Importance To The Study Sample In Research.

In this forum, you will use you will use skillsets you have developed in this module related to the sampling process by responding to the following discussion prompt and answering all (4) questions. You may want to use the internet in addition to resources provided in Module 7. 

A researcher based in Miami wants to learn about the experiences of homeless people in the United States by conducting interviews with them. He plans to conduct in-person interviews with the homeless people he samples. He is having trouble deciding how to draw a representative sample of homeless people because homeless people are difficult to find, often move from place to place, and are often cycled in and out of homelessness. The researcher has one year and a $5,000 grant with which to conduct his interviews.

Your task is to help this researcher.

1. What is the population he wants to generalize to?
2. What are possible sampling frames (sources) from which he can draw his sample?
3. What do you think would be a good/realistic sample size?
4. What sampling methods would you suggest he uses? Why?

Resources.

Use this website 

 4 Types of Bias in Research and How to Avoid Them (url) 

By the end of class, the student will:
explain the relationship between study population and study sample (CSLO 1, 4);
demonstrate assessment of inclusion and exclusion criteria (CSLO 1); 
identify the best method for determining adequate sample size (CSLO 1, 4);
differentiate between probability and non-probability sampling methods (CSLO 4); and

explain the sampling section of a research report (CSLO 1, 4).

Biochemia Medica 2013;23(1):12–5 http://dx.doi.org/10.11613/BM.2013.003

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Abstract

By writing scientifi c articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any de- viation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research. Key words: bias; sampling errors; research design

Received: 10 December, 2012 Accepted: 10 January, 2013

Bias in research

Ana-Maria Šimundić

University Department of Chemistry, University Hospital Center “Sestre Milosrdnice”, Zagreb, Croatia

*Corresponding author: am.simundic@gmail.com

Lessons in biostatistics

Introduction

Scientifi c papers are tools for communicating sci- ence between colleagues and peers. Every re- search needs to be designed, conducted and re- ported in a transparent way, honestly and without any deviation from the truth. Research which is not compliant with those basic principles is mislead- ing. Such studies create distorted impressions and false conclusions and thus can cause wrong medi- cal decisions, harm to the patient as well as sub- stantial fi nancial losses. This article provides the in- sight into the ways of recognizing sources of bias and avoiding bias in research.

Defi nition of bias

Bias is any trend or deviation from the truth in data collection, data analysis, interpretation and publi- cation which can cause false conclusions. Bias can occur either intentionally or unintentionally (1). In- tention to introduce bias into someone’s research is immoral. Nevertheless, considering the possible consequences of a biased research, it is almost

equally irresponsible to conduct and publish a bi- ased research unintentionally.

It is worth pointing out that every study has its confounding variables and limitations. Confound- ing eff ect cannot be completely avoided. Every scientist should therefore be aware of all potential sources of bias and undertake all possible actions to reduce and minimize the deviation from the truth. If deviation is still present, authors should confess it in their articles by declaring the known limitations of their work.

It is also the responsibility of editors and reviewers to detect any potential bias. If such bias exists, it is up to the editor to decide whether the bias has an important eff ect on the study conclusions. If that is the case, such articles need to be rejected for publication, because its conclusions are not valid.

Bias in data collection

Population consists of all individuals with a charac- teristic of interest. Since, studying a population is

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Simundic AM. Bias in research

quite often impossible due to the limited time and money; we usually study a phenomenon of inter- est in a representative sample. By doing this, we hope that what we have learned from a sample can be generalized to the entire population (2). To be able to do so, a sample needs to be representa- tive of the population. If this is not the case, con- clusions will not be generalizable, i.e. the study will not have the external validity.

So, sampling is a crucial step for every research. While collecting data for research, there are nu- merous ways by which researchers can introduce bias in the study. If, for example, during patient re- cruitment, 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 en- ter the study will be over-represented relative to others in the general population, to which conclu- sions of the study are to be applied to. This is what we call a selection bias. To ensure that a sample is representative of a population, sampling should be random, i.e. every subject needs to have equal probability to be included in the study. It should be noted that sampling bias can also occur if sample is too small to represent the target population (3).

