Question

A systematic deviation of results or inferences from the truth includes O confounding O recall bias O selection bias O healthy worker effect O all of the above

Answer 1

Answer:- All of the above.

What is bias?

Bias is the lack of neutrality or prejudice. It can be simply defined as "the deviation from the truth". In scientific terms it is "any factor or process that tends to deviate the results or conclusions of a trial systematically away from the truth. Such deviation leads, usually, to over-estimating the effects of interventions making them look better than they actually are.

Bias can occur and affect any part of a study from its planning phase to its publication. It arises mainly due to the adoption of an inadequate design, misconduct of the research methodology or the inadequate analysis of data. As research is important for determining whether a new intervention is effective or not and if effective what is the magnitude of its effectiveness, bias is obviously detrimental to research and hence to clinical practice.

There are many types of bias that affect scientific research.

Confounders

Research aims primarily at measuring the association between two variables; an intervention (or exposure) and an outcome. This can be achieved by designing a comparative research with at least two groups; one receiving the intervention under investigation (study group) and another either receiving a placebo or another intervention (a control group). The outcomes in both groups are then compared. But to study the effect of interventions properly one important pre-requisite is that participants in both groups (the study group and the control one) should be similar in all characteristics except for the intervention being studied.

A confounder is defined as "a variable, other than the one studied, that can cause or prevent the outcome of interest." For any outcome in research there are many confounders that should be considered in the planning phase of the trial, reported in the results section and analyzed for significant differences between the groups. Any confounding variable should be equally distributed in the two groups to give balanced groups. Some other examples of confounders are the effect of smoking, life style, and dietary habits on bone mineral density and the frequency of sexual intercourse, duration of sexual activity, and number of partners on cancer cervix.

Selection bias

Interferences from researchers to divide patients into groups (select which patient goes to which group) will result in dissimilar or unbalanced groups and would introduce bias into the study. Such type of bias is known as "selection bias." If investigators "thought wrongly" that they can equally distribute or balance all the basic characteristics and risk factors or confounders between the groups, they definitely can not ensure balancing unknown risk factors or unknown confounders. The best way of eliminating selection bias, then, is by randomizing patients properly into groups.

Recall bias:- Systematic error due to differences in accuracy or completeness of recall to memory of past events or experiences.

Recall bias is a systematic error that occurs when participants do not remember previous events or experiences accurately or omit details: the accuracy and volume of memories may be influenced by subsequent events and experiences. Recall bias is a problem in studies that have self-reporting, such as case-control study and retrospective cohort studies.

The "Healthy Worker" Effect

The "health worker" effect is really a special type of selection bias that occurs in cohort studies of occupational exposures when the general population is used as the comparison group. The general population consists of both healthy people and unhealthy people. Those who are not healthy are less likely to be employed, while the employed work force tends to have fewer sick people. Moreover, people with severe illnesses would be most likely to be excluded from employment, but not from the general population. As a result, comparisons of mortality rates between an employed group and the general population will be biased.

for example, that a given occupational exposure truly increases the risk of death by 20% (RR=1.2). Suppose also that the general population has an overall risk of death that is 10% higher than that of the employed workforce. Given this scenario, use of the general population as a comparison group would result in a underestimate of the risk ratio, i.e. RR=1.1.