They are also useful in the study of infections [ 9 ] and for hypothesis generation [ 19 ]. Due to the design of cohort studies, and since temporal sequence is present, both incidence rate and cumulative incidence can be calculated [ 2 , 8 , 20 , 21 , 22 ]. They also allow for the measurement of relative risk RR [ 2 , 8 , 23 ], hazard ratio [ 8 ], and attributable risk [ 8 , 23 ].
Furthermore, they allow for the study of multiple outcomes that can be associated with a single type of exposure [ 2 , 20 ] or multiple exposures [ 18 ]. Additionally, they allow for the study of rare exposures [ 2 , 18 , 20 ].
Finally, cohort studies have lower risk of encountering survivor bias [ 2 ], and recall bias [ 9 , 21 ]. Among the disadvantages of cohort studies is selection bias, which may occur when the participants are not representative of the population or of the patient grouping that they fall under.
This in turn will influence how well or not the results can be generalized to the rest of the population, in what is known as external validity [ 2 , 12 , 18 , 24 , 25 ]. This will be covered later in section three of this chapter under aspects of cohort studies. Another disadvantage is that causation cannot be established from cohort studies [ 18 , 20 ], as it would require an experimental design in order to determine any causal effect [ 20 ].
However, due to the longitudinal design of cohort studies, they may aid in studying a certain causal hypothesis [ 20 ]. A third disadvantage is that they require a large sample size, which might pose an issue when dealing with outcomes that take a long time to develop [ 10 ]. Finally, cohort studies cannot be used to study rare outcomes [ 23 ]. Cohort studies are either prospective or retrospective [ 1 , 2 , 18 ].
In the former, the researcher would assess exposure at baseline and then follow the person over time in order to determine the outcome such as the development of a disease [ 9 , 18 , 20 , 21 , 26 ].
In the latter, the order is reversed, as a cohort is established after the follow up has been conducted, or the outcome has developed, and exposure is then assessed in a retrospective manner [ 9 , 18 , 20 , 21 , 27 ]. Merrill indicates that the outcome status at the start of the study is what determines the overall study type.
If the outcome has not yet developed then it is a prospective study, and if the outcome has already developed then it is a retrospective study [ 23 ].
Cohort studies can also be classified based on whether or not participants are replaced once they are lost. If those that drop out or are lost to follow up are replaced with new participants, then this would be classified as a dynamic or an open cohort. In the case that those lost do not get replaced, then it would be classified as a fixed or closed cohort [ 4 , 20 ].
Prospective cohort studies, as the name indicates, observes a group of people after being exposed to a certain factor in order to investigate the outcome, following the natural sequence of time, starting with the present and looking forward in time [ 12 , 18 , 20 ], which in turn provides true risk absolute estimates for the groups under investigation [ 26 ].
It is considered the gold standard among observational studies [ 8 ]. Under this type of study, the researcher would have control over data collection methodology, as well as the overall cohort study set up, which gives prospective cohort studies an advantage over retrospective cohort studies [ 9 ].
Further advantages and disadvantages of prospective cohort studies are discussed below. Euser et al. Others list the following as advantages of prospective cohort studies; first: the exposure has already been measured before the outcome has occurred, which allows for the assessment of temporal sequence [ 28 ].
This allows for the calculation of incidence and the determination of the disease process [ 2 , 12 , 20 , 23 ]. Second: elimination of recall bias, as there is no need for any recollection of information since the data is being collected in a prospective manner [ 7 ]. However, Kip et al.
Third: It allows for the study of exposures were randomization is not practical or ethical [ 12 ]. Fourth: it allows for the study of rare exposures [ 20 ].
Fifth: it allows for the study of multiple outcomes [ 20 , 26 ]. Among the disadvantages of prospective cohort studies is the loss to follow up, which is common among cohort studies. This can ultimately lead to differential loss to follow up among those exposed and unexposed, which in turn can complicate the interpretation of the results [ 2 , 7 , 12 , 18 , 24 ]. Another disadvantage is that they are time consuming if follow up periods are far apart.
This would be resource consuming as well, which would make prospective cohort studies not suitable for the study of outcomes that take long time to develop [ 18 , 20 , 24 , 26 ]. A third disadvantage is that they are expensive to conduct [ 18 , 20 , 30 ]. The third section of this chapter is dedicated to providing examples of prospective cohort studies. As previously described, retrospective cohort studies, also known as historic [ 28 ] or historical [ 24 ] cohorts, use data that has already been collected, such as databases of healthcare records, in order to investigate the association between the exposure and the outcome [ 22 , 24 , 26 , 28 ].
