difference between case control cohort and cross sectional studies pdf Tuesday, May 4, 2021 12:37:07 AM

Difference Between Case Control Cohort And Cross Sectional Studies Pdf

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As noted earlier, descriptive epidemiology can identify patterns among cases and in populations by time, place and person. From these observations, epidemiologists develop hypotheses about the causes of these patterns and about the factors that increase risk of disease. In other words, epidemiologists can use descriptive epidemiology to generate hypotheses, but only rarely to test those hypotheses.

Case–control study

Metrics details. We propose a conceptualization of cohort studies in systematic reviews of comparative studies. The main aim of this conceptualization is to clarify the distinction between cohort studies and case series. We discuss the potential impact of the proposed conceptualization on the body of evidence and workload. All studies with exposure-based sampling gather multiple exposures with at least two different exposures or levels of exposure and enable calculation of relative risks that should be considered cohort studies in systematic reviews, including non-randomized studies.

Instead, all studies for which sufficient data are available for reanalysis to compare different exposures e. There are possibly large numbers of studies without a comparison for the exposure of interest but that do provide the necessary data to calculate effect measures for a comparison. Consequently, more studies could be included in a systematic review. Therefore, on the one hand, the outlined approach can increase the confidence in effect estimates and the strengths of conclusions.

On the other hand, the workload would increase e. Peer Review reports. Systematic reviews that include non-randomized studies often consider different observational study designs [ 1 ]. However, the distinction between different non-randomized study designs is difficult. One key design feature to classify observational study designs is to distinguish comparative from non-comparative studies [ 2 , 3 ].

The lack of a comparison group is of particular importance for distinguishing cohort studies from case series because in many definitions, they share a main design feature of having a follow-up period examining the exposed individuals over time [ 2 , 3 ].

The only difference between cohort studies and case series in many definitions is that cohort studies compare different groups i. Table 1 shows an example definition [ 3 ]. The problem with this definition is that vague terms, such as comparison and examination of association, might be interpreted as an analytic comparison of at least two exposures i.

For example, imagine a study of 20 consecutive patients with a certain disease that can be treated in two different ways. A study that divides the 20 patients into two groups according to the treatment received and compares the outcomes of these groups e. A sample of this study type is illustrated in Fig. An example of this study type is illustrated in Fig.

In the medical literature, the data on exposure and outcomes are usually provided in either running text or spreadsheet formats [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. A good example is the study by Wong et al.

In this study, information on placental invasion exposure and blood loss outcome is separately provided for 40 pregnant women in a table. The study by Cheng et al. These examples illustrate that distinguishing between cohort studies and case series is difficult. Vague definitions are probably the reason for the common confusion between study designs. Many systematic reviews of non-randomized studies included cohort studies but excluded case series see examples in [ 23 , 24 , 25 , 26 , 27 , 28 ].

Therefore, the unclear distinction between case series and cohort studies can result in inconsistent study selection and unjustified exclusions from a systematic review. The risk of misclassification is particularly high because study authors also often mislabel their study or studies are not classified by their authors at all see examples in [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. The main objective of this conceptualization is to clarify the distinction between cohort studies and case series in systematic reviews, including non-randomized comparative studies.

In the following report, we propose a conceptualization for cohort studies and case series e. Our proposal is based on a recent conceptualization of cohort studies and case series by Dekkers et al. The main feature of this conceptualization is that it is exclusively based on inherent design features and is not affected by the analysis.

Dekkers et al. The definition of cohort studies according to Dekkers et al. This idea can be easily extended to studies with more than one exposure. In this case, all studies with exposure-based sampling gathering multiple exposures i. The sampling is based on exposure, and there are different groups. Consequently, relative risks can be calculated [ 29 ].

Cohort study deduced from Dekkers et al. In short, all studies that enable calculation of a relative risk to quantify a difference in outcomes between different groups should be considered cohort studies.

According to Dekkers et al. Consequently, no absolute risk and also no relative effect measures for an outcome can be calculated in a case series.

Note that sampling in a case series does not need to be consecutive. Consecutiveness would increase the quality of the case series, but a non-consecutive series is also a case series [ 29 ].

Case series Deckers et al. In short, for a case series, there are no absolute risks, and also, no risk ratios can be calculated. Consequently, a case series cannot be comparative. The definition of a case series by Dekkers et al.

