Question

In this discussion, you will have an opportunity to make connections between your prior knowledge and the new content you are learning this week. Use your results from this week’s Show What You Know diagnostic to provide context for your prior knowledge and consider the new content about data in the real world to answer the following questions in a minimum of 175 words:

• How might bias impact a study?
• How might bias be reduced?

Answer 1

This question is a theoretical question and requires a detailed understanding of bias and its properties includes impacts.

Let us firstly try to understand as to what is a bias ?

A Bias occurs when there is a systematic difference between the results from a study and the true state of affairs .Bias is often introduced when a study is being designed, but can be introduced at any stage . Bias is any trend or deviation from the truth in data collection, data analysis, interpretation and publication which can cause false conclusions. Bias can occur either intentionally or unintentionally . Bias is described as the intentional or unintentional influence that the researcher may have on a study. Therefore, a bias will prejudice the results of the research findings. In general, bias is a type of systematic error that is introduced into the sampling or testing and encourages one outcome over another.

Thus having understood the definition of bias, we can now understand as to what can be their impact on the study .

The bias impacts the study in many aspects .

1. Bias in data collection:- Sampling is a crucial step for every research. While collecting data for research, there are numerous ways by which researchers can introduce bias in the study. If, for example, during patient recruitment, some patients are less or more likely to enter the study than others, such sample would not be representative of the population in which this research is done. In that case, these subjects who are less likely to enter the study will be under-represented and those who are more likely to enter the study will be over-represented relative to others in the general population, to which conclusions of the study are to be applied to. This is what we call a selection bias.

Generally speaking, whenever cross-sectional or case control studies are done exclusively in hospital settings, there is a good chance that such study will be biased. This is called admission bias. Bias exists because the population studied does not reflect the general population.

2. Bias in data analysis

A researcher can introduce bias in data analysis by analyzing data in a way which gives preference to the conclusions in favor of research hypothesis. There are various opportunities by which bias can be introduced during data analysis, such as by fabricating, abusing or manipulating the data.

3). Bias in data interpretation

By interpreting the results, one needs to make sure that proper statistical tests were used, that results were presented correctly and that data are interpreted only if there was a statistical significance of the observed relationship . Otherwise, there may be some bias in a research.

4). Publication bias

Unfortunately, scientific journals are much more likely to accept for publication a study which reports some positive than a study with negative findings. Such behavior creates false impression in the literature and may cause long-term consequences to the entire scientific community. Also, if negative results would not have so many difficulties to get published, other scientists would not unnecessarily waste their time and financial resources by re-running the same experiments.

2). There exists bias in study in the realms of diagnostics in various ways.

A way to remove them is to first of all identify the sources of bias and then to take action plan to remove them.

Type of Bias	How to Avoid
		Pre-trial bias
Flawed study design	• Clearly define risk and outcome, preferably with objective or
	validated methods. Standardize and blind data collection.
Selection bias	• Select patients using rigorous criteria to avoid confounding
	results. Patients should originate from the same general
	population. Well designed, prospective studies help to avoid
	selection bias as outcome is unknown at time of enrollment.
Channeling bias	• Assign patients to study cohorts using rigorous criteria.
		Bias during trial
Interviewer bias	• Standardize interviewer's interaction with patient. Blind
	interviewer to exposure status.
Chronology bias	• Prospective studies can eliminate chronology bias. Avoid
	using historic controls (confounding by secular trends).
Recall bias	• Use objective data sources whenever possible. When using
	subjective data sources, corroborate with medical record.
	Conduct prospective studies because outcome is unknown at
	time of patient enrollment.
Transfer bias	• Carefully design plan for lost-to-followup patients prior to
	the study.
Exposure Misclassification	• Clearly define exposure prior to study. Avoid using proxies
	of exposure.
Outcome Misclassification	• Use objective diagnostic studies or validated measures as
	primary outcome.
Performance bias	• Consider cluster stratification to minimize variability in
	surgical technique.
		Bias after trial
Citation bias	• Register trial with an accepted clinical trials registry. Check
	registries for similar unpublished or in-progress trials prior to
	publication.
Confounding	• Known confounders can be controlled with study design
	(case control design or randomization) or during data analysis
	(regression). Unknown confounders can only be controlled
	with randomization.