Docstoc

Bias

Document Sample
Bias Powered By Docstoc
					Spring 2008




               Bias, Confounding,

              and Effect Modification

                   STAT 6395



                                        Filardo and Ng
Bias


       Any systematic error in the design or conduct
       of a study that results in a mistaken estimate
       of the association between an exposure and
       a disease



       Bias is often a major problem in observational
       epidemiologic studies
Systematic error (bias) is different than random error



    • Example:        an association between an
       exposure an a disease in which the true
       relative risk is 2.0
Systematic error (bias) is different than random error



    • If the design and conduct of a study are
       unbiased, and there is no confounding, and
       we repeat the study an infinite number of
       times, the mean relative risk will be 2.0, with
       the individual relative risks from the different
       studies fluctuating around 2.0
Systematic error (bias) is different than random error



    • If the design or conduct of the study is biased,
       and we repeat the study an infinite number of
       times, the mean relative risk will differ from
       2.0 (for example, it may be 1.2), with the
       individual relative risks from the different
       studies fluctuating around 1.2
Systematic error (bias) is different than random error



    • Due to random variation, an association that
       is far from the truth can be observed in an
       unbiased study, but it usually won’t be.
Systematic error (bias) is different than random error



    • Due to random variation, the true association
       can be observed in a biased study, but it
       usually won’t be
Systematic error (bias) is different than random error


       Statistical significance     does    not   protect
       against bias
Two major categories of bias




         • Selection bias
         • Information bias
Selection bias


      Error that results from criteria or procedures
      used to select study subjects or from factors
      that influence study participation.


       With selection bias, the relation between exposure
       and disease is different for those who are selected for
       and participate in the study and those who should be
       theoretically eligible to participate.
Selection bias


    Selection bias can occur as a result of:
          Incorrect selection criteria for study subjects
          Differences in characteristics between eligible subjects who agree
           to participate and eligible subjects who do not participate
Information bias


      Error due to collection of incorrect information
      about study subjects. Due to this incorrect
      information, subjects are classified into
      incorrect exposure or disease categories.
Selection bias is a major issue in case-control studies


    • Source population: the population that gives
       rise to the cases
Selection bias is a major issue in case-control studies


    • Cases should be selected such that the
       distribution of the exposures of interest
       among the cases selected for the study is the
       same as it is among all cases that arise in the
       source population. The cases should be
       representative of all cases that arise in the
       source population with respect to the
       exposures of interest.
Selection bias in case-control studies (cont.)


    • Controls should be selected such that the
       distribution of the exposures of interest
       among the controls is the same as it is in the
       source population. The controls should be
       representative of the source population with
       respect to the exposures of interest.
Selection bias in case-control studies (cont.)


    • Selection bias occurs when either:
           The cases are not representative of all cases that arise in the
            source population with respect to the exposures of interest and/or
           The controls are not representative of the source population with
            respect to the exposures of interest.
Selection bias in case-control studies: how it works


    • In the hypothetical data depicted in the
       following tables, we will assume there is:
                 » no information bias,
                 » confounding, or
                 » random variability



       so that all differences are due to differences
    in selection of cases or controls
Hypothetical case-control study including all cases and all
non-cases from Source Population A



                       All Cases       All Non-cases

     Exposed               500             100,000
   Nonexposed             1,000            900,000
        Total             1,500           1,000,000


   Gold standard OR = 4.5
Hypothetical case-control study including a 70% unbiased
sample of the cases and 0.5% unbiased sample of the
controls from Source Population A


                        Cases              Controls

                      500 x 0.7 =      100,000 x 0.005 =
      Exposed
                         350                 500
                     1,000 x 0.7 =     900,000 x 0.005 =
     Nonexposed
                         700                4,500
        Total           1,050               5,000


  Unbiased OR = (350x4,500)/(500x700) = 4.5
  This is an unbiased odds ratio because the selection of
  cases and controls was unrelated to exposure.
Selection bias in choosing controls in a hypothetical case-
control study including a 70% sample of the cases and 0.5%
sample of the controls from Source Population A

