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PLEASE NOTE: We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed. The prodominant study designs can be categorised into observational and interventional studies. Observational studies, such as cross-sectional, case control and cohort studies, do not actively allocate participants to receive a particular exposure, whilt interventional studies do. Each of the above study designs are described here in turn. Cross-sectional StudiesIn a cross-sectional study, data are collected on the whole study population at a single point in time to examine the relationship between disease (or other health-related outcomes) and other variables of interest (exposures). Cross-sectional studies therefore provide a snapshot of the frequency of a disease or other
health-related characteristics in a population at a given point in time. This methodology can be used to assess the burden of disease or health needs of a population, for example, and is therefore particularly useful in informing the planning and allocation of health resources. Types of cross-sectional study
In practice cross-sectional studies often include an element of both types of design. Issues in the design of cross-sectional surveys
Potential bias in cross-sectional studies Non-response is a particular problem affecting cross-sectional studies and can result in bias of the measures of outcome. This is a particular problem when the characteristics of non-responders differ from responders. Recall bias can occur if the study asks participants about past exposures. Analysis of cross-sectional studies In a cross-sectional study all factors (exposure, outcome, and confounders) are measured simultaneously. The main outcome measure obtained from a cross-sectional study is prevalence: For continuous variables such as blood pressure or weight, values will fall along a continuum within a given range. Prevalence may therefore only be calculated when the variable is divided into those values that fall below or above a particular pre-determined level. Alternatively, mean or median levels may be calculated. In analytical cross-sectional studies the odds ratio can be used to assess the strength of an association between a risk factor and health outcome of interest, provided that the current exposure accurately reflects the past exposure. Strengths and weaknesses of cross-sectional studies Strengths
Weaknesses
Case Control StudiesIn a case-control study the study group is defined by the outcome (e.g. presence of a disease), not by exposure to a risk factor. The study starts with the identification of a group of cases (individuals with a particular health outcome) in a given population and a group of controls (individuals without the health outcome) from the same population. The prevalence of exposure to a potential risk factor is then compared between cases and controls. If the prevalence of exposure is more common among cases than controls, the exposure may be a risk
factor for the outcome under investigation. One of the advantages of case-control studies is that they can be used to study outcomes or diseases that are rare. However, a major characteristic is that data on potential risk factors are collected retrospectively and as a result may give rise to bias. This is a particular problem associated with case-control studies and therefore needs to be carefully considered during the design and conduct of the study. Issues in the design of case-control studies
The procedures used for the collection of exposure data should be the same for cases and controls. Common sources of bias and confounding in case-control studies Due to the retrospective nature of case-control studies they are particularly susceptible to the effects of bias which may be introduced as a result of a poor study design or during the collection of exposure and outcome data. Because the disease and exposure have already occurred at the outset of a case-control study there may be differential reporting of exposure information between cases and controls based on their disease status. Cases and controls may recall past exposure differently, because knowledge of being a case may affect whether the individual remembers a certain exposure, for example (recall bias). Similarly, the recording of exposure information may vary depending on the investigator’s knowledge of an individual's disease status (interviewer/observer bias). Selection bias can occur in case-control studies when the selected control group are not representative of the population from which the cases arose, thus comparisons of exposure distributions between cases and controls may give misleading results. Temporal bias (also known as reverse causality) may also occur in case-control studies. When trying to establish a link between exposure and outcome, it must be clear that the exposure occurred well before the diagnosis of the disease of interest. Therefore, the design and conduct of the study must be carefully considered as there are limited options for the control of bias during the analysis. A confounder is a factor associated independently with both the exposure and outcome, and can be a problem where cases and controls differ with respect to a potential confounder. It can be dealt with at two stages:
More detail on adjusting for confounding can be found in the chapter
