What is loss to follow up bias?
A loss-to-follow-up bias occurs in prospective cohort studies. With this type of bias, the true relationship between exposure and disease will only be distorted if the losses during follow-up are selective (non-random) with respect to both exposure and outcome. Click to see full answer.
What is loss of participants to follow-up?
Loss to follow-up is a particular problem associated with cohort studies. Bias may be introduced if the individuals lost to follow-up differ with respect to the exposure and outcome from those persons who remain in the study. The differential loss of participants from groups of a randomised control trial is known as attrition bias.
What is loss-to-follow-up bias?
A loss-to-follow-up bias occurs in prospective cohort studies. With this type of bias, the true relationship between exposure and disease will only be distorted if the losses during follow-up are selective (non-random) with respect to both exposure and outcome.
Why is loss to follow-up important in determining validity?
1 Loss to follow-up is very important in determining a study's validity because patients lost to follow-up often have... 2 Properly calculating the loss to follow-up can only be done by determining the right denominator. 3 A good rule of thumb is that <5% loss leads to little bias, while >20% poses serious threats to validity.
What kind of bias is loss to follow-up?
Selection bias due to loss to follow up is the absolute or relative bias that arises from how participants are selected out of a given risk set 3.
What is information bias example?
An example of information bias is believing that the more information that can be acquired to make a decision, the better, even if that extra information is irrelevant for the decision.
How do you reduce loss to follow-up bias?
The only way to prevent bias from loss to follow-up is to maintain high follow up rates (>80%). This can be achieved by: Enrolling motivated subjects. Using subjects who are easy to track.
Does loss to follow-up affect internal validity?
If follow-up is incomplete or interrupted, leading to missing data at the end of the study, this could impact the internal validity of the study. Participants with missing data, compared with those with complete data, may differ systematically, for example when loss to follow-up is related to the death of participants.
What is information bias?
Information bias is a distortion in the measure of association caused by a lack of accurate measurements of key study variables. Information bias, also called measurement bias, arises when key study variables (exposure, health outcome, or confounders) are inaccurately measured or classified.
How do you identify information bias?
If you notice the following, the source may be biased: Heavily opinionated or one-sided. Relies on unsupported or unsubstantiated claims. Presents highly selected facts that lean to a certain outcome.
Why is loss to follow-up a problem?
Loss to follow up is a problem for two main reasons: It reduces the effective sample size because the investigators will be missing outcome measures on those who are lost. If follow up rates differ among comparison groups and if attrition is related to the outcome, the results of the study can be biased.
How do you define loss to follow-up?
In the clinical research trial industry, loss to follow-up refers to patients who at one point in time were actively participating in a clinical research trial, but have become lost (either by error in a computer tracking system or by being unreachable) at the point of follow-up in the trial.
What causes loss to follow-up?
Common reasons for loss to follow-up were social or structural. These included problems with transportation, finances, and work/child care responsibilities. Among those lost to follow-up, subsequent outcomes were heterogeneous.
Does bias affect internal or external validity?
Bias can affect both the internal validity and the external validity of a study.
Is there loss to follow-up in case control studies?
Attrition Bias (Loss to follow-up) If too many subjects are loss to follow-up, the internal validity of the study is reduced. A general rule of thumb requires that the loss to follow-up rate not exceed 20% of the sample.
What does attrition bias mean?
Attrition bias is a type of selection bias due to systematic differences between study groups in the number and the way participants are lost from a study.
What is information bias?
Information bias is any systematic difference from the truth that arises in the collection, recall, recording and handling of information in a study, including how missing data is dealt with. Major types of information bias are misclassification bias, observer bias, recall bias and reporting bias. It is a probable bias within observational studies, ...
Why is it important to minimise information bias?
An important element to minimise information bias is to ensure that blinding of intervention status (or exposure status in observational studies) is maintained whilst outcomes are measured and recorded. If this is not possible, then the participants and investigators should be blind to the main hypotheses of the research.
What is missing data?
Missing data can be a major cause of information bias, where certain groups of people are more likely to have missing data. An example where differential recording may occur is in smoking data within medical records.
Should participants and investigators be blind to the main hypotheses of the research?
If this is not possible, then the participants and investigators should be blind to the main hypotheses of the research. Where possible, the information should be collected prospectively, using standardised methods and devices. Where interviewers collect data, questions should be posed neutrally.
Is observational research at risk?
Observational studies may be at greater risk, particularly those relying on self-reports and retrospective data collection. Although randomisation in intervention studies reduces the risk of bias and confounding, it can not entirely eradicate these ( Shahar & Shahar 2009).
What is nondifferential misclassification?
Records may be incomplete, e.g., a medical record in which none of the healthcare workers remember to ask about tobacco use. There may be errors in recording or interpreting information in records, or there may be errors in assigning codes to disease diagnoses by clerical workers who are unfamiliar with a patient's hospital course, diagnosis, and treatment. Subjects completing questionnaires or being interviewed may have difficulty in remembering past exposures. Note that if difficulty in remembering past exposures occurs to the same extent in both groups being compared, then there is nondifferential misclassification, which will bias toward the null. However, if one outcome group in a case-control study remembers better than the other, then there is a differential misclassification which is called "recall bias." Recall bias is described below under differential misclassification of exposure.
What is the health 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.
How does misclassification of outcomes affect a study?
Misclassification of outcomes can also introduce bias into a study, but it usually has much less of an impact than misclassification of exposure. First, most of the problems with misclassification occur with respect to exposure status since exposures are frequently more difficult to assess and categorize. We glibly talk about smokers and non-smokers, but what do these terms really mean? One needs to consider how heavily the individual smoked, the duration, how long ago they started, whether and when they stopped, and even whether they inhaled or whether they were exposed to environmental smoke. In addition, as illustrated above, there are a number of mechanisms by which misclassification of exposure can be introduced. In contrast, most outcomes are more definitive and there are few mechanisms that introduce errors in outcome classification.
