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Abstract Purpose Observational studies using electronic administrative healthcare databases are often used to estimate the effects of treatments and exposures. Traditionally, a cohort design has been used to estimate these effects, but increasingly, studies are using a nested case—control NCC design.
The relative statistical efficiency of these two designs has not been examined in detail. Methods We used Monte Carlo simulations to compare these two designs in terms of the bias and precision of effect estimates.
We examined three different settings: A treatment occurred at baseline, and there was a single outcome of interest; B treatment was time varying, and there was a single outcome; and C treatment occurred at baseline, and there was a secondary event that competed with the primary event of interest.
Results In Setting A, bias was similar between designs, but the cohort design was more precise and had a lower MSE in all scenarios. In both Settings B and C, the NCC design tended to result in estimates with greater bias compared with the cohort design.
Conclusions We conclude that in a range of settings and scenarios, the cohort design is superior in terms of precision and MSE. Advantages to the use of administrative healthcare databases include comprehensive coverage of entire populations, relatively low cost for the acquisition of data on outcomes and covariates, and the ability to examine the effects of treatments and interventions as they are applied outside of the tightly controlled confines of randomized controlled trials.
The traditional approach to the analysis of these large observational datasets is the retrospective cohort design. The treatment status of each subject is determined at the time of cohort entry or at some observable time subsequent to cohort entry. Subjects are followed over time for the occurrence of the outcome of interest.
The incidence of the outcome is then compared between those who were treated and those who were untreated using the incidence rate ratio. In observational studies, treatment assignment is not at random but is often influenced by subject characteristics.
There are often systematic differences in baseline characteristics between treated and untreated subjects. Therefore, statistical methods must be used to reduce the bias in the estimate of association. For a cohort design with time-to-event data, this is often accomplished using the Cox proportional hazards model.
There has been a recent increase in the use of the nested case—control NCC design in pharmacoepidemiological studies.
This efficiency arises from the fact that, whereas in a cohort study, data on covariates must be collected from all subjects, in a case—control design, data on covariates are required from all cases, but from only a sample of those who do not experience the outcome i.
This is not relevant in studies using administrative or other secondary data where the marginal cost of data collection for covariates is close to zero.
More recently, some authors have suggested that another form of efficiency relates to computational efficiency, in particular, where there may be some time-varying element to the treatment.
Because identifying a well-defined cohort is the first step when using either a cohort or NCC design, it is possible to use either design to estimate treatment effects in the same set of subjects. Although both designs, given specific conditions, can result in unbiased treatment effects when the research question relates to a relatively simple treatment—outcome relationship, it is less clear how these designs compare when more complex treatment—outcome relationships are of interest.
Understanding the implications of one design over the other is required for investigators to make informed decisions. One way to judge the comparative quality of the estimates of treatment effect produced by the two designs is the bias and precision of these estimates.
The objective of the current study was to compare estimates of treatment effect made from a cohort design with those from an NCC design in terms of bias and precision. We used a series of Monte Carlo simulations to examine these issues in three different settings that describe important treatment—outcome relationships in pharmacoepidemiology: Setting B introduces a variation in the definition of treatment, with treatment status being allowed to vary over time.
However, there is still only one event of interest. Setting C introduces an issue related to the outcome by allowing there to be secondary outcomes or events, which compete with the primary event of interest.
In each of the three settings, we examined several different scenarios defined by the magnitude of the true treatment effect, the proportion of subjects who were treated, and the proportion of subjects who experienced the event or outcome.
We examined three simple settings that form a foundation for more complex definitions of exposure.A nested case–control (NCC) study is a variation of a case–control study in which cases and controls are drawn from the population in a fully enumerated cohort.
 Usually, the exposure of interest is only measured among the cases and the selected controls. Case control studies unmatched Nested case control study Case cohort study from HESC at California State University, Fullerton. The study described here is a pilot case–cohort study of lung cancer nested within the cohort to examine if it is possible, in a larger study to be conducted later, to identify specific potentially carcinogenic occupational exposures in poultry workers.
Nested Case-Control Study: This is a case-control study within a cohort study. At the beginning of the cohort study (t 0), members of the cohort are assessed for risk factors. Cases and controls are identified subsequently at time t 1.
A nested case-control study is a type of case-control study that draws its cases and controls from a cohort population that has been followed for a period of time.
Explanation A nested-case control study depends on the pre-existence of a cohort that has been followed over time. The nested case-control study design (or the case-control in a cohort study) is described here and compared with other designs, including the classic case-control and cohort studies and the case-cohort study.