                        Cases             Controls

                     500 x 0.7 =     100,000 x 0.0095 =
       Exposed
                        350                 950
                     1,000 x 0.7 =   900,000 x 0.0045 =
     Nonexposed
                         700               4,050
                     1,500 x 0.7 =   1,000,000 x 0.005 =
        Total
                        1,050               5,000


  Biased OR = (350x4,050)/(950x700) = 2.13
  Selection of controls was related to exposure
  -over selecting exposed controls biases OR downward
Selection bias in choosing controls in a case-control study
due to incorrect criteria for control selection

       Example: A hospital-based case-control study
       of the relation of smoking to a given disease.
Selection bias in choosing controls in a case-control study
due to incorrect criteria for control selection


       If the control group includes persons
       hospitalized for smoking-related diseases
       (e.g, cardiovascular disease)…



       …the control group would likely have a higher
       proportion of smokers than the source
       population, and the resultant odds ratio would
       be biased downward
Selection bias in choosing controls in a case-control study
due to a difference in participation rates between exposed
controls and nonexposed controls

    • Example: Case-control study of the relation between
      housing characteristics and lead poisoning among
      children 6 years of age or younger who are screened
      for blood lead levels at the Hill Health Center in New
      Haven
Selection bias in choosing controls in a case-control study
due to a difference in participation rates between exposed
controls and nonexposed controls

    • Cases: all children with a blood lead level of >10
      micrograms/dL

    • Controls: a systematic sample of children with a
      blood lead level of <10 micrograms/dL
Housing characteristics and lead poisoning (cont.)


    • Incentive for participation: the parents of the children
       were offered a free lead inspection of their homes

    • Participation rate among cases: 91% (parents were
       motivated by their child’s elevated blood lead level to
       have the inspection)
Housing characteristics and lead poisoning (cont.)


    • Participation rate among controls: 69% (parents did
       not have the same motivation to participate)



       The condition of the housing of the control parents
       who refused to participate was better than the
       condition of the housing of the control parents who
       did participate
Housing characteristics and lead poisoning (cont.)


    • The housing of the controls selected for the study
       was in poorer condition than the housing of the
       source population


       The odds ratio for the association between measures
       of dilapidated housing and childhood lead poisoning
       would be biased downward
Housing characteristics and lead poisoning (cont.)


    • Although the criteria for selecting controls were
       sound, the difference in participation rate between
       exposed controls and nonexposed controls resulted
       in a biased odds ratio
Selection bias in choosing cases in a hypothetical case-
control study including a 70% sample of the cases and 0.5%
sample of the non-cases from Source Population A

                       Cases             Controls

                     500 x 0.9 =    100,000 x 0.005 =
       Exposed
                        450               500
                    1,000 x 0.6 =   900,000 x 0.005 =
     Nonexposed
                        600              4,500
                    1,500 x 0.7 =   1,000,000 x 0.005 =
        Total
                       1,050               5,000


  Biased OR = (450x4,500)/(500x600) = 6.75

  Selection of cases was related to exposure
  -over-selecting exposed cases biases OR upward
Selection bias in choosing cases in a case-control study


    • Example: Population-based case-control study of
       pancreatic cancer cancer

    • Hypothesis: vitamin C protects against development of
       pancreatic cancer



       Vitamin C intake    assessed   by   food   frequency
       questionnaire
Selection bias in choosing cases in a case-control study


    • Median interval between diagnosis and interview: 9
       months

    • One-year case fatality rate of pancreatic cancer: 80%


    Many cases would die before being interviewed
Selection bias in choosing cases in a case-control study


       Suppose vitamin C intake improves survival from
       pancreatic cancer

    • Then vitamin C intake among cases selected for the
       study would be higher than vitamin C intake among all
       cases

    • Over-selection of exposed cases would bias OR
       upward
Compensating Selection Bias



   To avoid biased odds ratios, investigators often attempt
   to equalize selection bias between cases and controls
   by selecting cases and controls undergoing the same
   selection processes
Compensating bias in choosing cases and controls in a
hypothetical case-control study including a 70% sample of
the cases and 0.5% sample of the non-cases from Source
Population A

