Risk patterns in drug safety study using relative times by accelerated failure time models when proportional hazards assumption is questionable: an illustrative case study of cancer risk of patients on glucose-lowering therapies

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry 2015-09, Vol.14 (5), p.382-394
• Main Authors: Ng, Edmond S.-W., Klungel, Olaf H., Groenwold, Rolf H. H., van Staa, Tjeerd-Pieter
• Format: Article
• Language: English
• Subjects: accelerated failure time; Aged; Bias; Confounding Factors, Epidemiologic; Drug-Related Side Effects and Adverse Reactions - epidemiology; Female; Humans; Hypoglycemic Agents - administration & dosage; Hypoglycemic Agents - adverse effects; Male; Middle Aged; Models, Statistical; Neoplasms - chemically induced; Neoplasms - diagnosis; Neoplasms - epidemiology; Observational Studies as Topic - methods; Proportional Hazards Models; relative time; Risk; risk patterns; safety studies; time difference; Time Factors
• ISSN: 1539-1604; 1539-1612
• DOI: 10.1002/pst.1697

Observational drug safety studies may be susceptible to confounding or protopathic bias. This bias may cause a spurious relationship between drug exposure and adverse side effect when none exists and may lead to unwarranted safety alerts. The spurious relationship may manifest itself through substantially different risk levels between exposure groups at the start of follow‐up when exposure is deemed too short to have any plausible biological effect of the drug. The restrictive proportional hazards assumption with its arbitrary choice of baseline hazard function renders the commonly used Cox proportional hazards model of limited use for revealing such potential bias. We demonstrate a fully parametric approach using accelerated failure time models with an illustrative safety study of glucose‐lowering therapies and show that its results are comparable against other methods that allow time‐varying exposure effects. Our approach includes a wide variety of models that are based on the flexible generalized gamma distribution and allows direct comparisons of estimated hazard functions following different exposure‐specific distributions of survival times. This approach lends itself to two alternative metrics, namely relative times and difference in times to event, allowing physicians more ways to communicate patient's prognosis without invoking the concept of risks, which some may find hard to grasp. In our illustrative case study, substantial differences in cancer risks at drug initiation followed by a gradual reduction towards null were found. This evidence is compatible with the presence of protopathic bias, in which undiagnosed symptoms of cancer lead to switches in diabetes medication. Copyright © 2015 John Wiley & Sons, Ltd.