Evaluating medication effects outside of clinical trials: new-user designs.

By W. Ray. American Journal of Epidemiology, 2003

1,615 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Endocrinology, Diabetes and Metabolism, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2d1c8f056f8948523dabff5d499cc5b6c3d9b0ea

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Citations in the corpus

Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses (2016, background): “The resulting left truncation will generally bias the risk ratio estimate because the analysis is restricted to those who remained under follow-up at the reset time zero [8,9].”
Major Hemorrhage and Tolerability of Warfarin in the First Year of Therapy Among Elderly Patients With Atrial Fibrillation (2007, neutral): “Diabetes mellitus 22 (106) 19 (29) 24 (77) 0.”
Unequal risks for breast cancer associated with different hormone replacement therapies: results from the E3N cohort study (2007, methodology): “(We restricted analysis in this way in our previous study [16], to avoid potential biases described by Ray [25]).”
High-dimensional propensity score adjustment in studies of treatment effects using health care claims data (2009, neutral): “The index date was the first date an NSAID prescription was filled.(28) The follow-up period included the 180 days after the initiation of therapy.”
A review of uses of health care utilization databases for epidemiologic research on therapeutics. (2005, background): “Such new-user designs are underused because of the logistic difficulty of screening large populations to identify new users and the loss of sample size and statistical power with exclusion of prevalent users [84].”
Antipsychotic Drug Use and Mortality in Older Adults with Dementia (2007, background): “To reduce the potential for selection bias, we studied only new users of antipsychotics and excluded those who had received antipsychotics in the year before cohort entry (21).”
Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease (2008, methodology): “This exclusion bias may be quantified through simulations,(21) reduced by stratification of the analysis on duration of therapy at baseline,(21) and eliminated by making the start of follow-up coincident with the time of treatment initiation, as discussed by Robins(22,23) and Ray.(24) The approach w”
Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease (2011, background): “LDL-cholesterol).(5) In our study, we have shown that there is much measured confounding by indication for statin therapy.”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “Comparisons of initiators of the various treatment strategies under investigation (sometimes referred to as new-user designs) are 1 simple way to avoid biases due to the selection of individuals who meet eligibility criteria that are defined after the initiation of a treatment strategy and therefore”