                        Cases             Controls

                      500 x 0.9 =    100,000 x 0.00714 =
      Exposed
                         450                714
                     1,000 x 0.6 =   900,000 x 0.004762 =
     Nonexposed
                         600                4,286
                     1,500 x 0.7 =   1,000,000 x 0.005 =
        Total
                        1,050               5,000


  Unbiased OR = (450x4,286)/(714x600) = 4.5

  Equal over-selection (1.5x) of exposed cases and controls
Hypothetical case-control study including a 70% unbiased
sample of the cases and 0.5% unbiased sample of the
controls from Source Population A


                          Cases            Controls

                        500 x 0.7 =    100,000 x 0.005 =
       Exposed
                           350               500
                       1,000 x 0.7 =   900,000 x 0.005 =
     Nonexposed
                           700              4,500
        Total              1,050            5,000



  Unbiased OR = (350x4,500)/(500x700) = 4.5

  This is the original table
Cases and controls undergoing the same selection
processes in a case-control study of breast cancer


    • Example: Cases and controls selected from among
      women attending a breast cancer screening program



      These women are likely to have high prevalence of
      known breast cancer risk factors, (family history of
      breast cancer, history of benign breast disease, late
      age at first birth)
Cases and controls undergoing the same selection
processes in a case-control study of breast cancer


    • Example: Cases and controls selected from among
      women attending a breast cancer screening program



      If cases from this population were compared to
      controls from the general population, an overestimate
      of the magnitude of some risk factors would probably
      occur
Cases and controls undergoing the same selection
processes in a case-control study of breast cancer

    • Selecting both cases and controls from the screening
      program should make the bias the same in both
      groups, leading to unbiased odds ratios



      This is another way of saying that controls should be
      selected from the source population that gave rise to
      the cases
Minimizing selection bias in case-control studies


    • In the study design stage, carefully consider the
       criteria for selection of cases and controls,
       particularly with respect to ensuring internal validity
Minimizing selection bias in case-control studies


    • Choose study procedures aimed at maximizing the
       participation rate of the subjects selected for the
       study
Selection bias in cohort studies using internal comparison
groups is unlikely


    • Selection bias would occur if participation were related
       to both exposure and the subsequent development of
       disease

    • Because study participants are selected before the
       development of disease, this is unlikely



       The exposed group and nonexposed comparison group
       were drawn from the same source population and went
       through the same selection process
Selection bias in cohort studies using internal comparison
groups is unlikely


    • The nurses who participated in the Nurses’ Health
       Study most likely differed from the nurses who did
       not, but since the same selection process was
       used to select the exposed group and the
       nonexposed internal comparison group, the
       relative risk estimates should be unbiased.
Cohort studies using external comparison groups are prone
to selection bias


 • Exposed cohort and nonexposed external comparison
   group are not selected from the same source population



   The exposed cohort may be selected such that it is at
   higher or lower risk for disease than the external
   comparison group for a reason other than the exposure
   of interest
Healthy worker effect


    • A selection bias in occupational cohort studies using
       a general population external comparison group



       Persons selected for employment are usually
       healthier than and have lower mortality rates than the
       general population, which includes the sick and
       disabled.
Healthy worker effect


    • A selection bias in occupational cohort studies using
       a general population external comparison group



       The healthy worker effect makes any excess disease
       or mortality associated with an occupational exposure
       more difficult to detect than it would have been if a
       valid comparison group had been used, biasing the
       estimates of relative risk downward
Losses to follow-up in cohort studies are analogous to
selection bias in case-control studies


    • When a subject in a cohort study is lost to follow-up,
       we do not know whether that subject developed the
       disease of interest during the remainder of the study’s
       follow-up period
Losses to follow-up in cohort studies are analogous to
selection bias in case-control studies


    • If the subjects lost to follow-up have a different
       incidence of the disease of interest than the subjects
       not lost to follow-up, the estimates of the incidence
       rate of the disease of interest in the cohort will be
       biased
Losses to follow-up in cohort studies are analogous to
selection bias in case-control studies


    • However, relative risk estimates will be unbiased if
       the bias on the incidence rate estimates is the same
       in the exposed and nonexposed groups.


