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Because these studies use already existing data and do not require any follow-up with subjects, they tend to be quicker and cheaper than other types of research. The “cases” are the individuals with the disease or condition under study, and the “controls” are similar individuals without the disease or condition of interest. The method of assignment of individuals to study and control groups in observational studies when the investigator does not intervene to perform the assignment. Each case is matched individually with a control according to certain characteristics such as age and gender.
Research Design: Case-Control Studies
Assessment of post-COVID-19 fatigue among female survivors 2 years after hospital discharge: a nested case–control ... - BMC Public Health
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When subjective outcome data (e.g., diagnosis of pneumonia) are being collected during the study period, exposure status should be blinded for the outcome assessors and adjudicators, to prevent responder bias. When previously collected data (i.e., secondary data) are being used, investigators should ideally use outcome definitions that have been validated in previous studies. For example, Hux and others19 validated definitions of diabetes by comparing International Classification of Diseases codes obtained from administrative health care databases in Ontario with diagnostic data from primary care charts. To achieve these advantages, the design characteristics of the case control study must be rigorously applied. The authors outline those characteristics so that readers can avoid misinterpreting cross-sectional studies, case series, and less rigorous reports as case control studies.
Risk Factors and Sampling
One also does not have to wait for the development of the outcome – it has already occurred, thus shortening the study process. When designing a case-control study, the researcher must find an appropriate control group. Ideally, the case group (those with the outcome) and the control group (those without the outcome) will have almost the same characteristics, such as age, gender, overall health status, and other factors. If, for example, our cases of Kaposi's sarcoma came from across the country but our controls were only chosen from a small community in northern latitudes where people rarely go outside or get sunburns, asking about sunburn may not be a valid exposure to investigate.
Case-control studies without matching
One of the most important things each party provides is helping identify correct controls for the cases. Matching the controls across a spectrum of factors outside of the elements of interest take input from nurses, pharmacists, social workers, physicians, demographers, and more. Failure for adequate selection of controls can lead to invalid study conclusions and invalidate the entire study. The major method for analyzing results in case-control studies is the odds ratio (OR). The odds ratio is the odds of having a disease (or outcome) with the exposure versus the odds of having the disease without the exposure. The most straightforward way to calculate the odds ratio is with a 2 by 2 table divided by exposure and disease status (see below).
Controls
It is a design that should be used more frequently in neurosurgical clinical research. Observational study designs include case-control, cohort, and cross-sectional studies, and each study is distinct with a unique role in clinical research. Case-control studies can be a robust option in neurosurgical research compared to other observational study designs. A better understanding of the differences in design type will facilitate better study designs and further improve the quality of reporting. In our review, we explored those differences and how the case-control study design can contribute to the neurosurgical literature. While both case-control and cohort studies are longitudinal by design, cross-sectional studies, often mislabeled as case-control studies, reflect a single period in time (Figure 1).
LEVEL OF EVIDENCE PROVIDED BY CASE-CONTROL STUDIES
In the hierarchy of study designs used to produce evidence-based guidelines, the role of case-control studies has varied. The level of evidence designation for study designs is a hierarchical classification reflective of the inherent potential for bias that may skew the results that may be introduced by the study design. Because most neurosurgical conditions are rare in the general population and may have long latency periods, this can often be an ideal study choice for neurosurgical clinical research. In a case-control study, on the other hand, the cases, those patients with the outcome of interest, are located and matched, sometimes quite easily, with similar patients, but without the outcome of interest.
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As is shown in Supplementary Appendix B, Theorem 3, the above odds ratio is identified by the ratio of the baseline exposure odds given L0 among the cases versus controls, provided the key identifiability conditions of consistency, baseline conditional exchangeability, and positivity are met. To facilitate understanding, it is useful to consider every case-control study as being “nested” within a cohort study. A case-control study could be considered as a cohort study with missingness governed by the control sampling scheme. Therefore, when the observed data distribution of a case-control study is compatible with exactly one value of a given estimand, then so is the available or observed data distribution of the underlying cohort study. In other words, identifiability of an estimand with a case-control study implies identifiability of the estimand with the cohort study within which it is nested (conceptually). In this paper, the focus is on sets of conditions or assumptions that are sufficient for identifiability in case-control studies.
Case-control studies with matching
Only the case-control pairs (A0,A′) with discordant exposure values (i.e., (1,0) or (0,1)) are used. Under the stated sampling schemes and assumptions, the respective estimands are identified by the ratio of discordant pairs. The counterfactual framework offers a language rich enough to articulate a wide variety of causal claims that can be expressed as what-if statements [1]. Another, albeit closely related, approach to causal inference is target trial emulation, an explicit effort to mitigate departures from a study (the ‘target trial’) that, if carried out, would enable one to readily answer the causal what-if question of interest [2]. While it may be too impractical or unethical to implement, making explicit what a target trial looks like has particular value in communicating the inferential goal and offers a reference against which to compare studies that have been or are to be conducted.
An odds ratio is the ratio of the odds of an exposure in the case group to the odds of an exposure in the control group. A confidence interval that includes 1.0 means that the association between the exposure and outcome could have been found by chance alone and that the association is not statistically significant. Case-control studies cannot provide any information about the incidence or prevalence of a disease because no measurements are made in a population based sample.
When the subjects are enrolled in their respective groups, the outcome of each subject is already known by the investigator. This, and not the fact that the investigator usually makes use of previously collected data, is what makes case-control studies ‘retrospective’. Another important aspect of a case-control study is that we should measure the exposure similarly in cases and controls. For instance, if we design a research protocol to study the association between metabolic syndrome (exposure) and psoriasis (outcome), we should ensure that we use the same criteria (clinically and biochemically) for evaluating metabolic syndrome in cases and controls. If we use different criteria to measure the metabolic syndrome, then it may cause information bias. In a case-control study, participants are selected for the study based on their outcome status.
Retrospective cohort studies use existing secondary research data, such as medical records or databases, to identify a group of people with a common exposure or risk factor and to observe their outcomes over time. Case-control studies conduct primary research, comparing a group of participants possessing a condition of interest to a very similar group lacking that condition in real time. Once cases and controls are selected, we can start to derive inverse probability weights W according to Eq. We then compute the odds of baseline exposure among cases in the pseudopopulation that is obtained by weighting everyone by W and the odds of baseline exposure among controls weighted by W multiplied by the number of times the individual was selected as a control.
They look into the past to find clues, like habits or experiences, that are different between the two groups. This study would be retrospective in that the former lifeguards would be asked to recall which type of sunscreen they used on their face and approximately how often. This could be either a matched or unmatched study, but efforts would need to be made to ensure that the former lifeguards are of the same average age, and lifeguarded for a similar number of seasons and amount of time per season. The odds ratio tells us how strongly the exposure is related to the disease state. An odds ratio of greater than one implies the disease is more likely with exposure.
Consider a case-control study intended to establish an association between the use of traditional eye medicines (TEM) and corneal ulcers. TEM might cause corneal ulcers but it is also possible that the presence of a corneal ulcer leads some people to use TEM. The temporal relationship between the supposed cause and effect cannot be determined by a case-control study. Nonetheless, matching may be useful to control for certain types of confounders. For instance, environment variables may be accounted for by matching controls for neighbourhood or area of residence.
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