Case-Control Studies PDF

CASE CONTROL STUDIES 12 EBH11 Lecturer: Dr Maisoon Mairghani  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...

CASE CONTROL STUDIES 12 EBH11 Lecturer: Dr Maisoon Mairghani  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  Population Health Framework Epidemiology: Study design Evidence Ep Year 1 Year 2 ide y m lic FFP2 GIHEP po io log Ecological & Case Control h alt y He Cross-sectional eterminants Cohort Health syste Principles of Ethical Research alth d CV CNS ms Randomised Qualitative & He ea controlled trials mixed methods H lt h o n ti pro otec Resp ENDOB m o t io pr th Protecting Critical n H eal Disease prevention Participants appraisal Introduction to RCT Sustainability Critical Observatio Equity Appraisal nal Researching Systematic Vulnerable Reviews & Groups Meta-analysis  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 Describe a case control study and explain how it is conducted  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  Case-control study design Population Identify cases (i.e. all people in the population who have the outcome e.g. lung cancer) Identify controls (a representative sample of the study population without the outcome e.g. no lung cancer) Smokers Non-smokers  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  Why? Sample (n=400) 200 smokers, 200 non- Cohort study design: Association between smoking and lung cancer smokers 2 cases among non- smokers 5 cases among smokers 20 years Smokers Non-smokers  When cohort studies fail.. If the outcome is rare If the outcome takes a long time to develop – Very few people will develop the outcome – A very long follow-up period (possibly decades) would be needed – Cohort studies become very expensive and difficult to conduct – You might never identify enough people with the outcome to make meaningful conclusions  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  Selecting controls Often more than one control is selected for each case This increases statistical power – In other words, we say there is evidence of an association when there really is an association – we reach the right conclusion  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. QUESTIONS? Evaluate the advantages and limitations of case control studies  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 QUESTIONS? Identify and interpret the measure of association that can be calculated from a case control study  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  Analysing a case control study In a case control study we specifically include the people with the outcome Don’t start with a representative sample of the population and see who has the outcome Don’t start with a representative sample of the population who don’t have the outcome and see who develops it Can’t calculate prevalence or incidence Can’t calculate relative risk  The odds ratio In a case control study we can only calculate an odds ratio The odds ratio is the odds of exposure among the cases compared to the odds of exposure among the controls The odds ratio is a good approximation of the relative risk. 𝑂𝑑𝑑𝑠 𝑟𝑎𝑡𝑖𝑜 = The Australian Ovarian Cancer Study Group published a case-control study in 2008 examining risk factors for ovarian and other cancers. Participants were asked to complete a lifestyle and reproductive questionnaire. Data relating to cases with ovarian cancer, controls without ovarian cancer, and having a relative with ovarian and breast cancer are presented in this table Cases of ovarian cancer Controls Total First degree relative Yes 129 191 320 with breast/ovarian No 495 1296 1791 cancer Total 627 1508 21335 The odds of being “exposed” to a first degree relative with cancer among the cases is = ? The odds of being “exposed” to a first degree relative with cancer among the controls is = ? The odds ratio is:? The odds of being “exposed” to a first degree relative with cancer among the cases is 129/495 = 0.26. The odds of being “exposed” to a first degree relative with cancer among the controls is 191/1296 = 0.15. The odds ratio is: 𝑂𝑑𝑑𝑠 𝑜𝑓 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑎𝑚𝑜𝑛𝑔 𝑐𝑎𝑠𝑒𝑠 0.26 𝑂𝑑𝑑𝑠 𝑟𝑎𝑡𝑖𝑜 = = = 1.73 𝑂𝑑𝑑𝑠 𝑜𝑓 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑎𝑚𝑜𝑛𝑔 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠 0.15  Poll Which of the following is true? 1. People with malignancy are more likely to get Covid-19 that requires mechanical ventilation than those without malignancy 2. People with CVD are more likely to get Covid-19 that requires mechanical ventilation than those with CKD 3. People with asthma are nearly 3 times more likely to get Covid-19 that requires mechanical ventilation than those without asthma QUESTIONS? Identify three ways of dealing with confounding  Effects of confounding 1. Create an apparent association when one does not exist E.g., odds ratio for association between asthma and Covid-19 ICU admission is 2.84. If we controlled for educational level somehow, the odds ratio might be 1 2. Over- or under-estimation of the size of the true association E.g. odds ratio for association between asthma and Covid-19 ICU admission is 2.84. If we controlled for educational level, the odds ratio might be 2.00 3. Hide a true association if it exists E.g., if we found the odds ratio for association between asthma and Covid-19 ICU admission is 1 when we don’t control for educational level, and it becomes 2.84 when we do control for educational level. Asthma and Covid-19 ICU admission are associated, but the association is hidden unless we control for educational level 4. Reverse the direction of the association (Simpson’s paradox) E.g., odds ratio for association between asthma and Covid-19 ICU admission is 2.84. If we controlled for educational level, the odds ratio might become 0.9 (i.e., the association goes from a positive association to a negative association)  Dealing with confounding 1. Design a study in a way that minimises the effect of confounding factors Restriction: restricting the sample to people with or without the confounding variable, Matching: matching cases and controls for potential confounding factors, makes them more similar with respect to potential confounding factors. Randomisation (covered in Randomised Controlled Trials lecture)  Dealing with confounding 2. Use statistical methods for adjusting the effects of confounding Multivariable analysis using regression techniques Stratification (or post-stratification): splitting the sample into strata according to their level of the confounding variable (e.g. smokers and non-smokers) and estimating the association between the exposure and outcome for each strata  Multivariable regression Adjusts the estimated effect of an exposure on an outcome for the effect of other potentially confounding factors. Hence, derive an estimate of the independent effect of the exposure of interest Provides “adjusted” effect (e.g., adjusted odds ratio)  Poll After adjusting for age, sex, educational level, marital status and region of birth, are people with type 1 diabetes more likely to get Covid-19 requiring mechanical ventilation than those without type 1 diabetes? 1. Yes 2. No When you adjust for age and sex the odds ratio is 2.32. When you adjust for age, sex, educational level, marital status and region of birth the odds ratio is 3.13. Because the odds ratio changed when additionally adjusted for educational level, marital status and region of birth, indicates that these factors do confound the association between the exposure (type 1 diabetes) and outcome (Covid-19 requiring mechanical ventilation)  Exercise Exercise on interpreting adjusted effects on Moodle Thank you for listening and… Please add any questions to the forum on Moodle Recommended Reading Sections: 6.1 Why do a case-control study? 6.2 Key elements of study design 6.3.1 The odds ratio 5.7.5 Methods for dealing with confounding  Additional resources Science: from cradle to grave https://www.bbc.co.uk/programmes/b012wg2q