       A biased relative risk estimate will occur only if losses
       to follow-up are related to both disease and exposure
    • The best defense against bias due to losses to
       follow-up is to make intense efforts to locate each
       cohort member, and thus minimize losses
Losses to follow-up in cohort studies are analogous to
selection bias in case-control studies



    • The best defense against bias due to losses to
       follow-up is to make intense efforts to locate each
       cohort member, and thus minimize losses
Hypothetical cohort study with 100% follow-up (to keep the
examples simple, we will not use the person-years method,
but will use 10-year cumulative incidence)

                           No                 Incidence
               Disease              Total
                         Disease             ( x10,000 x 10 yrs)



     Exposed      50      10,000    10,050       49.75


       Non-
                 100      90,000    90,100       11.10
     exposed


       Gold standard RR = 49.75/11.10 = 4.48
Hypothetical cohort study with 30% of the cohort lost to
follow-up: losses to follow-up independent of exposure and
disease
                            No                            Incidence
          Disease                           Total
                          Disease                        ( x10,000 x 10 yrs)



          50 x 0.7 =   10,000 x 0.7 =   10,050 x 0.7 =
Exposed                                                      49.75
              35           7,000            7,035

  Non-  100 x 0.7 =    90,000 x 0.7 =   90,100 x 0.7 =
                                                             11.10
exposed     70            63,000           63,070



       Unbiased RR = 49.75/11.10 = 4.48
Hypothetical cohort study with 40% of the exposed group and
20% of the nonexposed group lost to follow-up: losses to
follow-up related to exposure, but not disease
                            No                            Incidence
          Disease                           Total
                          Disease                        ( x10,000 x 10 yrs)



          50 x 0.6 =   10,000 x 0.6 =   10,050 x 0.6 =
Exposed                                                      49.75
              30           6,000            6,030

  Non-  100 x 0.8 =    90,000 x 0.8 =   90,100 x 0.8 =
                                                             11.10
exposed     80            72,000           72,080



      Unbiased RR = 49.75/11.10 = 4.48
Hypothetical cohort study with 40% of those who developed
disease and 20% of those who did not develop disease lost to
follow-up: losses to follow-up related to disease, but not
exposure
                            No                    Incidence
          Disease                       Total
                          Disease                ( x10,000 x 10 yrs)



          50 x 0.6 =   10,000 x 0.8 =
Exposed                                 8,030        37.36
              30           8,000

  Non-  100 x 0.6 =    90,000 x 0.8 =
                                        72,060        8.33
exposed     60            72,000


       Unbiased RR = 37.36/8.33 = 4.48
Hypothetical cohort study: losses to follow-up related to
disease and exposure


                            No                    Incidence
          Disease                       Total
                          Disease                ( x10,000 x 10 yrs)



          50 x 0.6 =   10,000 x 0.8 =
Exposed                                 8,030        37.36
              30           8,000

  Non-  100 x 0.8 =    90,000 x 0.8 =
                                        72,080       11.10
exposed     80            72,000


       Biased RR = 37.36/11.10 = 3.37
Information bias (error due to collection of incorrect
information about study subjects) results in misclassification
of exposure or disease

     • Nondifferential exposure misclassification:
        misclassification of exposure unrelated to disease


     • Nondifferential disease misclassification:
        misclassification of disease unrelated to exposure


     • Differential misclassification: misclassification related
        to both exposure and disease
Information bias (error due to collection of incorrect
information about study subjects) results in misclassification
of exposure or disease


     • Nondifferential misclassification tends to bias an
        association toward     the   null   hypothesis   (no
        association)



     • Differential misclassification can bias an association
        either toward or away from the null hypothesis,
        depending on the specific nature of the
        misclassification
Nondifferential exposure misclassification in a cohort study


    • Inclusion of nonexposed subjects in the exposed
       group and exposed subjects in the nonexposed
       group will bias the relative risk toward the null if the
       exposure misclassificiation is unrelated to the future
       development of disease, which is usually the case



       Differential exposure misclassification is not likely in
       cohort studies
Hypothetical cohort study with 100% follow-up and 100%
accuracy in exposure and disease classification


                           No                 Incidence
               Disease              Total
                         Disease             ( x10,000 x 10 yrs)