It is noteworthy that the conceptualization also ensures a clear distinction of case series from other study designs that apply outcome-based sampling. Case series, case-control studies including case-time-control , and self-controlled case-control designs e. Case series have no control at all because only patients with a certain manifestation of outcomes are sampled e. In contrast, all case-control designs as well as self-controlled case-control designs have a control group.

In case-control studies, the control group constitutes individuals with another manifestation of the outcome e. This outcome can be considered as two case series i. Self-controlled case-control studies are characterized by an intra-individual comparison each individual is their own control [ 30 ]. Information is also sampled when patients are not exposed.

Therefore, case-control designs as well as self-controlled case-control studies enable the calculation of risk ratios. This approach is not possible for a case series. Above, we illustrated that by using a vague definition, the classification of a study design might be influenced by the preparation and analysis of the study data. The proposed conceptualization is exclusively based on the inherent design features e.

After considering the example studies again using the proposed conceptualization, all studies would be classified as cohort studies because the relative risk can be calculated.

This outcome becomes clear looking at Table 2 and Table 3. If the patients in Table 3 are rearranged according the exposure and the data are reanalysed i. In the study by Wong et al. In this study, the data on gestational age are also provided in the table. Therefore, it is even possible to adjust the results for gestational age e. The proposed conceptualization is exclusively based on inherent study design features; therefore, there is less room for misinterpretation compared to existing conceptualizations because analysis features, presentation of data and labelling of the study are not determined.

Thus, the conceptualization ensures consistent study selection for systematic reviews. The prerequisite of an analytical comparison in the publication can lead to the unjustified exclusion of relevant studies from a systematic review.

Study 1 would likely be included, and Study 2 would be excluded from the systematic review. The only differences between Study 1 and Study 2 are the analysis and preparation of data.

If the data source e. Thus, the inclusion of Study 1 and the exclusion of Study 2 are contradictory to the requirement that systematic reviews identify all available evidence [ 31 ]. Considering that more studies would be eligible for inclusion and that the hierarchical paradigm of the levels of evidence is not valid per se, the proposed conceptualization can potentially enrich bodies of evidence and increase confidence in effect estimates.

The additional inclusion of all studies that enable calculating relative risk for the comparison of interest might impact the workload of systematic reviews. There might be a considerable number of studies not performing a comparison already but that provide sufficient data for reanalysis. Usually the electronic search strategy for systematic reviews of non-randomized studies is not limited to certain study types because there are no sensitive search filters available yet [ 32 ].

Therefore, the search results do not usually include cohort studies as discussed above. However, in many abstracts it would be not directly clear if sufficient data for re-calculations are reported in the full text article e.

Consequently, many additional potentially relevant full-text studies have to be screened. Additionally, studies often assess various exposures e. Considering the large amount of wrongly labelled studies, this approach can lead to additional screening effort [ 22 ].

As a result, more studies would be included in systematic reviews. All articles that provide potentially relevant data would have to be assessed in detail to decide whether reanalysis is feasible. For these data extractions, a risk of bias assessment would have to be performed. Challenges in the risk of bias assessment would arise because most assessment tools are constructed to assess a predefined control group [ 33 ]. For example, items regarding the adequacy of analysis e.

Effect measures must be calculated e. Moreover, advanced biometrical expertise would be necessary to judge the feasibility i. In the medical literature, it is likely that more retrospective mislabelled cohort studies comparison planned after data collection based on routinely collected data e. Thus, it can be assumed that the wrongly labelled studies tend to have lower methodological quality than studies that already include a comparison.

This aspect should be considered in decisions about including studies that must be reanalysed. In research areas in which randomized controlled trials or large planned prospective and well-conducted cohort studies can be expected e.

Consequently, in these areas, the additional effort might not be worthwhile. Again, the conceptualization is particularly promising in research areas in which evidence is sparse because studies are difficult to conduct or populations are small or the event rates are low.

In these areas, there might be no well-conducted studies at all [ 34 , 35 ].

Lesson 1: Introduction to Epidemiology

A case—control study also known as case—referent study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. A case—control study produces only an odds ratio, which is an inferior measure of strength of association compared to relative risk. The case—control is a type of epidemiological observational study. An observational study is a study in which subjects are not randomized to the exposed or unexposed groups, rather the subjects are observed in order to determine both their exposure and their outcome status and the exposure status is thus not determined by the researcher. Porta's Dictionary of Epidemiology defines the case—control study as: an observational epidemiological study of persons with the disease or another outcome variable of interest and a suitable control group of persons without the disease comparison group, reference group. For example, in a study trying to show that people who smoke the attribute are more likely to be diagnosed with lung cancer the outcome , the cases would be persons with lung cancer, the controls would be persons without lung cancer not necessarily healthy , and some of each group would be smokers. If a larger proportion of the cases smoke than the controls, that suggests, but does not conclusively show, that the hypothesis is valid.

