We develop a Bayesian Power generalized Weibull shape parameter (PgWSP) test as statistical method for signal detection of possible drug-adverse event associations using electronic health records for pharmacovigilance. The Bayesian approach allows the incorporation of prior knowledge about the likely time of occurrence along time-to-event data. The test is based on the shape parameters of the Power generalized Weibull (PgW) distribution. When both shape parameters are equal to one, the PgW distribution reduces to an exponential distribution, i.e. a constant hazard function. This is interpreted as no temporal association between drug and adverse event. The Bayesian PgWSP test involves comparing a region of practical equivalence (ROPE) around one reflecting the null hypothesis with estimated credibility intervals reflecting the posterior means of the shape parameters. The decision to raise a signal is based on the outcomes of the ROPE test and the selected combination rule for these outcomes. The development of the test requires a simulation study for tuning of the ROPE and credibility intervals to optimize specifcity and sensitivity of the test. Samples are generated under various conditions, including differences in sample size, prevalence of adverse drug reactions (ADRs), and the proportion of adverse events. We explore prior assumptions reflecting the belief in the presence or absence of ADRs at different points in the observation period. Various types of ROPE, credibility intervals, and combination rules are assessed and optimal tuning parameters are identifed based on the area under the curve. The tuned Bayesian PgWSP test is illustrated in a case study in which the time-dependent correlation between the intake of bisphosphonates and four adverse events is investigated.