     Exposed     75      15,000    15,075        49.75


       Non-
                150      135,000   135,150       11.10
     exposed



        Gold standard RR = 49.75/11.10 = 4.48
Hypothetical cohort study with 20% of exposed misclassified
as nonexposed and 10% of nonexposed misclassified as
exposed, independent of disease: nondifferential exposure
misclassification
                            No                 Incidence
               Disease               Total
                          Disease             ( x10,000 x 10 yrs)

                   75 –    15,000 –
                   15 +     3,000 +
  Exposed                            25,075       29.33
                   15 =    13,500 =
                     75      25,500
                  150 –   135,000 –
                   15 +     3,000 +
 Non-exposed                        124,650       12.03
                   15 =    13,500 =
                    150     124,500


       Biased RR = 29.33/12.03 = 2.44
Nondifferential exposure misclassification in a cohort study:
dietary assessment example


  • At baseline, study subjects complete a food frequency
     questionnaire about dietary habits over the past year.



     Measurement error due to imperfect recall will result in
     exposure misclassification –which will occur in both the
     exposed and nonexposed group
Hypothetical cohort study with 0.1% of nondiseased
misclassified as having developed the disease and 8% of the
diseased misclassified as nondiseased, independent of
exposure: nondifferential disease misclassification
                            No                 Incidence
               Disease               Total
                          Disease             ( x10,000 x 10 yrs)

                   75 +    15,000 -
                   15 -        15 +
  Exposed                            15,075       55.72
                    6=          6=
                     84      14,991
                  150 +   135,000 -
                  135 -       135 +
 Non-exposed                        135,150       20.20
                   12 =        12 =
                    273     134,877


       Biased RR = 55.72/20.20 = 2.76
Hypothetical cohort study with 0.5% of nondiseased in the
exposed group misclassified as having developed the
disease and 0.04% of the nondiseased in the nonexposed
group misclassified as having developed the disease:
differential disease misclassification
                            No                 Incidence
               Disease               Total
                          Disease             ( x10,000 x 10 yrs)


                   75 +    15,000 -
  Exposed          75 =        75 = 15,075        99.50
                    150      14,925
                  150 +   135,000 -
 Non-exposed        54=        54 = 135,150       15.09
                    204     134,946


            Biased RR = 99.50/15.09 = 6.59
Disease misclassification in cohort studies


    • Disease misclassification is a particular issue when
       information on disease is obtained from the members
       of the cohort themselves (e.g. health questionnaire)



       Whenever possible, subject reports about disease
       should be confirmed by more objective means, such
       as review of medical records
Disease misclassification in cohort studies


    • Differential misclassification is a concern if the study
       members involved in data collection on disease or in
       disease classification are aware of the exposure
       status of the subjects
Hypothetical case-control study with no misclassification of
exposure or disease



                      Cases    Controls


         Exposed         350         500


        Non-exposed      700       4,500



             Gold standard OR = 4.50
Hypothetical case-control study with 10% of cases
misclassified as controls and 5% of controls misclassified as
cases, independent of exposure: nondifferential disease
misclassification

                       Cases     Controls

                         350 –       500 +
                          35 +        35 –
         Exposed
                          25 =        25 =
                           340         510
                         700 –     4,500 +
                          70 +        70 –
       Non-exposed
                         225 =       225 =
                           855       4,345


                     Biased OR = 3.54
Nondifferential disease misclassification in      case-control
study: Alzheimer’s disease


    • Definitive diagnosis can only be made by brain
      biopsy, which isn’t done.



      We therefore must rely for diagnosis on clinical
      criteria and exclusion of other diseases.       The
      diagnostic criteria are imperfect and will result in
      misclassification of the disease status
Nondifferential disease misclassification in       case-control
study: Alzheimer’s disease


    • Persons with other types of dementia, such as multi-
      infarct dementia may be included in the case group.



