Modelling cumulative exposure for inference about drug effects in observational studies.
Pharmacoepidemiol Drug Saf
; 26(12): 1527-1533, 2017 Dec.
Article
em En
| MEDLINE
| ID: mdl-29024286
PURPOSE: To demonstrate a modelling approach that controls for time-invariant allocation bias in estimation of associations of outcome with drug exposure. METHODS: We show that in a model that includes terms for both ever-exposure versus never-exposure and cumulative exposure, the parameter for ever-exposure represents the effect of time-invariant allocation bias, and the parameter for cumulative exposure represents the effect of the drug after adjustment for this unmeasured confounding. This assumes no stepwise effect of the drug on the event rate, no reverse causation, and no unmeasured time-varying confounders. We demonstrated this by modelling the effect of statins on cardiovascular disease, for which the true effect has been well characterised in randomised trials, using time-updated Cox regression models in a national cohort of Type 2 diabetes patients. RESULTS: The crude hazard ratio associated with ever-use of statins was 1.13 in a standard cohort analysis comparing exposed with unexposed person-time intervals. When ever-never use and cumulative exposure are modelled jointly, the effect of statins can be estimated from the cumulative exposure parameter (hazard ratio 0.97 per year of exposure, 95% CI 0.97 to 0.98). The ever-exposed term (hazard ratio 1.20, 1.16 to 1.23) in this model can be interpreted as estimating the allocation bias. CONCLUSIONS: Where stepwise effects on the risk of adverse events are unlikely, as for instance for effects on risk of cancer, joint modelling of ever-never and cumulative exposure can be used to study the effects of multiple drugs and to distinguish causal effects from confounding by allocation.
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Base de dados:
MEDLINE
Assunto principal:
Doenças Cardiovasculares
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Inibidores de Hidroximetilglutaril-CoA Redutases
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Modelos Teóricos
Tipo de estudo:
Clinical_trials
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Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
País como assunto:
Europa
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article