“Bias and confounding”. Analysis of case-control studies The odds ratio (OR) is used in case-control studies to estimate the strength of the association between exposure and outcome. It is not possible to estimate the incidence or risk of disease from a case-control study, unless the study is population-based and all cases in a defined population are obtained. The results of a case-control study can be presented in a 2x2 table as follows: The odds ratio is a measure of the odds of exposure in the cases (= a/c), compared to the odds of exposure in the control group (= b/d). It is calculated as follows: Example: Calculate the odds ratio from a hypothetical case-control study of smoking and pancreatic cancer among 100 cases and 400 controls, the results of which are shown below. The OR calculated from the hypothetical data suggests that individuals with cancer of the pancreas (cases) are more likely to have smoked than those without the disease. Specifically, participants with pancreatic cancer have 4.5 times the odds of being smokers compared to those without pancreatic cancer. NB: The odds ratio above has been calculated without
adjusting for potential confounders. Further analysis of the data would involve stratifying by levels of potential confounders such as age. The 2x2 table can then be extended to allow stratum-specific rates of the confounding variables to be calculated and, where appropriate, an overall summary measure adjusted for the effects of confounding and a statistical test of significance. In addition, confidence intervals for the odds ratio would also be presented. Strengths and weaknesses of case-control studies Strengths
Weaknesses
Nested Case-control Studies A nested case-control study is one where the cases and controls are selected from individuals within an established cohort study. The case-control study is thus said to be ‘nested’ within the cohort study. Cases of a disease that arise within the defined cohort during the follow up period are identified, then a specified number of matched controls who have not developed the disease are selected from the same cohort. The main advantage of nested case-control studies is that certain exposure data will already have been collected for both cases and controls which limits the potential for recall bias. Analysis is carried out in the same way as for normal case-control studies, with the calculation of odds ratios. Strengths of nested case-control studies
Weaknesses of nested case-control studies
Examples of case-control studies2 Case-control studies have been used in a variety of situations to evaluate possible causes of rare conditions. Classic examples include the investigation of cases of childhood leukaemia near the nuclear procession plant at Sellafield in Cumbria (UK), as well as cases of vaginal adenocarcinoma, which is normally rare, but were seen in higher numbers than usual in the USA in the 1970s. The difference between the young women with vaginal adenocarcinoma and their comparison group was that the mothers of cases had taken stilboestrol during the pregnancy (to prevent miscarriage), but the mothers of the controls had not.
Cohort StudiesCohort studies evaluate a possible association between exposure and outcome by following a group of exposed individuals over a period of time (often years) to see whether they develop the disease or outcome of interest. A cohort is a group of individuals who share a common characteristic, and may be chosen based on a population definition, or based on a particular exposure (see “Selection of study groups”, below). The incidence of disease in the exposed individuals of the cohort is then compared to the incidence of disease in unexposed, or lowest risk, individuals, and a relative risk (incidence risk or incidence rate) is calculated to assess whether the exposure and disease are associated. Cohort studies may be prospective or retrospective, but both types define the cohort on the basis of exposure, not outcome. Prospective cohort studies – participants are identified and followed up over time until the outcome of interest has occurred, or the time limit for the study
has been reached. A temporal relationship between exposure and outcome can thus be established. Retrospective cohort studies – exposure and outcome have already occurred at the start of the study. Pre-existing data, such as medical notes, can be used to assess any causal links, so lengthy follow-up is not required. This type of cohort study is therefore less time consuming and costly, but it is also more susceptible to the effects of bias. For example,
the exposure may have occurred some years previously and adequate, reliable data on exposures may be differentially recorded in eventual cases compared to controls. In addition, information on confounding variables may be unavailable, inadequate or difficult to collect. Issues in the design of cohort studies
Potential sources of bias in cohort studies A major source of potential bias in cohort studies is losses to follow-up. Cohort members may die, refuse to continue participation in the study or fail to maintain contact. Such events may be related to the exposure, outcome or both, resulting in loss to follow-up bias. For example, individuals who develop a precursor to the outcome, such as symptoms of angina where the outcome of interest is a heart attack, may be less likely to continue to participate in the study. The degree to which losses to follow-up are correlated with either exposure or outcome can lead to significant bias in the measurement of the relationship between the exposure and outcome.1 Another source of potential
