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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...
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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An R-Based Landscape Validation of a Competing Risk Model
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Estimating conflict losses and reporting biases.

Benjamin J Radford1,2, Yaoyao Dai3, Niklas Stoehr4

  • 1Public Policy Program, University of North Carolina at Charlotte, Charlotte, NC 28223.

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Estimating conflict casualties is challenging due to unreliable data. This study developed a statistical model to analyze 4,609 reports, finding Russia suffered more personnel losses than Ukraine and both sides exaggerated enemy losses.

Keywords:
Bayesian statisticscasualtiesnews biasopen-source datawar

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Area of Science:

  • Conflict studies
  • Statistical modeling
  • International relations

Background:

  • Accurate casualty and fatality data are crucial for conflict analysis, measurement, and accountability.
  • Reliable statistics are often scarce in militarized conflicts due to intentional manipulation or lack of access.
  • The Russia-Ukraine conflict exemplifies wide variations in reported loss estimates.

Purpose of the Study:

  • To develop a robust methodology for estimating conflict casualties and fatalities using multiple, potentially biased, reporting sources.
  • To account for source biases, interdependencies in loss types, and varying temporal scales of reporting.
  • To provide more accurate estimates of personnel losses in the Russia-Ukraine conflict.

Main Methods:

  • Compilation of a dataset containing 4,609 reports on military and civilian losses from both sides of the conflict.
  • Development of a statistical model designed to estimate losses while accounting for reporting biases and correlations.
  • Integration of loss reports across different temporal granularities (daily and cumulative).

Main Results:

  • The statistical model indicates that Russia has sustained higher personnel losses compared to Ukraine.
  • Evidence suggests Russia likely has a higher fatality-to-casualty ratio.
  • Both Russia and Ukraine appear to overestimate their opponent's personnel losses, while Russian sources tend to underestimate their own losses.

Conclusions:

  • The developed statistical approach offers a more reliable method for estimating casualties in conflicts with biased reporting.
  • Findings suggest significant asymmetries in personnel losses and reporting accuracy between Russia and Ukraine.
  • The study highlights the need for critical assessment of data sources in conflict research and intelligence gathering.