Bias in Epidemiologic Research
Introduction
Potential biases are likely to be present and affect epidemiologic research at the design, implementation, and analysis of a study. By definition bias is defined as systematic errors that can affect the epidemiologic research hence leading to invalid measures of association and altered study outcomes (Aschengrau & Seage, 2008). There are two main categories of biases in epidemiologic research, and they include selection bias and information bias (Aschengrau & Seage, 2008).
Epidemiologic biases
In epidemiologic research, biases occur when the estimated association varies or deviates significantly from the true measure of association. The bias has an impact on the Risk Ratio (RR) and Odd Ratio (OR) estimates. Random or systematic errors are thought to be the leading cause epidemiologic biases.
Selection error is a type of bias that immanent from improper or differential procedures of selection of study participants from the target population to become members of the study population (Rothman, Greenland & Lash, 2008). This bias usually results in variance between the exposure and disease for persons who complete the study as compared to the target population. Selection bias leads to distortion of measures of association such as odd ratio, risk ratio, or rate ratio (Rothman, Greenland & Lash, 2008).
Information error in epidemiologic research is the distortion of measures of association estimates due to inaccurate measurements or classification of exposure or disease status (Rothman, Greenland & Lash, 2008). This type of errors arises even if the measure estimates produced are equal between diseased or non-diseased, or between diseased and non-diseased study members. Confounding bias is common in the pharmaceutical field and occurs when the in question is selectively used or not used by the individual who developed the outcome of interest. It is common whereby the casual relationship between the drug and its effects disease treatment cannot sufficiently be established (Fletcher & Fletcher, 2005).
The cohort study to look at the association of pesticides and the occurrence of childhood leukemia is likely to be affected with selection bias. The selection of children exposed pesticides to determine whether the outcome of interest, that is, occurrence of leukemia in high and low area may be biased. The systematic error in the design, conduct, and analysis of the study results about the level of exposure to leukemia is likely to be overestimated since there are children with family history of leukemia that are more likely to participate (Gabor, et. al, 2008).
The second scenario where the HIV positive and negative people are asked by the number of sexual they have had in their lifetime is bound to be affected by information bias. The information may be inaccurate depending on the status of the persons hence may overestimate or underestimate the measure of association between the positive and negative persons (Tripepi, Jager, Dekker & Zoccali, 2010). Cohort study about exposure PCB in the workplace and occurrence of cancer over 20 years is likely to yield selection bias. This is attributed to loss of follow up participants to assess their disease exposure status (Schoenbach, 1999). This lead to underestimation of measure of association such as RR and OR. Confounding bias is more likely to affect the clinical trial in scenario four. The clinical trials are conducted by a doctor who has prior knowledge about the outcome of interest hence it is going to effect on the outcome of the outcome of the drug (Fletcher & Fletcher, 2005).
Conclusion
When conducting epidemiologic research it of considerable importance to be mindful of all biases that are likely to affect the outcomes of the study. Proper measures should be put in place to minimize the biases thereby improving on the quality of the outcomes. It begins with proper research design and efficient conducting of the research.
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