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Modelling cumulative exposure for inference about drug effects in observational studies
Author(s) -
Farran Bassam,
McGurnaghan Stuart,
Looker Helen C.,
Livingstone Shona,
Lahnsteiner Eva,
Colhoun Helen M.,
McKeigue Paul M.
Publication year - 2017
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4327
Subject(s) - medicine , hazard ratio , confounding , proportional hazards model , cumulative incidence , statistics , marginal structural model , observational study , confidence interval , causal inference , econometrics , cohort , mathematics , pathology
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.