Updated: 6/9/2021

Bias

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Topic
  • Definition
    • A systematic error in collecting or interpreting observations found in the study design
  • Types of Bias
    • Types of Bias
      BiasDescriptionMitigation
      Accumulation Effect
      • patients sometimes must be exposed to a risk factor for aprolonged 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 arenot 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 assigment 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

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        Questions (3)

        (M2.OMB.18.1) A pharmaceutical company conducts a randomized clinical trial in an attempt to show that their new anticoagulant drug prevents more thrombotic events following total knee arthroplasty than the current standard of care. However, a significant number of patients are lost to follow-up or fail to complete treatment according to the study arm to which they were assigned. Several patients in the novel drug arm are also switched at a later time to a novel anticoagulant or warfarin per their primary care physician. All patients enrolled in the study are subsequently analyzed based on the initial group they were assigned to and there is a significant improvement in outcome of the new drug. What analysis most appropriately describes this trial?

        QID: 210435
        1

        As treated

        0%

        (0/15)

        2

        Intention to treat

        73%

        (11/15)

        3

        Modified intention to treat

        20%

        (3/15)

        4

        Non-inferiority

        7%

        (1/15)

        5

        Per protocol

        0%

        (0/15)

        M 5 C

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        (M2.OMB.18.1) Study X examined the relationship between coffee consumption and lung cancer. The authors of Study X retrospectively reviewed patients' reported coffee consumption and found that drinking greater than 6 cups of coffee per day was associated with an increased risk of developing lung cancer. The same study in lab mice; however, did not come to a similar conclusion. Which of the following most likely explains the difference between these 2 studies?

        QID: 210433
        1

        Confounding

        45%

        (5/11)

        2

        Lead time bias

        0%

        (0/11)

        3

        Measurement bias

        0%

        (0/11)

        4

        Observer bias

        0%

        (0/11)

        5

        Selection bias

        55%

        (6/11)

        M 5 C

        Select Answer to see Preferred Response

        Evidence (5)
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