Introduction: Many meta-analyses may provide misleading or inadequate inferences about the reliability of the evidence when an insufficient number of events and patients exist from trials that have been pooled. Nowadays most meta-analyses are frequently updated and subjected to significance testing as new trials emerge. This scenario is akin to interim analyses in randomized clinical trials (RCTs) where statistical monitoring boundaries and sample size requirements are used to efficiently and reliably establish whether the experimental treatment is superior. A growing body of evidence suggests that statistical monitoring boundaries yield similar utility when applied to meta-analysis. In addition, they can be useful for determining how many patients need to be randomized in future trials before the meta-analysis can be deemed conclusive and reliable. Methods: We performed prospective meta-analysis of RCTs that evaluated the effectiveness of isoniazid chemoprophylaxis versus placebo for preventing tuberculosis among HIV-positive individuals testing purified protein derivative negative. We calculated the required meta-analysis sample size, generated adjusted thresholds for statistical significance using trial LanDeMets monitoring boundaries and approximated the total number of patients required in future trials to make the meta-analysis statistically significant according to the adjusted thresholds. Results: The meta-analysis included nine trials comprising 2911 participants and yielded a relative risk of 0.74 (95% CI, 0.53-1.04, P = 0.08). To deem the meta-analysis statistically significant according to the adjusted thresholds set by the monitoring boundaries, a future RCT would need to randomize 3,800 participants (figure 1). Limitations: The projected future trial sample size of 3800 is only reliable to the extent that the underlying assumptions made for the required meta-analysis sample size are reasonable approximations of the truth . Conclusion: Statistical monitoring boundaries provide a framework for interpreting meta-analysis according to the adequacy of sample size and project the required sample size for a future RCT to make a meta-analysis conclusive.