We report our results using three different threaded acetabular components (Mecring A, Mecring B and Weill) in 715 hips with a follow-up of between one and ten years (median: 99.1, 56.5, 38.3 months, respectively). All cups were implanted with one type of cementless stem. The clinical results were good or acceptable in about 70% of the hips, but signs of loosening with radiolucency and/or migration were found in 10.1%. Radiological evidence of loosening did not correlate significantly with the clinical outcome. Pain was not a reliable indicator of loosening and its absence sometimes allowed severe osteolysis to develop. Twenty-five hips were revised (3.5%) for aseptic loosening of the acetabular component. Kaplan-Meier estimates of the cumulative rate of failure showed a rapid increase five years after the initial operation, but no significant correlation with gender, age or weight. The high rate of failure indicates that further use of these acetabular components cannot be recommended. Annual radiographs are required to assess osteolysis even if the patients are free from pain

Survival analysis is an important tool for assessing the outcome of total joint replacement. The Kaplan-Meier method is used to estimate the incidence of revision of a prosthesis over time, but does not account appropriately for competing events which preclude revision. In the presence of competing death, this method will lead to statistical bias and the curve will lose its interpretability. A valid comparison of survival results between studies using the method is impossible without accounting for different rates of competing events. An alternative and easily applicable approach, the cumulative incidence of competing risk, is proposed. Using three simulated data sets and realistic data from a cohort of 406 consecutive cementless total hip prostheses, followed up for a minimum of ten years, both approaches were compared and the magnitude of potential bias was highlighted. The Kaplan-Meier method overestimated the incidence of revision by almost 4% (60% relative difference) in the simulations and more than 1% (31.3% relative difference) in the realistic data set. The cumulative incidence of competing risk approach allows for appropriate accounting of competing risk and, as such, offers an improved ability to compare survival results across studies.