bias in cohort studies arises from the degree of accuracy with which subjects have been classified with respect to their exposure or disease status. Differential misclassification – when one group of participants is more likely to have been misclassified than the other – can lead to an over- or underestimation of the relationship between the exposure and outcome.1 Analysis of cohort studies The analysis of a cohort study uses the ratio of either the risk or rate of disease in the exposed cohort, compared with the risk or rate in the unexposed cohort. If follow-up times differ markedly between participants, a rate may be more appropriate. The risk ratio uses as a denominator the entire group recruited at the start of the study while the rate ratio uses as a denominator the person-years which accounts for different lengths of time spent in the study. Different lengths of time spent in a study are due to varying start points, and incorporate multiple possible end points (i.e. one is no longer in the study on developing the outcome, or following death or moving away). Calculation of the rate ratio from a hypothetical cohort study of smoking and pancreatic cancer followed-up for 1 year. From the data in the table above, the rate ratio and attributable risk can be calculated as follows: The rate ratio of 15 indicates that the risk of developing pancreatic cancer over 1 year is 15 times higher among smokers than non-smokers. Attributable risk (AR) AR = incidence rate among exposed (r1) – incidence rate among unexposed (r0) AR = 1.5 – 0.1 =
1.4 /1000/yr The attributable risk of pancreatic cancer due to smoking is 1.4 cases per 1000 per year Attributable risk percentage (ARP), which gives the proportion of cases attributable (and thus avoidable) to an exposure in relation to all cases, is calculated as: This can be interpreted as follows: smoking accounts for 93% of all cases of pancreatic cancer among smokers. Standardised mortality and morbidity ratios (SMR) are another commonly used method of presenting results in a cohort study. See chapter “Numerators, denominators and populations at risk” for further information on SMR. Strengths and weaknesses of cohort studies Strengths
Weaknesses
Examples of cohort studies2 One of the most famous examples of a cohort study is Sir Richard Doll’s study of the hazards of cigarette smoking in a cohort of nearly 35,000 British doctors. Baseline information about their smoking habits was obtained in 1951, and periodically thereafter. Cause-specific mortality was then monitored for 50 years and the results showed an excess mortality associated with smoking, chiefly due to vascular, neoplastic, and respiratory diseases. Other cohorts include the Framingham cardiovascular studies, following people living in the town of Framingham, Massachusetts. Much of our current knowledge about heart disease, such as the effects of diet, exercise, and common medications such as aspirin, is based on this longitudinal cohort study. In the UK, the Whitehall studies have followed cohorts of British civil servants, demonstrating how groups in the cohort with differing levels of a characteristic, such as cholesterol, subsequently have different rates of ischaemic heart disease. Cohort studies are also useful to study the longer-term effects of an unusual event. The most famous example of this is the cohort of people who survived
the atomic bomb explosions at Hiroshima and Nagasaki.
Intervention Studies (including Randomised Controlled Trials) Intervention studies can generally be considered as either preventative or therapeutic, and types of experimental intervention include:1
Therapeutic trials are designed to evaluate the effect of therapies, such as new drugs or surgical procedures. They may also be known as clinical trials, and are conducted among individuals with a particular disease to assess the effectiveness of an agent or procedure in achieving a specific
outcome, such as reduced mortality.1 Preventative trials are designed to evaluate whether an agent or procedure reduces the risk of developing a particular disease. They are carried out on individuals free from the disease at the beginning of the trial, but deemed to be at risk.1 Preventative trials may be conducted among individuals or entire communities, and examples include evaluations of new vaccines or bed nets to prevent infection
with malaria. Characteristics of an intervention study
The main intervention study design is the randomised controlled trial (RCT). Randomised controlled trials The randomised controlled trial is considered the most rigorous method of determining whether a cause-effect relationship exists between an intervention and outcome.4 The strength of the RCT lies in the process of randomisation which is unique to this type of study design. In an RCT, study participants are generally randomly assigned to one of two groups, the experimental group who will receive the intervention being tested, and a comparison group (controls) who receive a conventional treatment or placebo.4 These groups are then followed prospectively to assess the effectiveness of the intervention compared with the standard or placebo treatment. Subjects are randomly allocated to the two study arms so that the intervention and control groups are as similar as possible in all respects, apart from the treatment. Potential confounding factors should be equally distributed between the two groups. The choice of comparison treatments may include an existing standard treatment, no treatment, an alternative treatment or a placebo. A placebo is a substance that resembles the intervention treatment in all respects except that it contains no active ingredients. Figure 1. General outline of a two-arm randomised controlled trial. Basic outline of the design of a randomised controlled trial Prior to the conduct of an RCT, a comprehensive study protocol should be developed and registered with a trials database, such as ClinicalTrials.gov. The study protocol will include information on:
The gold standard of intervention studies is the randomised, double-blind placebo-controlled trial. This attempts to reduce bias in the following ways:3
Randomisation The aim of randomisation is to ensure that any observed differences between the study groups are due to differences in the treatment alone and not due to the effects of confounding or bias. Randomisation ensures that trial groups are similar at the outset in all respects with the exception of the intervention under investigation (i.e. they are balanced). Note that there still may be differences in how the groups are handled as the trial progresses (introducing performance bias). Methods of random allocation are used to ensure that all study participants have the same chance of allocation to the treatment or control group, and that the likelihood of receiving an intervention is equal regardless of when the participant entered the study. Therefore, the probability of any participant receiving the intervention should be independent of any other participant being assigned that treatment. The assignment of study subjects to each intervention is determined by formal chance processes and cannot be predicted or influenced by the investigator or participant. In a well-designed RCT, the random allocation sequence is pre-determined and cannot be influenced. It is important that those responsible for recruiting participants into a study are unaware which study arm the individual will be
allocated to. Allocation concealment avoids both conscious and unconscious selection of patients into the study, and attempts to ensure that the investigators cannot manipulate the trial by influencing which arm participants are enrolled into. One method of achieving this is by concealing allocation details in sealed, opaque envelopes. An alternative (and better) method is central randomisation by telephone, where the clinician calls a randomisation service to get the treatment allocation. The
latter allocation mechanism is beyond the control of both the investigator and the participant, limiting potential bias in allocating the treatment. The process should be carried out once the participant has been determined to be eligible for inclusion and after they have given informed consent to participate. Methods of treatment allocation
Once allocation is complete, the success of the randomisation process should be confirmed by comparing baseline factors between the two groups, to ensure that they are similar. Advantages of randomisation
Disadvantages of randomisation
Blinding in randomised controlled trials Blinding is used in RCTs to ensure that there are no differences in the way the study arms are assessed or managed, thus minimising bias. Bias may be introduced, for example, if the investigator is aware which treatment a subject is receiving, as this may influence (intentionally or unintentionally) the way in which they measure or interpret the outcome data. Similarly, a subject's knowledge of treatment assignment may influence their response to a specific treatment. Blinding also involves ensuring that the intervention and standard/placebo treatment appears the same. However, blinding is not always possible, for example when the treatment involved physiotherapy exercises, a weight loss programme or counselling. In a double-blind trial,
neither the investigator nor the study participant are aware of treatment assignments. However, this design is not always feasible and a single-blind RCT is where the investigator, but not the study participant, knows which treatment has been allocated. Variations of the randomised control trial
Ethical issues The use of RCTs raises important ethical issues. For example, there must be sufficient doubt about the particular agent being tested to allow withholding of it from half the subjects, and at the same time there must be sufficient belief in the agent's potential to justify exposing the remaining half of all willing and eligible participants.1 This is known as clinical equipoise. In addition, there must be sufficient belief that the intervention under investigation is safe. Informed consent is essential in RCTs (as it is in other study designs). Study subjects must understand that they are participating in an experiment and that in a placebo-controlled trial they may receive an inactive product. In addition, participants must be informed of the aims, methods and potential benefits or hazards of participating in the trial. In a controlled trial careful consideration should also be given to what intervention is given to the control group. For example, if an effective treatment already exists, participants in the control group should not receive a placebo, depriving them of treatment. Any comparison should therefore be between the current standard treatment and the new treatment. It is essential that study participants do not suffer as a consequence of a RCT. Most RCTs incorporate a data monitoring committee who are independent of the investigators, whose function is to review safety and efficacy data, and to ensure quality