    • Persons with early Alzheimer’s disease may be
      included in the control group
Hypothetical case-control study with 10% of exposed
controls misclassified as cases and 1% of nonexposed
controls misclassified as cases: differential disease
misclassification

                        Cases            Controls


        Exposed      350 + 50 = 400     500 – 50 = 450


      Non-exposed    700 + 45 = 745 4,500 – 45 = 4,455




                    Biased OR = 5.31
Differential disease misclassification in case-control study:
Alzheimer’s disease



    • Exposure: hypertension


       Hypertension is a risk factor for multi-infarct
       dementia, which could be confused with Alzheimer’s
       disease
Exposure misclassification in a case-control study: an
important source of both nondifferential and differential
misclassification


    • Classifying exposed persons as being nonexposed
      and nonexposed persons as being exposed will bias
      the odds ratio toward the null if the exposure
      misclassification is unrelated to disease status

    • Classifying exposed persons as being nonexposed
      and nonexposed persons as being exposed can bias
      the odds ratio in either direction if the exposure
      misclassification depends on disease status
Hypothetical case-control study with 20% of the nonexposed
misclassified as exposed and 16% of the exposed
misclassified as nonexposed, independent of disease:
nondifferential exposure misclassification

                         Cases                    Controls


        Exposed     350 + 140 – 56 = 434     500 + 225 – 80 = 1,320


      Non-exposed   700 – 140 + 56 = 616   4,500 – 225 + 80 = 3,680



                    Biased OR = 1.96
                    Example: dietary assessment
Hypothetical case-control study with 20% of the nonexposed
cases misclassified as exposed and 5% of the nonexposed
controls misclassified as exposed: differential exposure
misclassification

                         Cases            Controls


         Exposed     350 + 140 = 490     500 + 225 = 725


       Non-exposed   700 – 140 = 560 4,500 – 225 = 4,275



                     Biased OR = 5.16
                     Example: Recall bias
Types of information bias that can lead to differential
misclassification



             • Recall bias
             • Reporting bias
             • Observer bias
Recall bias

       Systematic error due to differences in accuracy of
       recall of past exposures or diseases between study
       groups



    • Example: family history of prostate cancer in a case-
       control study of prostate cancer
Recall bias

    • Men diagnosed with prostate cancer are often more
       aware of their family history than men who have not
       had prostate cancer



       In a case-control study, reporting of family history of
       prostate cancer could be more complete among
       cases than among controls, biasing the result away
       from the null hypothesis
Reporting bias


      Systematic error due to selective revealing or
      suppression of information about exposure or
      disease due to attitudes, beliefs, or perceptions



    • Example: married, apparently heterosexual men may
      not reveal homosexual behavior
Reporting bias


    • Example: persons who belong to religious groups
      that proscribe   alcohol   may   lie   about   alcohol
      consumption
Observer bias

      Systematic error due to well-intentioned members of
      the study team subconsciously or consciously
      collecting data or making decisions about subjects’
      exposure or disease status in different ways
      according to study group. This may occur because
      the observer has his/her own hypothesis about the
      relationship between exposure and disease
Observer bias

    • Interviewer bias: in a case-control study, an
      interviewer may probe more thoroughly for an
      exposure in a case than in a control



    • Abstractor bias: in a cohort study, a data abstractor
      may probe over the medical records of an exposed
      subject more thoroughly than the medical records of
      an unexposed subject to identify evidence of disease
Observer bias


    • Bias on the part of study team members involved in
      the classification of disease in a cohort study:
      classification of disease may be influenced by
      knowledge of the exposure status of the subject
Reducing bias

    • Ensure that the study design is appropriate for
      addressing the study hypotheses

    • Carefully define exposure and disease
    • Choose valid measurement methods
    • Train study personnel and standardize procedures
    • Perform quality control on all aspects of data
      collection and processing
Reducing bias

      Make every effort to maximize participation rates and
      to minimize losses to follow-up



    • Apply study methods in the same manner and with
      the same care to all study subjects, irrespective of
      the group to which they belong
        Blind interviewers, abstractors, and other study staff involved in
         data collection or exposure/disease classification to the subjects’
         case-control status in case-control studies and exposure status in
         cohort studies
        Blind study subjects and data collectors to study hypothesis
Reducing bias


    • If it is possible to improve the quality of exposure
      data in a case-control study in the case group or in
      the control group, but not in both, the investigator
      should resist the temptation to do so in order to
      preserve the validity of the comparison of exposures
      between cases and controls
Reducing bias


