The case–crossover and case–time‐control designs in pharmacoepidemiology

Donnan, Peter T.; Wang, Jixian

doi:10.1002/pds.590

Cited by 17 publications

(16 citation statements)

References 24 publications

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“…This design is an extension of the case-crossover design first described by Maclure [22],[23], which compares within-patient exposure to a potential risk factor in the period immediately preceding a putative adverse event (the risk interval) to exposure during a different time (the reference interval). Because cases serve as their own controls, fixed patient characteristics are controlled for implicitly under this design [24],[25]. However, the case-crossover design can be vulnerable to spurious associations between a drug and an outcome owing to temporal trends in drug utilization.…”

Section: Methodsmentioning

confidence: 99%

Cholinesterase Inhibitors and Hospitalization for Bradycardia: A Population-Based Study

et al. 2009

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Section: Methodsmentioning

confidence: 99%

Cholinesterase Inhibitors and Hospitalization for Bradycardia: A Population-Based Study

et al. 2009

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“…Second, the risk associated with the exposure must rise and fall rapidly. These assumptions mean that the investigation of chronic diseases with long‐term therapy is unsuitable with this type of study …”

Section: Maximising the Results Of Op Studiesmentioning

confidence: 99%

“…The case‐crossover design is also sensitive to misspecification of the exposure window (see risk window bias) and if the drug is available over‐the‐counter, nonprescribed doses would be omitted from the patient's prescribing record leading to information bias. This study design is also prone to recall bias, if patients' recollections are used to define exposure rather than more objective measures, such as prescriptions …”

Section: Maximising the Results Of Op Studiesmentioning

confidence: 99%

“…These assumptions mean that the investigation of chronic diseases with long-term therapy is unsuitable with this type of study. [112][113][114] Although in theory a case-cross over design could be used to investigate treatment effect, it is more often used to assess harm, such as demonstrating that recent vaccination does not appear to raise the risk of multiple sclerosis relapse. 115 Importantly, this type of study design cannot account for time-varying within-patient confounders, e.g.…”

Section: Instrumental Variable Analysismentioning

confidence: 99%

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Pharmacoepidemiology: Using randomised control trials and observational studies in clinical decision‐making

Caparrotta

Dear

Colhoun

et al. 2019

Brit J Clinical Pharma

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Weighing up sources of evidence is a key skill for clinical decision‐makers. Randomised controlled trials (RCTs) and observational studies each have advantages and disadvantages, and in both cases perceived weaknesses can be improved through modifications of design and analysis. In the field of pharmacoepidemiology, RCTs are the best way to determine whether an intervention modifies an outcome being studied, largely because randomisation reduces bias and confounding. Observational studies are useful to investigate whether benefits/harms of a treatment are seen in day‐to‐day clinical practice in a wider group of patients. Although observational studies, even in a small cohort, can provide very useful clinical evidence, they may also be misleading (as shown by subsequent RCTs), in part because of allocation bias. There is an unmet need for clinicians to become well versed in appraising the study design and statistical analysis of observational pharmacoepidemiology (OP) studies, rather like the medical training already offered for RCT evaluation. This is because OP studies are likely to become more common with the computerisation of healthcare records and increasingly contribute to the evidence base available for clinical decision‐making. However, when the results of an RCT conflict with the results of an OP study, the findings of the RCT should be preferred, especially if its findings have been repeated elsewhere. Conversely, OP studies that align with the findings of RCTs can provide rich and useful information to complement that generated by RCTs.

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“… If severity, which is associated with drug use also increases (or decreases) within subjects over time, and does so differently for case and control subjects, what is believed to be the residual effect associated with the drug could well remain confounded to some extent [21,[24][25][26]  It usually produces effect estimates that are systematically lower than those from the case-control design [27]  The within subjects correlation induced by this design may result in less precise effect estimate. But the apparent loss is an artefact since the case-control approach produces biased estimates of drug effects, confounded by disease severity.…”

Section: Case-in-time Designmentioning

confidence: 99%

How Real is Intention-To-Treat (ITT) Analysis in Non-Interventional Post Authorization Safety Studies? We Can Do Better

Kiri¹,

MacKenzie²

2009

CDS

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Although cohort studies which are based on intention-to-treat (ITT) approach offer a simple design with data which are simpler to analyse and results easier to interpret, such studies also intrinsically assume that any time-varying treatment effect that exits can be adequately estimated by a fixed-effect component. However, such an assumption may not reflect real-life drug use. Reflection of real-life clinical practice is a major strength of epidemiologic safety studies. The failure to properly reflect reality may result in effect under-estimation leading to false and irreproducible conclusions due to exposure misclassification. In effect, the use of nested case-control design is a concession that ITT in cohort design may not be adequate. But the nested design also has its own sources of bias, including confounding by indication. We present an overview of the counter-matched version of the nested case-control, case-crossover, case-in-time, case series and case-cohort designs as alternatives in prospective postauthorization safety studies.

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The case–crossover and case–time‐control designs in pharmacoepidemiology

Cited by 17 publications

References 24 publications

Cholinesterase Inhibitors and Hospitalization for Bradycardia: A Population-Based Study

Cholinesterase Inhibitors and Hospitalization for Bradycardia: A Population-Based Study

Pharmacoepidemiology: Using randomised control trials and observational studies in clinical decision‐making

How Real is Intention-To-Treat (ITT) Analysis in Non-Interventional Post Authorization Safety Studies? We Can Do Better

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