What is selection bias?
However, if sampling is not representative of the exposure-outcome distributions in the overall population, then the measures of association will be biased, and this is referred to as selection bias. Consequently, selection bias can result when the selection of subjects into a study or their likelihood of being retained in a cohort study leads ...
What factors affect enrollment of subjects into a prospective cohort study?
Factors affecting enrollment of subjects into a prospective cohort study would not be expected to introduce selection bias. In order for bias to occur, selection has to be related to both exposure and outcome. Subjects are enrolled in prospective cohort studies before they have experienced the outcome of interest. Therefore, while it is easy to see how enrollment might be related to exposure (exposed might be more or less likely to enroll), it is difficult to imagine how either investigators or enrollees could be influenced by awareness of an outcome that hasn't yet occurred.
What is selection bias in case control?
In a case-control study selection bias occurs when subjects for the "control" group are not truly representative of the population that produced the cases. Remember that in a case-control study the controls are used to estimate the exposure distribution (i.e., the proportion having the exposure) in the population from which the cases arose. The exposure distribution in cases is then compared to the exposure distribution in the controls in order to compute the odds ratio as a measure of association.
What is the "would" criterion?
Epidemiologists sometimes use the " would" criterion " to test for the possibility of selection bias; they ask "If a control had had the disease, would they have been likely to be enrolled as a case?" If the answer is 'yes', then selection bias is unlikel
What is information bias?
1. Information bias. Information bias results from systematic differences in the way data on exposure or outcome are obtained from the various study groups. 1 This may mean that individuals are assigned to the wrong outcome category, leading to an incorrect estimate of the association between exposure and outcome.
How to minimize recall bias?
Methods to minimise recall bias include: Collecting exposure data from work or medical records. Blinding participants to the study hypothesis.
What is bias in epidemiology?
Bias may be defined as any systematic error in an epidemiological study that results in an incorrect estimate of the true effect of an exposure on the outcome of interest. 1. Bias results from systematic errors in the research methodology. The effect of bias will be an estimate either above or below the true value, ...
How many types of bias are there in epidemiology?
Common types of bias in epidemiological studies. More than 50 types of bias have been identified in epidemiological studies, but for simplicity they can be broadly grouped into two categories: information bias and selection bias. 1. Information bias.
What is confounding in science?
Confounding provides an alternative explanation for an association between an exposure (X) and an outcome. It occurs when an observed association is in fact distorted because the exposure is also correlated with another risk factor (Y). This risk factor Y is also associated with the outcome, but independently of the exposure under investigation, X. As a consequence, the estimated association is not that same as the true effect of exposure X on the outcome.
Why is there a risk of bias in occupational cohort studies?
There is a risk of bias here because individuals who are employed generally have to be healthy in order to work.
What is recall bias?
Recall bias may occur when the information provided on exposure differs between the cases and controls. For example an individual with the outcome under investigation (case) may report their exposure experience differently than an individual without the outcome (control) under investigation.
Why do studies use doctors?
Studies sometimes use doctors or nurses or other professionals because they are more likely to remain interested in the study, and because they belong to professional organizations that make it easer to track them down if they relocate.
Can a prospective cohort study be biased?
Ordinarily, some of the individuals invited to be subjects in a prospective cohort study refuse to participate. This can produce bias in retrospective cohort studies and case-control studies, because exposure status and outcomes have already occurred at the time of enrollment. However, non-participations will not bias a prospective cohort study in ...
Can selection bias occur in cohort studies?
Selection bias from enrollment procedures rarely occurs in cohort studies, because the outcomes have not yet occurred at the time when subjects are enrolled, so a potential participant's eventual outcome status is unknown and therefore can not influence . However, selection bias can occur in a prospective cohort study as a result ...
Does a study with substantial loss to follow have to produce a biased estimate of an association?
A study with substantial loss to follow does not have to produce a biased estimate of an association, but it certaintly raises concerns about the accuracy of the estimate. If the losses among the groups being compared are non-differntial, then the estimate will not be biased by the losses.
How to prevent losses in follow up?
Techniques for preventing losses follow-up include ensuring good communication between study staff and participants, accessibility to clinics, effective communication channels, incentives to continue, and ensuring that the study is of relevance to the participants.
What is the rule of thumb for analyzing data only from participants remaining in the study?
Analysing data only from participants remaining in the study is called complete case analysis. A rule of thumb states that <5% attrition leads to little bias, while >20% poses serious threats to validity. While this is useful, it is important to note that even small proportions of patients lost to follow-up can cause significant bias.
Why do people leave a study?
Regardless of the mechanisms used to obtain estimates of outcome data, the reasons that participants leave the study should be carefully considered: if people leave for reasons unrelated to the exposure (treatment) or the outcome this may have little or no impact on the results.
Is it harder to adhere to diet regimens in depression?
For instance, in an intervention study of diet in people with depression, those with more severe depression might find it harder to adhere to the diet regimen and therefore more likely to leave the study.
Does loss to follow up change the characteristics of the groups?
It almost always happens to some extent. Different rates of loss to follow-up in the exposure groups, or losses of different types of participants, whether at similar or different frequencies, may change the characteristics of the groups, irrespective of the exposure or intervention. Losses may be influenced by such factors as unsatisfactory ...
Can you follow up on an attrition study?
However, for many studies, complete follow up is unlikely. In such cases, the reasons for attrition should be carefully considered. After the study has been completed, a number of analysis methods can be used to reduce the impact of attrition bias.
