This article presents longitudinal likelihood ratio test (LongLRT) methods for large databases with exposure information. These methods are applied to a pooled large longitudinal clinical trial dataset for drugs treating osteoporosis with concomitant use of proton pump inhibitors (PPIs). When the interest is in the evaluation of a signal of an adverse event for a particular drug compared with placebo or a comparator, the special case of the LongLRT, referred to as sequential LRT (SeqLRT), is also presented. The results show that there is some possible evidence of concomitant use of PPIs leading to more adverse events associated with osteoporosis. The performance of the proposed LongLRT and SeqLRT methods is evaluated using simulated datasets and shown to be good in terms of (conditional) power and control of type I error over time. The proposed methods can also be applied to large observational databases with exposure information under the US Food and Drug Administration Sentinel Initiative for active surveillance. Published 2014. This article is a US Government work and is in the public domain in the USA.