Nucleic acid amplification test (NAAT) are increasingly used to diagnose Clostridium difficile infection (CDI) in the Emerging Infections Program (EIP). Because NAAT is highly sensitive compared to EIA, adjustment of cases diagnosed by EIA is needed to monitor incidence trends. We conducted a literature review and obtain the individual excess positive rate by NAAT compared to EIA. We then developed a Bayesian approach to conduct the random-effects meta-analysis to estimate the pool excess positive rates by NAAT. The advantage of Bayesian approach is that the random effects variance is acknowledged in the pooled effect, unlike a classical analysis where the pooled estimate uses just a point estimate of the variance. From 2011-2015, the proportion CDI cases diagnosed by NAAT increased from 52% to 88%, due to highly sensitivity of NAAT , crude CDI incidence increased from 2011 to 2015, after applying the estimated excess positive rates by NAAT to EIA diagnosis cases, the adjusted CDI incidence declined over the years. Our results demonstrated the importance of adjusting CDI incidence based on testing types for understanding trends in CDI incidence in EIP.