Table Of Content
Indeed, RCTs typically follow patients for only a small fraction of the time that the drug would be used in clinical practice, especially when the medications are for chronic diseases. An additional use of case-control studies for therapeutic effectiveness is to investigate variables or exposures that can guide clinicians in choosing the preferred neurosurgical treatment when more than one therapeutic option exists. An example in the neurosurgical literature includes the study reported by Lennarson et al27 described above utilizing a nested case-control study that evaluated age as a risk factor for nonfusion with halo-vest immobilization for treatment of type II odontoid fractures (Figure 3). The premise for this study was based on existing class III evidence showing no clear difference between the surgical interventions versus halo-vest immobilization for treatment of these fractures. The results of this study provided strong class II medical evidence that age is a significant risk factor for nonfusion with halo-vest immobilization.27 Based on this finding, age can be used to define a treatment algorithm for choosing surgical intervention versus halo-vest immobilization. Similar case-control study designs could be used for other conditions with apparently equivocal treatment options and the findings could help guide the determination of appropriate neurosurgical treatment in subpopulations of patients.
Antipsychotic Reexposure and Recurrent Pneumonia in Schizophrenia: A Nested Case-Control Study - The Journal of Clinical Psychiatry
Antipsychotic Reexposure and Recurrent Pneumonia in Schizophrenia: A Nested Case-Control Study.
Posted: Sat, 21 Oct 2023 12:26:24 GMT [source]
Analysis; Odds Ratios and Confidence Intervals
These additional complexities and designs are beyond the scope of this paper and represent an interesting direction for future research. We establish how, and under which conditions, various causal estimands relating to intention-to-treat or per-protocol effects can be identified based on the data that are collected under popular sampling schemes (case-base, survivor, and risk-set sampling, with or without matching). We present a concise summary of our identification results that link the estimands to the (distribution of the) available data and articulate under which conditions these links hold. Similarly, the researcher must recognize the potential for failing to identify confounding variables or exposures, introducing the possibility of confounding bias, which occurs when a variable that is not being accounted for that has a relationship with both the exposure and outcome. This can cause us to accidentally be studying something we are not accounting for but that may be systematically different between the groups. There are two reasons why, in case-control studies, large samples are desirable, and whymany controls may be matched to a single case.
Control group selection
Then through further exploration, often using other study types (such as cohort studies or randomized clinical studies) the researcher may be able to develop further support for the evidence of the possible association between the exposure and the outcome. The most commonly cited disadvantage in case-control studies is the potential for recall bias.[2] Recall bias in a case-control study is the increased likelihood that those with the outcome will recall and report exposures compared to those without the outcome. The first is to test a preliminary hypothesis to determine if a lengthy and costly RCT is justified. Additionally, this study design may be the only practical option for studying the treatment of a rare disease where enrollment would be insufficient for an RCT, or where randomization would be unethical.
Share this article
A case-control study is an experimental design that compares a group of participants possessing a condition of interest to a very similar group lacking that condition. Here, the participants possessing the attribute of study, such as a disease, are called the ‘case’, and those without it are the ‘control’. Beyond color correction, the panel offers a range of transport and grading controls conveniently positioned around the edges.
Study Designs in the Health Sciences
Dietary patterns associated with colorectal cancer risk in the Malaysian population: a case–control study with ... - BMC Public Health
Dietary patterns associated with colorectal cancer risk in the Malaysian population: a case–control study with ....
Posted: Wed, 19 Jul 2023 07:00:00 GMT [source]
While most observational studies are qualitative in nature, case-control studies can also be quantitative, and they often are in healthcare settings. Case-control studies can be used for both exploratory and explanatory research, and they are a good choice for studying research topics like disease exposure and health outcomes. Complete and accurate measurement of the outcome of interest is essential to ensure the validity of study results.
In cross-sectional studies, researchers are simply examining a group of participants and depicting what already exists in the population. Researchers will first identify the two groups, and then look back in time to investigate which subjects in each group were exposed to the condition. Case control studies are also known as "retrospective studies" and "case-referent studies." An important technique for adding power to a study is to enroll more than one control for every case.
This is important, for the statistical analyses appropriate for supporting the conclusion from such studies, the amount of bias control possible, and the ability to estimates odds and relative risk are different depending on study design. The neurosurgeon hoping to appropriately incorporate the results of the studies she reads into practice needs to have at least a fundamental understanding of these issues related to study design to avoid placing unjustified confidence in the conclusions of a study done with insufficient rigor. A frequent source of controls is patients from the same hospital who do not have the outcome. However, hospitalised patients often do not represent the general population; they are likely to suffer health problems and they have access to the health care system. An alternative may be to enroll community controls, people from the same neighborhoods as the cases. Care must be taken with sampling to ensure that the controls represent a ‘normal’ risk profile.
However, cohort studies with long observation periods may be more susceptible to losses to follow-up and to inaccurate measurement of exposures and outcomes. Large numbers of patients may be required to precisely estimate meaningful drug–outcome associations, especially for rare outcomes or outcomes that take a long time to occur. Observational studies are methodologically difficult, susceptible to bias and confounding, and difficult to interpret, given the many types of bias potentially at play. For these reasons, observational studies are limited to studying drug–outcome associations and cannot be used to measure the causal effects of drugs. Many of these techniques can account for multiple potential confounders simultaneously.
Moreover, while the estimand may be identifiable, there need not exist an estimator with the desired properties (see e.g. [5]). Here, our focus is on identification, so that the purely statistical issues of the next step in causal inference, estimation, can be momentarily put aside. Thus, the outcome is measured after exposure in retrospective cohort studies, whereas the outcome is measured before the exposure in case-control studies. Case-control studies may prove an association between exposures and outcomes, but they can not demonstrate causation.
A comprehensive review of these techniques is beyond the scope of this article, but such reviews may be found elsewhere,25,31–33 Bias and confounding result in spurious drug–outcome associations and are introduced at both the design and analysis stages. Despite the essential difference in these types of scientific inquiry, similar clinical questions can be answered by a well-designed case-control study as illustrated above. This utility gives the case-control study design a unique advantage when studying clinical outcomes in populations affected by rare conditions and diseases. We need well-designed and rigorous cohort and case– control studies because their findings provide knowledge complementary to that garnered from RCTs (Table 1). The design properties of RCTs maximize their ability to estimate the potential causal effects of drugs under ideal circumstances and thereby to estimate the efficacy of those drugs. However, many RCTs involve a relatively limited number of highly selected patients and a limited duration.
In case-control studies, participants are recruited on the basis of disease status. Thus, some of participants have the outcome of interest (referred to as cases), whereas others do not have the outcome of interest (referred to as controls). Case-control studies are less expensive and quicker to conduct (compared with prospective cohort studies at least).
In addition, those with the outcome are more likely to recall and report exposures more clearly than those without the outcome. A case-control study looks at people who already have a certain condition (cases) and people who don’t (controls). By comparing these two groups, researchers try to figure out what might have caused the condition.
An odds ratio of less than one implies the disease is less likely with exposure and thus the exposure may be protective. For example, a patient with a prior heart attack taking a daily aspirin has a decreased odds of having another heart attack (odds ratio less than one). An odds ratio of one implies there is no relation between the exposure and the disease process. Case-control studies may prove an association but they do not demonstrate causation.
No comments:
Post a Comment