The Bradford Hill aspects to consider when evaluating evidence to assess whether an association is causal have important exceptions and qualifications; therefore, the aspects, however useful, are neither criteria nor hard and fast rules for assessing causality (Rothman and Greenland, 2005). The validity of data and individual studies that may contribute evidence as to whether an association is causal must also be considered. Although strict rules are not available, design flaws that threaten the validity of a study often fall into one of three major categories: selection bias, confounding, and misclassification or information bias. Of particular relevance here is the healthy warrior effect described earlier. Failure to account for such differences can lead to biased estimates of an effect. These factors were all considered by the committee in evaluating the quality of data and individual studies, in determining the primary and secondary literature that would be used to draw conclusions, and in evaluating how those studies contribute to the body of evidence concerning health effects seen in Gulf War veterans.


Bias refers to systematic, or nonrandom, error. Bias causes an observed value to deviate from the true value, and can weaken an association, strengthen an association or generate a spurious association. Because all studies are susceptible to bias, a primary goal of the research design is to minimize bias or to adjust the observed value of an association by correcting for bias if the sources are known. There are different types of bias, such as selection bias. Selection bias refers to a systematic error in the way subjects are identified, recruited, included, excluded, or the way they participate in the study that leads to a distortion of the true association.

Information bias results from the manner in which data are collected and can result in measurement errors, imprecise measurement, and misdiagnosis. Those types of errors might be uniform in an entire study population or might affect some parts of the population more than others. Information bias might result from misclassification of study subjects with respect to the outcome variable or from misclassification of exposure. Other common sources of information bias are the inability of study subjects to recall the circumstances of their exposure accurately (recall bias) and the likelihood that one group more frequently reports what it remembers than another group (reporting bias). Information bias is especially harmful in interpreting study results when it affects one comparison group more than another.


Confounding occurs when a variable or characteristic otherwise known to be predictive of an outcome and associated with the exposure (and not on the causal pathway under consideration) can account for part or all of an apparent association. A confounding variable is an uncontrolled variable that influences the outcome of a study to an unknown extent, and makes precise evaluation of its effects impossible. Carefully applied statistical adjustments can often control for or reduce the influence of a confounder.

Sampling Error

Sampling error (sometimes referred to as chance or random error) is a type of error that can lead to an apparent association between an exposure to an agent and a health effect when no

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