This bias is more likely in non-randomized trials when patient assignment to groups is performed by medical personnel.
Channeling bias is commonly seen in pharmaceutical trials comparing old and new drugs to one another In surgical studies, channeling bias can occur if one intervention carries a greater inherent risk For example, hand surgeons managing fractures may be more aggressive with operative intervention in young, healthy individuals with low perioperative risk.
Similarly, surgeons might tolerate imperfect reduction in the elderly, a group at higher risk for perioperative complications and with decreased need for perfect hand function. Thus, a selection bias exists for operative intervention in young patients. Now imagine a retrospective study of operative versus non-operative management of hand fractures.
In this study, young patients would be channeled into the operative study cohort and the elderly would be channeled into the nonoperative study cohort. Information bias is a blanket classification of error in which bias occurs in the measurement of an exposure or outcome. Thus, the information obtained and recorded from patients in different study groups is unequal in some way Many subtypes of information bias can occur, including interviewer bias, chronology bias, recall bias, patient loss to follow-up, bias from misclassification of patients, and performance bias.
Interviewer bias refers to a systematic difference between how information is solicited, recorded, or interpreted 18 , Interviewer bias is more likely when disease status is known to interviewer. An example of this would be a patient with Buerger's disease enrolled in a case control study which attempts to retrospectively identify risk factors. Interviewer bias can be minimized or eliminated if the interviewer is blinded to the outcome of interest or if the outcome of interest has not yet occurred, as in a prospective trial.
Chronology bias occurs when historic controls are used as a comparison group for patients undergoing an intervention. Secular trends within the medical system could affect how disease is diagnosed, how treatments are administered, or how preferred outcome measures are obtained Each of these differences could act as a source of inequality between the historic controls and intervention groups.
For example, many microsurgeons currently use preoperative imaging to guide perforator flap dissection. Imaging has been shown to significantly reduce operative time A retrospective study of flap dissection time might conclude that dissection time decreases as surgeon experience improves.
More likely, the use of preoperative imaging caused a notable reduction in dissection time. Thus, chronology bias is present. Chronology bias can be minimized by conducting prospective cohort or randomized control trials, or by using historic controls from only the very recent past.
Recall bias refers to the phenomenon in which the outcomes of treatment good or bad may color subjects' recollections of events prior to or during the treatment process.
One common example is the perceived association between autism and the MMR vaccine. This vaccine is given to children during a prominent period of language and social development. As a result, parents of children with autism are more likely to recall immunization administration during this developmental regression, and a causal relationship may be perceived Recall bias is most likely when exposure and disease status are both known at time of study, and can also be problematic when patient interviews or subjective assessments are used as a primary data sources.
In almost all clinical studies, subjects are lost to follow-up. In these instances, investigators must consider whether these patients are fundamentally different than those retained in the study. Researchers must also consider how to treat patients lost to follow-up in their analysis.
Well designed trials usually have protocols in place to attempt telephone or mail contact for patients who miss clinic appointments. Transfer bias can occur when study cohorts have unequal losses to follow-up.
This is particularly relevant in surgical trials when study cohorts are expected to require different follow-up regimens. Consider a study evaluating outcomes in inferior pedicle Wise pattern versus vertical scar breast reductions. Because the Wise pattern patients often have fewer contour problems in the immediate postoperative period, they may be less likely to return for long-term follow-up. By contrast, patient concerns over resolving skin redundancies in the vertical reduction group may make these individuals more likely to return for postoperative evaluations by their surgeons.
Some authors suggest that patient loss to follow-up can be minimized by offering convenient office hours, personalized patient contact via phone or email, and physician visits to the patient's home 20 , Misclassification of exposure can occur if the exposure itself is poorly defined or if proxies of exposure are utilized. For example, this might occur in a study evaluating efficacy of becaplermin Regranex, Systagenix Wound Management versus saline dressings for management of diabetic foot ulcers.
Significantly different results might be obtained if the becaplermin cohort of patients included those prescribed the medication, rather than patients directly observed to be applying the medication. Similarly, misclassification of outcome can occur if non-objective measures are used.
For example, clinical signs and symptoms are notoriously unreliable indicators of venous thromboembolism. Thus, using Homan's sign calf pain elicited by extreme dorsi-flexion or pleuritic chest pain as study measures for deep venous thrombosis or pulmonary embolus would be inappropriate.
Venous thromboembolism is appropriately diagnosed using objective tests with high sensitivity and specificity, such as duplex ultrasound or spiral CT scan 26 - In surgical trials, performance bias may complicate efforts to establish a cause-effect relationship between procedures and outcomes. As plastic surgeons, we are all aware that surgery is rarely standardized and that technical variability occurs between surgeons and among a single surgeon's cases.
Variations by surgeon commonly occur in surgical plan, flow of operation, and technical maneuvers used to achieve the desired result. The surgeon's experience may have a significant effect on the outcome. To minimize or avoid performance bias, investigators can consider cluster stratification of patients, in which all patients having an operation by one surgeon or at one hospital are placed into the same study group, as opposed to placing individual patients into groups.
