corrected estimators and sensitivity analysis framework were developed.
- Application to the HIV Prevention Trials Network 037 study indicated potential underestimation of indirect effects and overestimation of direct effects when contamination is ignored.
Conclusions:
- Network contamination is a critical issue in ENRTs that can lead to biased causal effect estimates.
- The developed bias-corrected estimators and sensitivity analysis provide essential tools for robust causal inference in ENRTs.
- Researchers using ENRTs should consider and assess potential network contamination to ensure the validity of their findings.
Related Concept Videos
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:27Confounding in Epidemiological Studies
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 phenomenon...
01:16Contaminants and Errors
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
01:09Censoring Survival Data
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...