Definition A systematic error in collecting or interpreting observations found in the study design Types of Bias Types of Bias Bias Description Mitigation Accumulation Effect patients sometimes must be exposed to a risk factor for a prolonged period of time before they develop a clinically detectable result e.g., patients must smoke for many pack-years before bronchogenic carcinoma develops try to follow study participants for as long as is feasible Confounding a third factor is either positively or negatively associated with both the exposure and outcome confounders are not in the causal pathway if not adjusted for can distort true association either towards or away from the null hypothesis randomization ensures similar baseline characteristics between control and exposure/experimental groups use intention-to-treat analysis to preserve randomization even if participants change study treatments matching group similar participants into study pairs stratification analyze in separate subgroups determined by a potential confounder restriction only include groups with specific features in the sample adjustment can only adjust for confounders that areknown and measureable crossover studies subject acts as own control Selection Bias sampled population is not representative of the population researchers are trying to study due to non-random selection of study participants sampling (ascertainment) bias certain individuals are more or less likely to be selected for a study group, leading to incorrect conclusions non-response bias e.g., participants who pick up the phone may be less sick than participants who don't healthy worker effect samples with employed subjects only may be healthier volunteer bias people who volunteer for a study may be different in some fundamental way from those who do not volunteer late-look bias patients with severe disease are less likely to be studied, because they die or are otherwise unavailable, making a disease look less severe e.g., a group of HIV+ individuals are all asymptomatic also can have opposite effect e.g.,people with more mild disease are cured before the study takes place and only persistently sick folks are included in the study, making a disease seem more severe Berkson bias hospitalized study subjects are more likely to have a greater burden of illness than other possible subjects attrition bias those lost to follow-up may be different from those who remain in the study randomization include patients in multiple settings (outpatient, hospitalized) study designs that are longitudinal in nature rather than cross-sectional gather maximal information on participants Measurement Bias information is gathered in a way that distorts the information or misclassifies study participants interviewer bias subjects in one group are interviewed in a different way than another differences due to interviewing style disrepencies are falsely attributed to group differences standardize data collection Recall Bias subjects with the disease are more likely to recall the exposure of interest e.g., parents of children with cancer recall exposure to a chemical reducing follow-up time in retrospective studies Performance Bias researchers treat groups differently or subjects alter their behavior/responses due to study group awareness Hawthorne effect subjects alter their behavior when they know they are being studied procedure bias researcher decides assigment of treatment versus control and assigns particular patients to one group or the other nonrandomly patient decides assignment of treatment versus control blinding Lead-Time Bias subjects appear to survive longer when in reality their disease was detected earlier common with improved screening e.g.,a cancer screening test is deemed to increase survival when in reality the disease was picked up earlier, increasing the time from detection to death use mortality rate instead of survival time in screening studies estimate lead time and add that to survival in unscreened group Design Bias the control group is inappropriately non-comparable to the intervention group allocation bias difference in the way participants are placed in control versus experimental groups e.g., all zebras in control group and all lions in exposure group randomization matching Cognitive Bias observer bias (pygmalion effect) investigator inadvertently conveys her high expectations to subjects, who then produce the expected result a "self-fulfilling prophecy" golem effect is the opposite: study subjects decrease their performance to meet low expectations of investigator confirmation bias researcher ignores results that do not support their hypothesis response bias participants do not respond accurately because they are concerned about the social desirability of their responses or misinterpret the question double blinding include positive and negative results Surveillance Bias outcomes are more likely to be detected in certain groups because of increased monitoring e.g.,a certain skin disease being detected more often in hypertensive patients because they have more physician visits than non-hypertensive patients researchers may falsely attribute hypertension to causing the skin disease match participants on similar likelihood of surveillance Examples of Effects that are Not Bias Effect modification Effect modification occurs when a third factor affects the magnitude of the relationship between the exposure and the disease e.g., the increased risk of cancer in smokers is even higher among those who also drink heavily. NOT a type of bias Latent period The negative effects of a disease may take years to become clinically apparent NOT a type of bias Generalizability the ability to use results from a study to draw conclusiosn about populations different than that used in the study this is most problematic for studies that evaluate only a very specific population