Case-Control Studies PDF

CASE CONTROL STUDIES Lecturer: Prof Ghufran Ahmed Jassim RCSI DEVELOPING HEALTHCARE LEADERS WHO MAKE A DIFFERENCE WORLDWIDE  Learning Outcomes By the end of the lecture, students will be able to: Describe a case control study and explain...

CASE CONTROL STUDIES Lecturer: Prof Ghufran Ahmed Jassim RCSI DEVELOPING HEALTHCARE LEADERS WHO MAKE A DIFFERENCE WORLDWIDE  Learning Outcomes By the end of the lecture, students will be able to: Describe a case control study and explain how it is conducted Evaluate the advantages and limitations of case control studies Identify and interpret the measure of association that can be calculated from a case control study Identify three ways of dealing with confounding  Did the observer assign the exposure? yes no Intervention studies Observational studies Random Comparison allocation? group? yes no yes no Randomised Non-randomised Analytic study Descriptive study trial trial Ecological Case-control Cohort Cross-sectional study study study study  Did the observer assign the exposure? yes no Intervention studies Observational studies Random Comparison allocation? group? yes no yes no Randomised Non-randomised Analytic study Descriptive study trial trial Ecological Case-control Cohort Cross-sectional study study study study  There are two possible ways of measuring association between exposure and outcome  Describe a case control study and explain how it is conducted RCSI DEVELOPING HEALTHCARE LEADERS WHO MAKE A DIFFERENCE WORLDWIDE  The case-control study approach A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the disease or condition (controls). Purpose is to examine association between an exposure(s) and an outcome Four key steps 1. Identify cases – the people with the disease or outcome 2. Identify the controls – the people who do not have the disease or outcome 3. Measure exposures (e.g. potential risk factors for the outcome) among the cases and controls 4. Analyse whether or not the cases are more likely to have been exposed to a risk factor than the controls DESIGN OF A CASE-CONTROL STUDY Not Not Exposed Exposed Exposed Exposed Disease No Disease “CASES” “CONTROLS”  The case-control approach Individuals are selected from a defined population on the basis of their disease/condition status Start here Smokers Cases: Lung cancer Non smokers Smokers Controls: no lung cancer Non-smokers Study  Case-control study design Population Identify cases (i.e. all people in the population who have the condition) Identify controls (a representative sample of the study population without the condition) Smokers Non-smokers  Where a cohort study is difficult… If the outcome is rare And/or if the outcome has long latency period A cohort study will need to have a large sample size, have a long follow-up…….. And therefore be very costly and difficult to conduct  Conducting a case-control study Must clearly state the research question in order to develop clear definitions of cases and controls Case Definition Definition should be replicable and applied to all cases Can be based on clinical or laboratory definition Must state inclusion and exclusion criteria  Example: case definition Quantification of risk factors for herpes zoster Data source: General practice database Case definition: Individuals with a code for herpes zoster in general practice records or Hospital Episode Statistics Inclusion criteria: – Patients aged 18 years and over – Under follow-up between 1 January 2000 and 31 December 2011 – No evidence of previous zoster – At least 12 months of follow-up prior to first diagnosis of zoster to exclude past cases of zoster recorded retrospectively after registration at a GP Taken from Forbes HJ, Thomas SL, Clayton T. Quantification of risk factors for herpes zoster: population based case-control study. BMJ 2014;348:g2911.  Identifying cases Must have a clear case definition – Definition should be replicable and applied to all cases – Can be based on clinical or laboratory definition Must describe carefully how cases are selected  Selecting controls Selection of appropriate controls is often the most demanding and difficult part of a case-control study Controls should be representative of the population from which the cases have arisen. But they should be without the disease or outcome.  Selecting controls Need to understand from what population the cases arose from Sources of “population controls” or “population-based controls” include Population registers Electoral rolls General practice databases  Measuring exposures Data on exposures can be measured in a variety of ways e.g., by interview, reviewing medical records, using biological samples. As with all studies you need to use a method that is valid and reliable to measure the exposure (and indeed the outcome). Case-controls studies are often not suitable to use when the exposure is rare.  Advantages Useful for rare diseases (i.e. outcomes) Useful for diseases with long latency Often cheaper and quicker than cohort studies Can study association between multiple exposures and an outcome Can conduct expensive or time-consuming tests, which may not be possible with a cohort study  Summary of sources of error All apply to case-control studies Source Type of error Selection of participants Sampling error (i.e., sampling) Selection bias Measurement – instrument Inaccuracy (poor validity) (e.g. a self-administered questionnaire, monitor, interview) Poor reliability Measurement – observer Between observers Within observers  Selection bias Controls are not representative of the population that cases come from Particularly arises if using hospital controls Hospital controls are usually people who are patients at the same hospital(s) as the cases who do not have the disease Have to ensure that – There are no health-care access issues that prevent hospital controls being representative of the population – The disease for which they were admitted is not related to risk factors for the outcome of interest – The distribution of exposures in the hospital controls may differ to the distribution of exposures in the population that cases came from  Observer bias (a.k.a. interviewer bias) Often information on exposures is collected by interview Interviewers knowing whether they are talking to a case or a control may change how they collect data on the exposure To minimise observer bias: Train interviewers and use standardised questioning Blind interviewers to whether a person is a case or control Limit knowledge among interviewers about the hypothesis being tested (e.g., don’t tell them which exposure is of most interest)  Recall bias Cases may describe their level of exposure differently than controls, even if there is no difference Having a disease may make people more aware of an exposure or the importance they attach to it Minimise recall bias by blinding cases and controls to the research question  Confounding As with all observational studies, an apparent association between an exposure and outcome may be due in part or whole to a third factor  Identify and interpret the measure of association that can be calculated from a case control study RCSI DEVELOPING HEALTHCARE LEADERS WHO MAKE A DIFFERENCE WORLDWIDE  Analysis of case-control studies Cases Controls Total Exposed Unexposed  Analysis of case-control studies Cases of Controls Total ovarian cancer First degree Yes 129 191 320 relative with breast/ovarian No 495 1296 1791 cancer Total 627 1508 2135 Taken from Jordan SJ, Green AC, Whiteman DC, Moore SP, Bain CJ, Gertig DM, et al. Serous ovarian, fallopian tube and primary peritoneal cancers: a comparative epidemiological analysis. 2007;122:1598- 1603.  Analysis of case-control studies Cases of Controls Total ovarian cancer First degree Yes 129 191 320 relative with breast/ovarian No 495 1296 1791 cancer Total 627 1508 2135 Odds of exposure = number of exposed people/ number of unexposed people !"#$%& '( )'#%* )+,-./*.%& )-' -/0% / &%1/,+0% )+,- $&%/2, '& '0/&+/*./*.%& 789 𝑂𝑑𝑑𝑠 𝑜𝑓 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑖𝑛 𝑐𝑎𝑠𝑒𝑠 = !"#$%& '( )'#%* )+,-./*.%& )-' 𝒅𝒐 𝒏𝒐𝒕 -/0% / &%1/,+0% )+,- $&%/2, '& '0/&+/*./*.%& = :9; = 0.26  Analysis of case-control studies Cases of Controls Total ovarian cancer First degree Yes 129 191 320 relative with breast/ovarian No 495 1296 1791 cancer Total 627 1508 2135 Odds of exposure = number of exposed people/ number of unexposed people 𝑂𝑑𝑑𝑠 𝑜𝑓 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑖𝑛 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 = ?