    • If it is possible to improve the quality of disease data
      in a cohort study in the exposed group or in the
      nonexposed comparison group, but not in both, the
      investigator should resist the temptation to do so in
      order to preserve the validity of the comparison of
      disease outcome between the exposed and
      nonexposed
Detection (surveillance) bias


       Error due to persons with an exposure of interest
       being under closer medical surveillance than persons
       without the exposure, resulting in a higher probability
       of detection of the disease of interest in exposed
       persons than in nonexposed persons
Detection bias is a threat when:


    • The disease has a high prevalence of asymptomatic
       cases, and would thus be more likely to be diagnosed
       in persons under close medical surveillance than in
       persons not under medical surveillance



    • The exposure of interest leads to frequent medical
       checkups:
                      A medical therapy
                      A medical condition
                      A harmful exposure
Detection bias in a case-control study: selection bias in
which selection of cases is related to the presence of the
exposure

     Example: Case-control study of hormone replacement
     therapy (HRT) use and breast cancer



   • Women who use HRT are likely to have more medical
     visits than women who do not

   • They may be more likely to have a screening
     mammography and have subclinical breast cancer
     detected
Detection bias in a case-control study: selection bias in
which selection of cases is related to the presence of the
exposure

     Example: Case-control study of hormone replacement
     therapy (HRT) use and breast cancer



   • HRT would cause breast cancer to be detected, but not
     to occur



     The OR for the relationship between HRT and breast
     cancer would be biased upward
Detection bias in a cohort study: information bias in which
exposed persons are under closer medical surveillance than
nonexposed persons


     Example: Cohort study of statin use and prostate cancer



   • Men who take statins have blood drawn periodically to
     check their serum cholesterol and liver function

   • May be more likely to have a PSA test than men not
     taking statins
Detection bias in a cohort study: information bias in which
exposed persons are under closer medical surveillance than
nonexposed persons


     Example: Cohort study of statin use and prostate cancer



   • This would lead to a higher probability of diagnosis of
     prostate cancer

   • Statin use would cause prostate cancer to be detected,
     but not to occur



     The RR for the relationship between statin use and
     prostate cancer would be biased upward
Detection bias: further observations


    • In a cohort study, more likely to occur when disease
       is ascertained through regular medical channels as
       opposed to when all study subjects are examined for
       disease using standardized methods (the same for
       exposed and nonexposed subjects) by members of
       the study team.
Detection bias: further observations


    • When detection bias occurs, the disease tends to be
       diagnosed in an early subclinical form in exposed
       persons more often than in nonexposed persons
          The RR or OR for the association between the exposure and less
           advanced disease is higher than the relative risk or odds ratio for
           the association between the exposure and more advanced disease
Qualitatively assessing how biases in case-control studies
work


    • In a case-control study, selection bias, information
      bias resulting in differential misclassification, or
      detection bias will lead to a biased distribution of
      subjects in the 2x2 table that is differential between
      cases and controls



      Assess which cells will be over-represented under
      various scenarios, as shown in the following slides
Over-representation of exposed cases


                    Cases        Controls

      Exposed         A              b
       Non-
      exposed
                        c            d

     OR = (   Ad / b
                  ) ( c)
     OR is biased upward
     Detection bias: HRT and breast cancer
Over-representation of nonexposed cases


                    Cases       Controls
        Expose
          d
                       a             b
        Non-
       exposed     C                 d

              d / bC
      OR = (a ) (       )
      OR is biased downward
      Differential exposure misclassification:
      Alcohol consumption and automobile accidents
Over-representation of exposed controls

                      Cases        Controls
        Expose
          d              a             B
        Non-
       exposed           c              d



              a
       OR = ( d)/(   Bc   )
       OR is biased downward
       Selection (nonparticipation) bias: Poor housing and
       elevated lead levels
Over-representation of nonexposed controls

                        Cases        Controls
           Expose
             d             a              b

           Non-
          exposed          c             D
 OR = (   aD)/(bc)
 OR is biased upward
 Selection bias: hospital-based case-control study in which
 investigator goes on a “witch hunt” against exposed controls

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:11
posted:8/8/2012
language:
pages:98