Although the outcome has already occurred, the design of retrospective cohort studies is similar to those of prospective cohort studies [ 22 ]. They also have similar advantages and disadvantages [ 26 , 28 ]. Hess indicates that retrospective studies in general are useful as pilot studies for future prospective studies [ 31 ]. Retrospective cohort studies have advantages and disadvantages. They are time efficient and cheap since the data has been collected previously and is available for scrutiny [ 18 , 20 , 26 ].
Additionally, since the exposure has already been measured before the outcome has occurred, this allows for the assessment of temporal sequence [ 28 ]. However, retrospective cohort studies use information that has been collected in the past for another objective other than the current study [ 18 ], and in some cases, collected for a purpose that is not related to medical research [ 9 ].
Due to this factor, the investigator lacks control over the collection of data [ 24 , 26 , 27 ]. Additionally, the measurement of exposure and outcome might be inconsistent or inaccurate, which can become a source of bias [ 24 , 27 , 28 , 31 , 32 ]. Examples of retrospective cohort studies: High plasma phosphate as a risk factor for decline in renal function and mortality in pre-dialysis patients [ 18 , 33 ].
In this study, Voormolen et al. Assessment of female sex as a risk factor in atrial fibrillation in Sweden: nationwide retrospective cohort study [ 28 , 34 ]. In this study, Friberg et al. Outcomes of care by hospitalists, general Internists, and family physicians [ 35 ]. In this study, Lindenauer et al. Validity is the epidemiological assessment to the lack of systematic error [ 4 , 11 ].
There are two types of validity: internal validity and external validity [ 4 , 11 , 25 ]. Internal validity refers to the inferences made from the study that are related to the same source population [ 4 , 5 , 11 , 25 , 36 ], as to whether or not the study has measured what it had originally planned on measuring [ 25 , 36 ]. For an example, if the exposure caused the observed change in the outcome, then the study would be considered to have high internal validity [ 11 ].
On the other hand, if the observed change in the outcome was caused by a systematic error bias , then the study would be considered to have low internal validity [ 11 ].
Threats or violations to internal validity will be discussed later in this section under bias. External validity refers to the degree to which the study results can be generalized to other populations [ 4 , 5 , 11 , 25 , 36 ]. For example, if the study participants were not representative of the general population, then the study results cannot be generalizable to others [ 12 ]. The highest level of external validity occurs when the results can be generalized to three other domains: other populations, other environments, and other times [ 36 ].
External validity can be improved by using random selection [ 37 ]. It is essential to have internal validity in order to establish external validity; that is the study must have internal validity in the first place in order to have external validity [ 4 , 11 ]. For an example, if the exposure caused the observed change in the outcome, then the results can be generalizable to others.
If the observed change was caused by any other factor, then the results cannot be generalized to others [ 4 , 11 ]. Based on the validity hierarchy, cohort studies are considered to have low internal validity, while the external validity is high [ 11 , 16 ]. Bias is a study systematic error in the design, conduct, or analysis that can be categorized into three main categories: selection bias, information bias, and confounding [ 4 , 25 , 38 ]. Selection bias occurs when the sample chosen for the study is not obtained randomly, so that the sample chosen is no longer representative of the overall population [ 4 , 25 , 38 , 39 ].
This type of bias includes three types: attrition bias, non-respondent bias, and the healthy entrant effect [ 38 ]. Attrition bias, or loss to follow up bias, occurs due to dropouts or death, which can be encountered in studies with long follow up durations prospective [ 23 ].
For example, nonsmokers are more likely to return questionnaires about smoking than smokers are [ 25 ]. The healthy entrant effect or the healthy worker effect occurs when there are differences between those that are exposed and those that are not exposed.
For an example, when comparing working individuals to the general population, as workers are more likely to be healthier than the general population. In order to avoid this type of bias, it is recommended to use two similar groups, such as using two groups of working individuals [ 23 ].
Information bias measurement bias [ 25 ], occurs when the data obtained is being recorded inaccurately [ 4 , 25 , 38 , 39 , 40 ].
This type of bias can be differential nonrandom or nondifferential random as related to the outcome [ 4 , 9 , 23 , 25 ]. The former is dependent on other variables and leads to overestimation or underestimation of any possible association, while the latter is independent from other variables and leads to underestimation of any possible association [ 4 , 9 , 23 ], and if the exposure was dichotomous, this type leads to bias towards the null [ 9 ].
Non differential is more commonly encountered in cohort studies [ 9 ]. Information bias can be reduced by using standardized assessment tools that have been validated [ 9 ].