This will minimize performance variability within groups and decrease performance bias. Cluster stratification of patients may allow surgeons to perform only the surgery with which they are most comfortable or experienced, providing a more valid assessment of the procedures being evaluated.
If the operation in question has a steep learning curve, cluster stratification may make generalization of study results to the everyday plastic surgeon difficult. Bias after a trial's conclusion can occur during data analysis or publication.
In this section, we will discuss citation bias, evaluate the role of confounding in data analysis, and provide a brief discussion of internal and external validity. Citation bias refers to the fact that researchers and trial sponsors may be unwilling to publish unfavorable results, believing that such findings may negatively reflect on their personal abilities or on the efficacy of their product.
Thus, positive results are more likely to be submitted for publication than negative results. Additionally, existing inequalities in the medical literature may sway clinicians' opinions of the expected trial results before or during a trial. In recognition of citation bias, the International Committee of Medical Journal Editors ICMJE released a consensus statement in 29 which required all randomized control trials to be pre-registered with an approved clinical trials registry.
In , a second consensus statement 30 required that all prospective trials not deemed purely observational be registered with a central clinical trials registry prior to patient enrollment.
ICMJE member journals will not publish studies which are not registered in advance with one of five accepted registries. Despite these measures, citation bias has not been completely eliminated. While centralized documentation provides medical researchers with information about unpublished trials, investigators may be left to only speculate as to the results of these studies.
Confounding occurs when an observed association is due to three factors: Examples of confounders include observed associations between coffee drinking and heart attack confounded by smoking and the association between income and health status confounded by access to care. Pre-trial study design is the preferred method to control for confounding.
Prior to the study, matching patients for demographics such as age or gender and risk factors such as body mass index or smoking can create similar cohorts among identified confounders.
However, the effect of unmeasured or unknown confounders may only be controlled by true randomization in a study with a large sample size. After a study's conclusion, identified confounders can be controlled by analyzing for an association between exposure and outcome only in cohorts similar for the identified confounding factor.
For example, in a study comparing outcomes for various breast reconstruction options, the results might be confounded by the timing of the reconstruction i.
In other words, procedure type and timing may have both have significant and independent effects on breast reconstruction outcomes.
One approach to this confounding would be to compare outcomes by procedure type separately for immediate and delayed reconstruction patients. Stratified analyses are limited if multiple confounders are present or if sample size is small. Multi-variable regression analysis can also be used to control for identified confounders during data analysis.
The role of unidentified confounders cannot be controlled using statistical analysis. Internal validity refers to the reliability or accuracy of the study results. A study's internal validity reflects the author's and reviewer's confidence that study design, implementation, and data analysis have minimized or eliminated bias and that the findings are representative of the true association between exposure and outcome. When evaluating studies, careful review of study methodology for sources of bias discussed above enables the reader to evaluate internal validity.
Studies with high internal validity are often explanatory trials, those designed to test efficacy of a specific intervention under idealized conditions in a highly selected population.
However, high internal validity often comes at the expense of ability to be generalized. For example, although supra-microsurgery techniques, defined as anastamosis of vessels less than 0. External validity of research design deals with the degree to which findings are able to be generalized to other groups or populations. In contrast with explanatory trials, pragmatic trials are designed to assess the benefits of interventions under real clinical conditions.
These studies usually include study populations generated using minimal exclusion criteria, making them very similar to the general population. While pragmatic trials have high external validity, loose inclusion criteria may compromise the study's internal validity.
When reviewing scientific literature, readers should assess whether the research methods preclude generalization of the study's findings to other patient populations. In making this decision, readers must consider differences between the source population population from which the study population originated and the study population those included in the study. Additionally, it is important to distinguish limited ability to be generalized due to a selective patient population from true bias 8.
When designing trials, achieving balance between internal and external validity is difficult. An ideal trial design would randomize patients and blind those collecting and analyzing data high internal validity , while keeping exclusion criteria to a minimum, thus making study and source populations closely related and allowing generalization of results high external validity For those evaluating the literature, objective models exist to quantify both external and internal validity.
Conceptual models to assess a study's ability to be generalized have been developed Additionally, qualitative checklists can be used to assess the external validity of clinical trials. These can be utilized by investigators to improve study design and also by those reading published studies Such high-level studies can be evaluated using the Jadad scoring system, an established, rigorous means of assessing the methodological quality and internal validity of clinical trials Like all studies, RCT's must be rigorously evaluated.
Descriptions of study methods should include details on the randomization process, method s of blinding, treatment of incomplete outcome data, funding source s , and include data on statistically insignificant outcomes Authors who provide incomplete trial information can create additional bias after a trial ends; readers are not able to evaluate the trial's internal and external validity As a result, readers can make independent judgments on the trial's internal and external validity.
None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript.
