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Infectious Disease Data from Surveillance, Outbreak Investigation, and Epidemiological Studies
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
Information bias refers to flaws in measuring exposure, covariate, or outcome variables that result in different accuracy of information between comparison groups [3]. Invalid information can lead to misclassification of the exposure status of individuals. When the study aim is to assess the effect of an exposure on an outcome, it is of key importance to assess whether this misclassification is differential (i.e., differing between cases and non-cases) or non-differential. Non-differential misclassification results in underestimating an effect, while differential misclassification can lead to over- or underestimating effects. In a vaccine effectiveness study, information bias can arise when cases of the infection of interest are more likely to remember that they were vaccinated than people who have not become ill. This would result in an underestimation of the protective effect of vaccination.
Epidemiology
Published in John C Watkinson, Raymond W Clarke, Louise Jayne Clark, Adam J Donne, R James A England, Hisham M Mehanna, Gerald William McGarry, Sean Carrie, Basic Sciences Endocrine Surgery Rhinology, 2018
Jan H.P. van der Meulen, David A. Lowe, Jonathan M. Fishman
Information bias results from incorrect information about the determinant or the outcome or both. The important question that has to be answered is: ‘Has information been gathered in the same way?’ In cohort studies (see ‘Cohort studies’ below), information about the outcome should be obtained in the same way for those with and without the determinants under consideration. Also, those who collect the information about the outcome should be unaware of (‘blind to’) the determinant status of the subjects as much as possible. In case-control studies (see below), information about the determinant status should be collected in the same way for cases and controls.
Bias and confounding
Published in Antony Stewart, Basic Statistics and Epidemiology, 2018
It is very difficult to avoid bias completely. However, it is possible to limit any problems by seeking out and eliminating potential biases as early as possible. The ideal time to do this is during the planning stages of a study. If detected at a later stage, biases can sometimes (but not always) be reduced by taking them into account during data analysis and interpretation. In particular, studies should be scrutinised to detect bias. Errors in data analysis can also produce bias, and should be similarly sought out and dealt with. The main types of bias are selection bias and information bias.
Dutch Prospective Observational Study on Prehospital Treatment of Severe Traumatic Brain Injury: The BRAIN-PROTECT Study Protocol
Published in Prehospital Emergency Care, 2019
Sebastiaan M. Bossers, Christa Boer, Sjoerd Greuters, Frank W. Bloemers, Dennis Den Hartog, Esther M. M. Van Lieshout, Nico Hoogerwerf, Gerard Innemee, Joukje van der Naalt, Anthony R. Absalom, Saskia M. Peerdeman, Matthijs de Visser, Stephan Loer, Patrick Schober
To minimize selection bias, all consecutive patients who comply with the inclusion criteria are included. Information bias is minimized by the prospective design and use of objective (e.g., blood pressure) and validated (e.g., AIS scores) data. However, in a dynamic prehospital setting where variables are repetitively observed or measured at varying intervals, standardization of measurement time points is difficult. In this study, vital parameters were routinely documented at three predetermined time points, with the possibility to add additional relevant vital parameters at other time points to document nadir values or vital parameters associated with specific events or interventions. However, measurement artifacts, oversight of brief events (e.g., brief hypotension after induction of anesthesia), deliberate nonreporting of complications, or documentation errors cannot be excluded and may bias the results.
Challenges in estimating influenza vaccine effectiveness
Published in Expert Review of Vaccines, 2019
Kylie E. C. Ainslie, Michael Haber, Walt A. Orenstein
Bias may be present in observational studies for the estimation of influenza VE because differences in the risk of outcomes (e.g., infection) may exist that are caused by factors other than vaccination status. Depending on the study design, this bias may be a result of differences in the selection of cases and controls, such as a) differing health-care seeking behavior between vaccinees and non-vaccinees or the effect of vaccination on the probability of developing non-influenza ARI; b) confounding, such as the effect of health status or frailty on both the probability of vaccination and the probability of influenza ARI; or c) misclassification of influenza virus infection and/or vaccination status. In the following section we discuss three types of bias: selection bias, confounding, and information bias. However, the sources of bias discussed below are not exhaustive and additional bias in influenza VE studies may result from sparse data, missing data, and volunteer bias, among others.
An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting
Published in Expert Opinion on Drug Safety, 2023
Salvatore Crisafulli, Zakir Khan, Yusuf Karatas, Marco Tuccori, Gianluca Trifirò
Information bias occurs when key study variables related to either exposure or outcome are inaccurately measured or classified (i.e. misclassification). Misclassification causing information bias can be differential, when the information errors differ between those who have the disease and those do have not, and non-differential, when the probability of individuals being misclassified is equal across all groups in the study [62]. The most common example of information bias is the recall bias, which arises when participants in case-control studies have different recall of past events or experiences [63].