Information bias is also known as classification bias, observation bias [ 25 ], or misclassification bias [ 23 ]. Cohort studies are usually but not exclusively prospective, the opposite is true for case-control studies. The following notes relate cohort to case-control studies:.
Download a free trial here. Prospective vs. Retrospective Studies Prospective A prospective study watches for outcomes, such as the development of a disease, during the study period and relates this to other factors such as suspected risk or protection factor s. Retrospective A retrospective study looks backwards and examines exposures to suspected risk or protection factors in relation to an outcome that is established at the start of the study.
Case-Control studies Case-Control studies are usually but not exclusively retrospective, the opposite is true for cohort studies.
Cohort studies Cohort studies are usually but not exclusively prospective, the opposite is true for case-control studies. Data harmonisation involves retrospectively adjusting data collected by different surveys to make it possible to compare the data that was collected. This enables researchers to make comparisons both within and across studies. Repeating the same longitudinal analysis across a number of studies allows researchers to test whether results are consistent across studies, or differ in response to changing social conditions.
Data imputation is a technique for replacing missing data with an alternative estimate. There are a number of different approaches, including mean substitution and model-based multivariate approaches. Data linkage simply means connecting two or more sources of administrative, educational, geographic, health or survey data relating to the same individual for research and statistical purposes. For example, linking housing or income data to exam results data could be used to investigate the impact of socioeconomic factors on educational outcomes.
Data protection refers to the broad suite of rules governing the handling and access of information about people. Data protection principles include confidentiality of responses, informed consent of participants and security of data access.
Data structure refers to the way in which data are organised and formatting in advance of data analysis. In analysis, the dependent variable is the variable you expect to change in response to different values of your independent or predictor variables.
A derived variable is a variable that is calculated from the values of other variables and not asked directly of the participants.
It can involve a mathematical calculation e. Diaries are a data collection instrument that is particularly useful in recording information about time use or other regular activity, such as food intake.
They have the benefit of collecting data from participants as and when an activity occurs. As such, they can minimise recall bias and provide a more accurate record of activities than a retrospective interview. Dissemination is the process of sharing information — particularly research findings — to other researchers, stakeholders, policy makers, and practitioners through various avenues and channels, including online, written publications and events.
Dissemination is a planned process that involves consideration of target audiences in ways that will facilitate research uptake in decision-making processes and practice. Dummy variables , also called indicator variables , are sets of dichotomous two-category variables we create to enable subgroup comparisons when we are analysing a categorical variable with three or more categories. Empirical data refers to data collected through observation or experimentation.
Analysis of empirical data can provide evidence for how a theory or assumption works in practice. In metadata management, fields are the elements of a database which describes the attributes of items of data. General ability is a term used to describe cognitive ability, and is sometimes used as a proxy for intelligent quotient IQ scores.
Growth curve modelling is used to analyse trajectories of longitudinal change over time allowing us to model the way participants change over time, and then to explore what characteristics or circumstances influence these patterns of longitudinal change. Hazard rate refers to the probability that an event of interest occurs at a given time point, given that it has not occurred before.
Health assessments refers to the assessments carried out on research participants in relation to their physical characteristics or function. These can include measurements of height and weight, blood pressure or lung function. Heterogeneity is a term that refers to differences, most commonly differences in characteristics between study participants or samples.
It is the opposite of homogeneity, which is the term used when participants share the same characteristics. Where there are differences between study designs, this is sometimes referred to as methodological heterogeneity.
Both participant or methodological differences can cause divergences between the findings of individual studies and if these are greater than chance alone, we call this statistical heterogeneity. See also: unobserved heterogeneity. Household panel surveys collect information about the whole household at each wave of data collection, to allow individuals to be viewed in the context of their overall household. To remain representative of the population of households as a whole, studies will typically have rules governing how new entrants to the household are added to the study.
As a way of encouraging participants to take part in research, they may be offered an incentive or a reward. These may be monetary or, more commonly, non-monetary vouchers or tokens.
Incentives are advertised beforehand and can act as an aid to recruitment; rewards are a token of gratitude to the participants for giving their time. In analysis, an independent variable is any factor that may be associated with an outcome or dependent variable. For example, the number of hours a student spends on revision may influence their test result.
A key principle of research ethics , informed consent refers to the process of providing full details of the research to participants so that they are sufficiently able to choose whether or not to consent to taking part. To put it another way, it is a measure of how thin or fat the lower and upper ends of a distribution are. It centres on the individual and emphasises the changing social and contextual processes that influence their life over time. Many longitudinal studies focus upon individuals, but some look at whole households or organisations.
