How the bias would affect the measure of association. Would it be under or over estimated?

A cohort study was designed to look at the association of pesticides and the occurrence of childhood leukumia. Children living in rural areas were followed and leukemia rates were compared between those children living in areas with high and low pesticides exposure. pesticide exposure was considered high if it was over 20 "units" and low if it was under 20 "units". The method used to quantify pesticide exposure was accurate to within +/-12 units.

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In this scenario, bias can affect the measure of association between pesticide exposure and childhood leukemia. Bias refers to systematic errors or distortions in the way data is collected, analyzed, or interpreted, which can lead to incorrect conclusions.

If the bias is present in this cohort study, it can result in an overestimation or underestimation of the association between pesticides and childhood leukemia. Let's consider both possibilities:

1. Overestimation: Suppose there is a bias in the way pesticide exposure is measured, resulting in an overestimation of the exposure levels. This means that some children who should be classified as having low exposure may be mistakenly classified as having high exposure due to measurement error. As a result, the observed association between pesticide exposure and childhood leukemia may appear stronger than it actually is. Therefore, the measure of association would be overestimated.

2. Underestimation: Conversely, if there is a bias in the measurement of pesticide exposure that leads to an underestimation of the exposure levels, some children with high exposure may be misclassified as having low exposure. This misclassification can weaken the observed association between pesticide exposure and childhood leukemia, making it appear less significant or even non-existent. In this case, the measure of association would be underestimated.

To minimize bias, it is important to have an accurate and reliable method for quantifying pesticide exposure. It is mentioned that the method used to quantify pesticide exposure has an accuracy of +/-12 units. However, it's crucial to assess the potential for additional sources of bias in the study, such as selection bias or confounding. Additionally, consider conducting sensitivity analyses to explore the impact of different measurement assumptions on the results, which can provide helpful insights into the potential influence of bias.