Please confirm topic selection

Are you sure you want to trigger topic in your Anconeus AI algorithm?

Please confirm action

You are done for today with this topic.

Would you like to start learning session with this topic items scheduled for future?

Updated: Mar 1 2021


Images triangle.jpg
  • 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
1 of 0
1 of 16
Private Note

Attach Treatment Poll
Treatment poll is required to gain more useful feedback from members.
Please enter Question Text
Please enter at least 2 unique options
Please enter at least 2 unique options
Please enter at least 2 unique options