For example, if the aim of the study is to assess the average hsCRP (high sensitive C-reactive protein) concentration in healthy population in Croatia, the way to go would be to recruit healthy individuals from a general population during their regular an- nual health check up. On the other hand, a biased study would be one which recruits only volunteer blood donors because healthy blood donors are usually individuals who feel themselves healthy and who are not suff ering from any condition or illness which might cause changes in hsCRP con- centration. By recruiting only healthy blood do- nors we might conclude that hsCRP is much lower that it really is. This is a kind of sampling bias, which we call a volunteer bias.

Another example for volunteer bias occurs by in- viting colleagues from a laboratory or clinical de- partment to participate in the study on some new

marker for anemia. It is very likely that such study would preferentially include those participants who might suspect to be anemic and are curious to learn it from this new test. This way, anemic in- dividuals might be over-represented. A research would then be biased and it would not allow gen- eralization of conclusions to the rest of the popu- lation.

Generally speaking, whenever cross-sectional or case control studies are done exclusively in hospi- tal 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 re- fl ect the general population.

Another example of sampling bias is the so called survivor bias which usually occurs in cross-section- al studies. If a study is aimed to assess the associa- tion of altered KLK6 (human Kallikrein-6) expres- sion with a 10 year incidence of Alzheimer’s dis- ease, subjects who died before the study end point might be missed from the study.

Misclassifi cation bias is a kind of sampling bias which occurs when a disease of interest is poorly defi ned, when there is no gold standard for diag- nosis of the disease or when a disease might not be easy detectable. This way some subjects are falsely classifi ed as cases or controls whereas they should have been in another group. Let us say that a researcher wants to study the accuracy of a new test for an early detection of the prostate cancer in asymptomatic men. Due to absence of a reliable test for the early prostate cancer detection, there is a chance that some early prostate cancer cases would go misclassifi ed as disease-free causing the under- or over-estimation of the accuracy of this new marker.

As a general rule, a research question needs to be considered with much attention and all eff orts should be made to ensure that a sample is as close- ly matched to the population, as possible.

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.

Biochemia Medica 2013;23(1):12–5 http://dx.doi.org/10.11613/BM.2013.003

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Simundic AM. Bias in research

There are various opportunities by which bias can be introduced during data analysis, such as by fab- ricating, abusing or manipulating the data. Some examples are:

reporting non-existing data from experiments • which were never done (data fabrication); eliminating data which do not support your hy-• pothesis (outliers, or even whole subgroups); using inappropriate statistical tests to test your • data; performing multiple testing (“fi shing for P”) by • pair-wise comparisons (4), testing multiple end- points and performing secondary or subgroup analyses, which were not part of the original plan in order “to fi nd” statistically signifi cant diff erence regardless to hypothesis.

For example, if the study aim is to show that one biomarker is associated with another in a group of patients, and this association does not prove sig- nifi cant in a total cohort, researchers may start “torturing the data” by trying to divide their data into various subgroups until this association be- comes statistically signifi cant. If this sub-classifi ca- tion of a study population was not part of the orig- inal research hypothesis, such behavior is consid- ered data manipulation and is neither acceptable nor ethical. Such studies quite often provide mean- ingless conclusions such as:

CRP was statistically signifi cant in a subgroup of • women under 37 years with cholesterol con- centration > 6.2 mmol/L; lactate concentration was negatively associated • with albumin concentration in a subgroup of male patients with a body mass index in the lowest quartile and total leukocyte count be- low 4.00 x 109/L.

Besides being biased, invalid and illogical, those conclusions are also useless, since they cannot be generalized to the entire population.

There is a very often quoted saying (attributed to Ronald Coase, but unpublished to the best of my knowledge), which says: “If you torture the data long enough, it will confess to anything”. This ac- tually means that there is a good chance that sta- tistical signifi cance will be reached only by increas-

ing the number of hypotheses tested in the work. The question is then: is this signifi cant diff erence real or did it occur by pure chance?

Actually, it is well known that if 20 tests are per- formed on the same data set, at least one Type 1 error (α) is to be expected. Therefore, the number of hypotheses to be tested in a certain study needs to determined in advance. If multiple hypotheses are tested, correction for multiple testing should be applied or study should be declared as explora- tory.

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 inter- preted only if there was a statistical signifi cance of the observed relationship (5). Otherwise, there may be some bias in a research.