The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. National Center for Biotechnology Information , U. Author manuscript; available in PMC Aug 1. Pannucci , MD and Edwin G. Wilkins , MD MS. University of Michigan Ann Arbor, Michigan. The publisher's final edited version of this article is available at Plast Reconstr Surg.
See other articles in PMC that cite the published article. Abstract This narrative review provides an overview on the topic of bias as part of Plastic and Reconstructive Surgery 's series of articles on evidence-based medicine.
Definition and scope of bias Bias is defined as any tendency which prevents unprejudiced consideration of a question 6. Open in a separate window. Table 1 Tips to avoid different types of bias during a trial. Standardize and blind data collection.
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. Questions may raise sensitive subjects, about which respondents would rather not talk. Respondents may give false answers to hide secrets. You need to build trust here. People will talk to others they like and trust. Use projective techniques and indirect questions in qualitative marketing research.
People say what is socially acceptable, even though they may feel or think something else. They may twist the truth, or offer half-truths. For example, not many respondents tell you directly that they seek power, social status, or are envious because of their insecurities. Use projective techniques or indirect questions that deal with socially sensitive subjects. When respondents know who is sponsoring the research, their feelings and opinions about the sponsor may bias answers.
Keep your studies blind as long as you can in qualitative research. You interview the wrong people. Poor screening and recruiting causes biased samples.
Random sampling during recruiting reduces sample bias. Professional respondents also cause sample bias. They typically show up in consumer focus groups. Their goal is to earn a part-time salary from focus group and survey incentives. Ask your focus group recruiter to guarantee they are not recruiting professional respondents. Listen for answers, or a lack of answers. Develop a sixth sense for professional respondents in consumer research.
They bias the sample and waste time and money. Strive to keep your sample is bias free. Screen in the respondents you want. You want a sample that represents your target segment, in qualitative marketing research. Moderators and analysts sometimes produce bias when reporting the results of qualitative research.
Experiences, beliefs, feelings, wishes, attitudes, culture, views, state of mind, reference, error, and personality can bias analysis and reporting. More than one analyst helps. Get a couple of people to analyze the data. If you subconsciously skew reporting, another analyst may spot it. Also see Instant Focus Group Questions , which is a page e-book packed with hundreds of focus group questions.
Learn more and download it today. Return to Moderating from qualitative research bias Return to Home Page. Bias slants and skews data in qualitative marketing research.
In qualitative marketing research, there are five major categories of bias: Moderator bias Biased questions Biased answers Biased samples Biased reporting Moderator Bias The moderator collects the data and has a major impact on the quality of the data. Here are some common biased questions found in qualitative research. Leading Questions Bias Leading questions suggest what answers should be.
Write and ask neutral questions. Misunderstood Question Bias Sometimes moderators ask questions respondents misunderstand. Simple, clear, and concrete questions reduce misunderstanding. Question Order Bias Question order can bias. Minimize question order bias in qualitative research: Ask general questions before specific questions unaided before aided questions positive questions before negative questions behavior questions before attitude questions Ordering your topics, questions and activities needs some judgment.
Biased Answers A biased answer is an untrue or partially true statement. Biased answers are common; be on guard for them. Here are common types of biased answers seen in qualitative research. Consistency Bias Respondents try to appear consistent in their answers. If an answer does not seem right, ask for clarification. Dominant Respondent Bias In a focus group, dominant respondents appear occasionally.
Keep dominant respondents in check. Make sure other respondents get equal talk time. Error Bias Respondents are not always right. Sometimes they make mistakes. Memories fade and people forget.
Hostility Bias Some respondents may be angry with the moderator or sponsor, and provide negative responses. Continue to ask questions. If hostility persists, break off the interview. Moderator Acceptance Bias Some respondents provide answers to please the moderator. Mood Bias When respondents are in an extreme mood state, they may provide answers that reflect their mood.
Check for mood state and assess answers. Overstatement Bias Sometimes respondents overstate their intentions or opinions.
This narrative review provides an overview on the topic of bias as part of Plastic and Reconstructive Surgery's series of articles on evidence-based medicine. Bias can occur in the planning, data collection, analysis, and publication phases of research. Understanding research bias allows readers to.
Research bias, also called experimenter bias, is a process where the scientists performing the research influence the results, in order to portray a certain outcome.
When we focus on the human elements of the research process and look at the nine core types of bias – driven from the respondent, the researcher or both – we are able to minimize the potential impact that bias has on qualitative market research. Bias in qualitative research affects the validity and reliability of findings, and consequently affects decisions. Know the five major categories of bias in qualitative research.
Bias in research, where prejudice or selectivity introduces a deviation in outcome beyond chance, is a growing problem, probably amplified by: the competitive aspects of the profession with difficulties in obtaining funding;. Bias is the systematic distortion of the estimated intervention effect away from the “truth”, caused by inadequacies in the design, conduct, or analysis of a trial cannot be reduced by sample size (which reduces the effects of chance/ random variation and improves the precision, but not the.