Review Topic
  • 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 
  • 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 are known 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
  • 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 

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