However, wishful thinking is not rare in scientifi c research. Some researchers tend to believe so much in their original hypotheses that they tend to neglect the original fi ndings and interpret them in favor of their beliefs. Examples are:

discussing observed diff erences and associa-• tions even if they are not statistically signifi cant (the often used expression is “borderline signif- icance”); discussing diff erences which are statistically • signifi cant but are not clinically meaningful; drawing conclusions about the causality, even • if the study was not designed as an experiment; drawing conclusions about the values outside • the range of observed data (extrapolation); overgeneralization of the study conclusions to • the entire general population, even if a study was confi ned to the population subset; Type I (the expected eff ect is found signifi cant, • when actually there is none) and type II (the ex- pected eff ect is not found signifi cant, when it is actually present) errors (6).

Even if this is done as an honest error or due to the negligence, it is still considered a serious miscon- duct.

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Simundic AM. Bias in research

Publication bias

Unfortunately, scientifi c journals are much more likely to accept for publication a study which re- ports some positive than a study with negative fi ndings. Such behavior creates false impression in the literature and may cause long-term conse- quences to the entire scientifi c community. Also, if negative results would not have so many diffi cul- ties to get published, other scientists would not unnecessarily waste their time and fi nancial re- sources by re-running the same experiments.

Journal editors are the most responsible for this phenomenon. Ideally, a study should have equal opportunity to be published regardless of the na- ture of its fi ndings, if designed in a proper way, with valid scientifi c assumptions, well conducted experiments and adequate data analysis, presen- tation and conclusions. However, in reality, this is not the case. To enable publication of studies re- porting negative fi ndings, several journals have al- ready been launched, such as Journal of Pharma- ceutical Negative Results, Journal of Negative Re- sults in Biomedicine, Journal of Interesting Nega- tive Results and some other. The aim of such jour- nals is to counterbalance the ever-increasing pres- sure in the scientifi c literature to publish only posi- tive results.

It is our policy at Biochemia Medica to give equal consideration to submitted articles, regardless to the nature of its fi ndings.

One sort of publication bias is the so called fund- ing bias which occurs due to the prevailing number of studies funded by the same company, related to the same scientifi c question and supporting the interests of the sponsoring company. It is abso- lutely acceptable to receive funding from a com- pany to perform a research, as long as the study is run independently and not being infl uenced in any way by the sponsoring company and as long as the funding source is declared as a potential confl ict of interest to the journal editors, reviewers and readers.

It is the policy of our Journal to demand such dec- laration from the authors during submission and to publish this declaration in the published article (7). By this we believe that scientifi c community is given an opportunity to judge on the presence of any potential bias in the published work.

Conclusion

There are many potential sources of bias in re- search. Bias in research can cause distorted results and wrong conclusions. Such studies can lead to unnecessary costs, wrong clinical practice and they can eventually cause some kind of harm to the patient. It is therefore the responsibility of all involved stakeholders in the scientifi c publishing to ensure that only valid and unbiased research conducted in a highly professional and competent manner is published (8).

Potential confl ict of interest None declared.

References 1. Gardenier JS, Resnik DB. The misuse of statistics: concepts,

tools, and a research agenda. Account Res 2002;9:65-74. http://dx.doi.org/10.1080/08989620212968.

2. Hren D, Lukić KI. Types of studies, power of study and choi- ce of test. Acta Med Croatica 2006;60 Suppl 1:47-62.

3. Holmes TH. Ten categories of statistical errors: a guide for research in endocrinology and metabolism. Am J Physi- ol Endocrinol Metab 2004;286:495-501. http://dx.doi. org/10.1152/ajpendo.00484.2003.

4. Dawson-Saunders B, Trapp RG. Reading the medical lite- rature. In: Basic&Clinical biostatistics. New York – Toronto: Lange Medical Books/McGraw-Hill; 2004.

5. Simundic AM. Practical recommendations for statistical analysis and data presentation in Biochemia Medica jour- nal. Biochem Med 2012;22:15-23.

6. Ilakovac V. Statistical hypothesis testing and some pitfalls. Biochem Med 2009;19:10-6.

7. Simundic AM. Biochemia Medica introduces the revised policy on Statement of Confl ict of Interest. Biochem Med 2011;21:104-5.

8. Strasak AM. Statistical errors in medical research – a review of common pitfalls. Swiss Med Wkly 2007;137:44-9.

 

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