1 Study Design Imre Janszky Faculty of Medicine, ISM NTNU

2 General notes on study design Randomized trials Cohort studies Case-control studies Cross-sectional studies Ecologic studies Overview

3 Choice of study design: –Ethics –Financial etc. resources –Available time –What type and amount of systematic error can be expected for the given research question with different study designs General notes

4 Randomized Trials Cohort Studies Case-control Studies Cross-sectional Studies Ecological studies } Experimental Observational

5 Randomized Trials Cohort Studies Case-control Studies Cross-sectional Studies Ecological studies Analytic Descriptive

6 Randomized Trials Cohort Studies Case-control Studies Cross-sectional Studies Ecological studies amount of systematic error (in general)

7 Randomized Trials Cohort Studies Case-control Studies Cross-sectional Studies Ecological studies possibility for causal inference (in general) amount of systematic error (in general)

8 Gold standard, if possible Main pillar of evidence based medicine Individuals or groups of individuals (= cluster randomization) are randomly assigned to exposure status Randomized studies

9 Use of placebo group or alternative treatment group to avoid placebo effect Blinding: to avoid placebo effect, to ensure equivalent management, follow up and objective assessment of outcome Randomized studies

10 Randomization might eliminate confounding by eliminating the association between the exposure and the potential confounders But the number of randomized units must be high to achieve this In practice, randomized trials are often confounded, especially cluster randomized studies Detection and control for confounding in the same way as for observational studies Randomized studies and confounding

11 High costs Ethical considerations Selection (only those who are asked and who agree to be randomized; children, women and those with comorbidities are often excluded) Often only short follow up Randomized studies – problems

12 Inherently an experimental situtation, patients may behave differently, if they know it is an experiment Adherence problems, participants may change their minds, stop the treatment, seek alternative treatment outside of the study etc (solution: Intention-to-treat analysis) Randomized studies – problems

13 Intention-to-treat analysis (gold standard): everyone is analysed according to the randomization, irrespective of the actual treatment, i.e. the exposure will be random even if actual treatment is not; tends to underestimate the real association As-treated analysis (typically used only as secondary analyses): everyone is analysed according to the actual treatment; this approach can introduce confounding since the exposure will not be random and thus might both over- or underestimate associations Randomized studies – analytic strategies

14 Cohort study: a group of people (=cohort) is followed for an event of interest Censored observation: the event of interest has not occurred (until the last contact) Uncensored observation: the event of interest has happened during the follow-up time Cohort Studies

15 Cohort studies = event of interest end of follow up start of follow up

16 Cohort studies The baseline often varies for participants (depending on the recruitment procedure)

17 Generally risks, incidence rates and their differences and ratios are all available If censoring is dependent on the outcome and exposure: biased follow up (type of selection bias) Several diseases and several exposures can be investigated at the same time Generally rather expensive, especially for rare diseases Cohort Studies

18 Case-control studies Ideally, a case-control study can be conceptualized as a more efficient version of a corresponding cohort study Incident cases are identified as either exposed or unexposed, controls are sampled from the source population that gives rise to the cases. The controls are identified as either exposed or unexposed; the controls should be sampled independently of exposure status. Usually much cheaper than cohort studies, especially for rare diseases

19 Cohort studies = event of interest end of follow up start of follow up

20 Case-control studies = included to the case-control study end of follow up start of follow up

21 Exposure Present Exposure Absent Disease Present abAll diseased Disease Absent cdNot diseased All exposedNot exposedAll subjects Odds ratio (OR)= (a*d)/(b*c) OR can be a good approximation of the relative risk (incidence rate ratio or risk ratio) Odds itself: p / (1-p) where p is probability

22 Case-control studies Generally we can investigate only one disease, but several exposures Generally only relative comparison of disease occurrence is possible (i.e. ratio of risks and incidence rates), risk, incidence rate and their differences are not available

23 Case-control studies In the past, there were many case-control studies with wrong sampling strategies, which is partly responsible for the somewhat bad reputation of case-control studies There are several correct sampling strategies, but the one recommended for most situations, if possible, is called “density sampling” or “incidence density sampling”

24 Case-control studies Special biases: –inappropriate selection of controls (i.e. if selection of controls from the population of potential controls depends on exposure status, type of selection bias), –recall bias (controls remember differently the exposure than the cases, type of information bias) Nested case-control study: when researchers have full access to the corresponding cohort study, selection of control is unbiased and these studies are often free of recall bias

25 Matching in case-control studies Controls typically matched to cases on the basis of specific features (age, sex etc.) Makes control for confounding more efficient Can create bias and must be handled in analyses Individuals (individual matching) or group of individuals (frequency matching) can be matched

26 A population is investigated at one point in time (snapshot) Typically prevalence is the measure of disease occurrence (cross-sectional studies are also often called prevalence studies) Often the aim is just descriptive Problems for causal inference: –Prevalence depends on the duration of disease –Temporality (it is not clear whether the exposure is the consequence of the disease or vice versa) Cross-sectional studies

27 Data are available only on group level, e.g. exposure is the average alcohol consumption of a country, outcome is incidence rate of myocardial infarction in that country, no data on individuals’ alcohol consumption and no data on individuals’ disease status Ecologic bias: any error which arises from aggregation of data Ecologic studies are very prone to bias, but typically are the cheapest of all designs, often the very first step to study a certain exposure – disease association Ecologic studies