Confounding Variables and Bias Discussion

 

 

Bias and confounding in epidemiological study may reflect the true effect of an exposure on the development of and outcome under investigation, this should always be considered that the findings may be the fact that is due to an alternative problem. If the alternative may be due to the effects of a random error, bias or confounding may produce spurious results, leading us to conclude the existence of a valid statistical association when one does not exist or alternatively the absence of an association when one is truly present (Carneiro I, et al, 2011). While in the observational studies are particularly susceptible to the effects of chance, bias and confounding and these factors need to be considered at both the design and analysis stage of an epidemiological study so that their effects can be minimized. Bias is a systematic error in an epidemiological study that results in an incorrect estimate of the true effect of an exposure on the outcome of interest. It can also result from systematic errors in the research methodology, of which the estimate is either above or below the true value, depending on the direction of the systematic error (Choudhary, P., et al, 2020). Next is the magnitude of bias is generally difficult to quantify, and limited scope exists for the adjustment of most forms of bias to analysis stage. As a result, careful consideration and control of the ways in which bias may be introduced during the design and conduct of the study is essential in order to limit the effects on the validity of the study results. There are different types of bias that have been identify in epidemiological studies, but for simplicity they can be broadly grouped into two categories: information bias and selection bias. While confounding is Confounding provides an alternative result for an association between an exposure and an outcome. It occurs when an observed association is in fact distorted because the exposure is also correlated with another risk factors. This risk factor is also associated with the outcome, but independently of the exposure will be under investigation, as a result, if the consequence, the estimated association is not that same as the true effect of exposure on the outcome. An unequal distribution of the additional risk factor, between the study groups will result in confounding. The observed association may be due totally, or in part, to the effects of differences between the study groups rather than the exposure under investigation. A potential confounder is any factor that might influence the risk of disease under study (Dechao Feng, et al, 2020). This may include factors with a direct causal link to the disease, as well as factors that are measures for other unknown causes, such as age and socioeconomic status.

For a variable to be considered as a confounder, it must be associated with the outcome, example, a person who is at risk. Next the variable must also be associated with the exposure under study in the source population and should not be lie on the causal pathway between exposure and disease outcome. When studying alcohol use consumption to be associated with the risk of coronary heart disease (CHD). However, smoking may have confounded the association between alcohol and CH. Smoking is a risk factor for CHD, so is independently associated with the outcome, and smoking is also associated with alcohol consumption because smokers tend to drink more than non-smokers. Controlling for the potential confounding effect of smoking may in fact show no association between alcohol consumption and confounding factor, if not controlled for, cause bias in the estimate of the imp when act of an exposure. Effects of confounding may result in observed association when no real association exists, no observed association when a true association does exist and when an underestimate and overestimate of the association is in a negative and positive confounding (Hennekens CH, et al, 1987). Confounding can be addressed either at the study design stage or adjusted to analysis stage to provide enough relevant data that have been collected. Several methods can be applied to control the potential confounding factors and aim that will make the groups as similar as possible with respect to confounding cases. Potential confounding factors may be identified at the design stage that is based on previous studies or link between the factor and outcome may be considered as biologically Methods to limit confounding at the design stage include randomization, restriction and matching ( Choudhary, P.,et al, 2020).

This is the ideal method of controlling for confounding because all potential confounding variables, both known and unknown, should be equally distributed between the study groups. It involves the random allocation (e.g. using a table of random numbers) of individuals to study groups. However, this method can only be used in experimental clinical trials. In restriction it helps limits participate in the study of an individuals who relate to the confounder. For example, if participate in a study is restricted to non-smokers only, any potential confounding effect of smoking will be eliminated. However, a disadvantage of restriction is that it may be difficult to generalize the results of the study to the wider population if the study group is homogenous. While matching involves selecting controls so that the distribution of potential confounders, such as age or status of those that smoke is as similar as possible to other case. In practice this is only utilized in case-control studies, but it can be done in two ways, like pair matching, selecting for each case one or more controls with similar characteristics of the age of smoking habits. Also, the frequency matching is to ensure that as a group the cases have similar characteristics to the controls (Xiao Hu, et al, 2020).

References: 

Choudhary, P., & Nain, N. (2020). CALAM: model-based compilation and linguistic statistical analysis of Urdu corpus. Sadhana, 45(1), 1–10. https://doi-org.lopes.idm.oclc.org/10.1007/s12046-019-1237-3

Dechao Feng, Xiao Hu, Yin Tang, Ping Han, & Xin Wei. (2020). The efficacy and saety of miniaturized percutaneous nephrolithotomy versus standard percutaneous nephrolithotomy: A systematic review and meta-analysis of randomized controlled trials. Investigative & Clinical Urology, 61(2), 115–126. https://doi-org.lopes.idm.oclc.org/10.4111/icu.2020.61.2.115

Hennekens CH, Buring JE. Epidemiology in Medicine, Lippincott Williams & Wilkins, 1987.

Carneiro I, Howard N. Introduction to Epidemiology. Open University Press, 2011.

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