Metrics details. We propose a conceptualization of cohort studies in systematic reviews of comparative studies. The main aim of this conceptualization is to clarify the distinction between cohort studies and case series. We discuss the potential impact of the proposed conceptualization on the body of evidence and workload. All studies with exposure-based sampling gather multiple exposures with at least two different exposures or levels of exposure and enable calculation of relative risks that should be considered cohort studies in systematic reviews, including non-randomized studies.

Case-control and Cohort studies: A brief overview

A retrospective cohort study , also called a historic cohort study , is a longitudinal cohort study used in medical and psychological research. A cohort of individuals that share a common exposure factor is compared with another group of equivalent individuals not exposed to that factor, to determine the factor's influence on the incidence of a condition such as disease or death. Retrospective cohort studies have existed for approximately as long as prospective cohort studies. The retrospective cohort study compares groups of individuals who are alike in many ways but differ by a certain characteristic for example, female nurses who smoke and ones who do not smoke in terms of a particular outcome such as lung cancer.

Retrospective cohort study

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.

Lesson 1: Introduction to Epidemiology

Posted on 6th December by Saul Crandon. Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence. These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies 1. Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

The cohort study design identifies a people exposed to a particular factor and a comparison group that was not exposed to that factor and measures and compares the incidence of disease in the two groups. A higher incidence of disease in the exposed group suggests an association between that factor and the disease outcome. This study design is generally a good choice when dealing with an outbreak in a relatively small, well-defined source population, particularly if the disease being studied was fairly frequent. The case-control design uses a different sampling strategy in which the investigators identify a group of individuals who had developed the disease the cases and a comparison of individuals who did not have the disease of interest. The cases and controls are then compared with respect to the frequency of one or more past exposures. If the cases have a substantially higher odds of exposure to a particular factor compared to the control subjects, it suggests an association. This strategy is a better choice when the source population is large and ill-defined, and it is particularly useful when the disease outcome was uncommon.

This short article gives a brief guide to the different study types and a comparison of the advantages and disadvantages. Figure 1 shows the tree of possible designs, branching into subgroups of study designs by whether the studies are descriptive or analytic and by whether the analytic studies are experimental or observational. The list is not completely exhaustive but covers most basics designs. Figure: Tree of different types of studies Q1, 2, and 3 refer to the three questions below. Our first distinction is whether the study is analytic or non-analytic. Descriptive studies include case reports, case-series, qualitative studies and surveys cross-sectional studies, which measure the frequency of several factors, and hence the size of the problem.

Case-control and Cohort studies: A brief overview

Cohort and case-control methodologies are the main tools for analytical epidemiological research. Other important types of epidemiological studies mainly for generating hypotheses include cross-sectional and ecological, or correlation studies. The conclusions that can be drawn from findings of these types of studies are, however, much weaker compared to those of cohort and case-control studies. This is not to say that findings from cohort and case-control studies always reflect true associations which can be universally generalized.

Although the relative risk from a prospective cohort study is numerically approximate to the odds ratio from a case-control study for a low-probability event, a definite relationship between case-control and cohort studies cannot be confirmed. In this study, we established a different model to determine the relationship between case-control and cohort studies. Two analysis models the cross-sectional model and multiple pathogenic factor model were established. Incidences in both the exposure group and the nonexposure group in a cohort study were compared with the frequency of the observed factor in each group diseased and nondiseased in a case-control study. The relationship between the results of a case-control study and a cohort study is as follows: ; , where Pe and Pn represent the incidence in the exposed group and nonexposed group, respectively, from the cohort study, while Pd and Pc represent the observed frequencies in the disease group and the control group, respectively, for the case-control study; finally, m represents the incidence in the total population.

Cross-sectional study designs are used when studying one or more variables within a given population at one point in time. Such studies are useful for establishing associations rather than causality and for determining prevalence, rather than incidence. In cohort studies, a group of people within a population is followed over a specified period of time to track who experiences or develops the same significant life event or treatment. This type of design can be used "to study incidence, causes, and prognosis. Because they measure events in chronological order they can be used to distinguish between cause and effect.

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Brett S. 11.05.2021 at 23:02

are used to determine prevalence. They are relatively quick and easy but do not permit.