Optimal information size in trial sequential analysis of time-to-event outcomes reveals potentially inconclusive results because of the risk of random error

Objectives: The current approach for evaluating the risk of random error in meta-analyses (MAs) using trial sequential analysis (TSA) can accommodate binary and continuous data but not time-to-event data. We conducted a TSA for time-to-event outcomes and applied the method to determine the risk of random error in MAs for treatments of multiple myeloma. Study Design and Setting: Literature search identified 11 systematic reviews consisting of 23 MAs. Of the 23 MAs, 13 had overall survival and 10 had progression-free survival as outcome; 48% (11 of 23) reported statistically significant treatment effects. We calculated the optimal a priori diversity-adjusted information size (APDIS) based on the relative risk reduction of 15% and 25%. We also calculated the optimal low-bias information size (LBIS) and low-bias diversity-adjusted information size (LBDIS). Results: Overall,, under APDIS(15%), 48% (11 of 23) of MAs were false negative (FN) and 17% (4 of 23) of MAs were false positive. Under APDIS(25%), 34% (8 of 23) of MAs were false negative and 4% (1 of 23) of MAs were false positive. LBIS identified 30% (7 of 23) as false negative MAs and 4% (1 of 23) as false positive MAs, whereas LBDIS identified 52% (12 of 23) as false negative MAs and 4% (1 of 23) as false positive MAs. Conclusion: The new method demonstrates the possibility of incorporating time-to-event outcomes into TSA and reveals that some MAs have potentially inconclusive results. (C) 2013 Elsevier Inc. All rights reserved.