and compliance. Analysis of RCTs The analysis of RCT data is focused on estimating the size of the difference in predefined outcomes between the intervention and control groups. The main measure of effect obtained is the rate or risk ratio. For trials of preventative interventions, the protective efficacy (or effectiveness) is calculated as: (where R is the risk or rate) Intention-to-treat analysis When RCTs are analysed using an intention-to-treat analysis (ITT), participants’ results are analysed in the group to which they were originally assigned.5 This should happen regardless of whether they were lost to follow-up, or whether they switched treatment groups during the trial. During the RCT subjects may refuse to continue to participate and stop taking their allocated treatment. If the investigators exclude participants from the analysis where they have not adhered to their allocated treatment strategy, the estimate of the effect of the treatment is likely to be flawed. The aim of the intention-to-treat analysis is to provide a pragmatic (real-world) estimate of the benefit of the treatment under investigation, rather than of its potential benefit in patients who receive treatment exactly as planned. Subjects who failed to take their allocated treatment may have done so because of adverse side effects, or because they felt it was not working, and - in the real world - patients do not always take their medication as prescribed. Additionally, there is evidence to suggest that those participants who are fully compliant with their prescribed medication do better than those who do not adhere, even after adjustment for all known prognostic factors and irrespective of assignment to active treatment or placebo. Excluding non-compliant participants from the analysis leaves those who may be destined to have a better outcome, and destroys the unbiased comparison afforded by randomisation.6 Where a comparison is performed using only those participants who completed the treatment originally allocated, this is known as a per protocol analysis. Where full outcome data is not known for all subjects, then researchers can either impute an outcome for each individual lost based on shared characteristics with other individuals in the study, or use the last recorded measure. In summary,
during the statistical analysis of an RCT, all study subjects should ideally be retained in the group to which they were originally allocated regardless of whether or not that was the treatment received. Strengths of randomised controlled trials A well designed RCT provides the strongest epidemiological evidence of any study design about the effectiveness and safety of a given intervention.
Weaknesses of randomised controlled trials
Examples of RCTs One of the first randomised trials to be carried out was the evaluation of streptomycin in the treatment of tuberculosis, published in 1948. Since then, there have been numerous other examples which have had a significant impact on clinical practice. A non-randomised study published in 1980 evaluated the possible benefit of vitamin supplementation at the time of conception in women at high risk of having a baby with a neural tube defect. The investigators found that the vitamin group subsequently had fewer babies with neural tube defects than the placebo control group. The control group included women ineligible for the trial as well as women who refused to participate. As a consequence, the findings were not widely accepted, and the Medical Research Council later funded a large RCT to answer the question in a way that would be widely accepted.7
References
Further Resources
© Helen Barratt, Maria Kirwan 2009, Saran Shantikumar 2018 Which types of study design could be used to measure the incidence of a disease?Cohort studies are used to study incidence, causes, and prognosis. Because they measure events in chronological order they can be used to distinguish between cause and effect. Cross sectional studies are used to determine prevalence.
What is the best design to study the etiology of a disease?Incidence studies are usually the preferred approach to studying the causes of disease, because they use all of the available information on the source population over the risk period.
What type of study design was used to determine the cause of the outbreak?Overview. Analytic studies are used during an outbreak investigation if and when there is at least one clear hypothesis regarding the source of the outbreak. By using an analytic study, investigators can estimate the risks of illness/disease associated with a hypothesis (e.g., a specific exposure).
Which type of study design is best for studying factors that might cause a rare disease?Case-control studies are inexpensive, efficient, and often less time-consuming to conduct. This study design is especially suitable for rare diseases that have long latency periods.
What designs exist to identify and investigate factors in disease?What designs exist to identify and investigate factors in disease? clinical trials are the most well known experimental design. Clinical trials use randomly assigned data.
Which of the following research designs is used to investigate the cause of different diseases in a prospective?Cohort studies are used instead of experimental study designs, such as clinical trials, when experiments are not feasible for practical or ethical reasons, such as when investigating the effects of a potential cause of disease.
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