Metadata refers to data about data, which provides the contextual information that allows you to interpret what data mean. Missing data refers to values that are missing and do not appear in a dataset. This may be due to item non-response, participant drop-out or attrition or, in longitudinal studies , some data e.
Large amounts of missing data can be a problem and lead researchers to make erroneous inferences from their analysis. There are several ways to deal with the issue of missing data, from casewise deletion to complex multiple imputation models. Multi-level modelling refers to statistical techniques used to analyse data that is structured in a hierarchical or nested way.
For example. Multi-level models can account for variability at both the individual level and the group e. Non-response bias is a type of bias introduced when those who participate in a study differ to those who do not in a way that is not random for example, if attrition rates are particularly high among certain sub-groups. Non-random attrition over time can mean that the sample no longer remains representative of the original population being studied. Two approaches are typically adopted to deal with this type of missing data : weighting survey responses to re-balance the sample , and imputing values for the missing information.
Panel studies follow the same individuals over time. They vary considerably in scope and scale. Examples include online opinion panels and short-term studies whereby people are followed up once or twice after an initial interview.
Peer review is a method of quality control in the process of academic publishing, whereby research is appraised usually anonymously by one or more independent academic with expertise in the subject.
Period effects relate to changes in an outcome associated with living during a particular time, regardless of age or cohort membership e. Piloting is the process of testing your research instruments and procedures to identify potential problems or issues before implementing them in the full study.
A pilot study is usually conducted on a small subset of eligible participants who are encouraged to provide feedback on the length, comprehensibility and format of the process and to highlight any other potential issues. Population refers to all the people of interest to the study and to whom the findings will be able to be generalized e.
Owing to the size of the population, a study will usually select a sample from which to make inferences. See also: sample , representiveness. A percentile is a measure that allows us to explore the distribution of data on a variable. It denotes the percentage of individuals or observations that fall below a specified value on a variable.
The value that splits the number of observations evenly, i. Primary research refers to original research undertaken by researchers collecting new data. It has the benefit that researchers can design the study to answer specific questions and hypotheses rather than relying on data collected for similar but not necessarily identical purposes. See also: secondary research.
In prospective studies, individuals are followed over time and data about them is collected as their characteristics or circumstances change. Qualitative data are non-numeric — typically textual, audio or visual.
Qualitative data are collected through interviews, focus groups or participant observation. Qualitative data are often analysed thematically to identify patterns of behaviour and attitudes that may be highly context-specific.
Quantitative data can be counted, measured and expressed numerically. They are collected through measurement or by administering structured questionnaires.
Quantitative data can be analysed using statistical techniques to test hypotheses and make inferences to a population.
Questionnaires are research instruments used to elicit information from participants in a structured way. They might be administered by an interviewer either face-to-face or over the phone , or completed by the participants on their own either online or using a paper questionnaire.
Questions can cover a wide range of topics and often include previously-validated instruments and scales e. Recall error or bias describes the errors that can occur when study participants are asked to recall events or experiences from the past. It can take a number of forms — participants might completely forget something happened, or misremember aspects of it, such as when it happened, how long it lasted, or other details.
Certain questions are more susceptible to recall bias than others. For example, it is usually easy for a person to accurately recall the date they got married, but it is much harder to accurately recall how much they earned in a particular job, or how their mood at a particular time. Record linkage studies involve linking together administrative records for example, benefit receipts or census records for the same individuals over time. A reference group is a category on a categorical variable to which we compare other values.
It is a term that is commonly used in the context of regression analyses in which categorical variables are being modelled. Repeated measures are measurements of the same variable at multiple time points on the same participants, allowing researchers to study change over time. Representativeness is the extent to which a sample is representative of the population from which it is selected. Representative samples can be achieved through, for example, random sampling, systematic sampling, stratified sampling or cluster sampling.
Research ethics relates to the fundamental codes of practice associated with conducting research. Academic research proposals need be approved by an ethics committee before any actual research either primary or secondary can begin. Research impact is the demonstrable contribution that research makes to society and the economy that can be realised through engagement with other researchers and academics, policy makers, stakeholders and members of the general public.
It includes influencing policy development, improving practice or service provision, or advancing skills and techniques. Residuals are the difference between your observed values the constant and predictors in the model and expected values the error , i.
Respondent burden is a catch all phrase that describes the perceived burden faced by participants as a result of their being involved in a study.
It could include time spent taking part in the interview and inconvenience this may cause, as well as any difficulties faced as a result of the content of the interview. Response rate refers to the proportion of participants in the target sample who completed the survey. Longitudinal surveys are designed with the expectation that response rates will decline over time so will typically seek to recruit a large initial sample in order to compensate for likely attrition of participants.
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