Calculation of NNTs in RCTs with time-to-event outcomes: A literature review

Abstract

BackgroundThe number needed to treat (NNT) is a well-known effect measure for reporting the results of clinical trials. In the case of time-to-event outcomes, the calculation of NNTs is more difficult than in the case of binary data. The frequency of using NNTs to report results of randomised controlled trials (RCT) investigating time-to-event outcomes and the adequacy of the applied calculation methods are unknown.MethodsWe searched in PubMed for RCTs with parallel group design and individual randomisation, published in four frequently cited journals between 2003 and 2005. We evaluated the type of outcome, the frequency of reporting NNTs with corresponding confidence intervals, and assessed the adequacy of the methods used to calculate NNTs in the case of time-to-event outcomes.ResultsThe search resulted in 734 eligible RCTs. Of these, 373 RCTs investigated time-to-event outcomes and 361 analyzed binary data. In total, 62 articles reported NNTs (34 articles with time-to-event outcomes, 28 articles with binary outcomes). Of the 34 articles reporting NNTs derived from time-to-event outcomes, only 17 applied an appropriate calculation method. Of the 62 articles reporting NNTs, only 21 articles presented corresponding confidence intervals.ConclusionThe NNT is used as effect measure to present the results from RCTs with binary and time-to-event outcomes in the current medical literature. In the case of time-to-event data incorrect methods were frequently applied. Confidence intervals for NNTs were given in one third of the NNT reporting articles only. In summary, there is much room for improvement in the application of NNTs to present results of RCTs, especially where the outcome is time to an event.

Effectiveness of cranberry capsules to prevent urinary tract infections in vulnerable older persons. A double-blind randomized placebo-controlled trial in long-term care facilities

“…36,37 The difference in NNT between treatment groups was tested using a z-test; P ≤ .05 was considered significant.…”

Using and Interpreting Adjusted NNT Measures in Biomedical Research

“…For the analysis of time-to-event outcomes the use of survival time methods is required. However, a recent systematic literature review of RCTs with parallel group design and individual randomization published in 4 major medical journals in the period [2003][2004][2005] found that in the case of time-to-event outcomes inadequate methods to estimate NNTs have been used in 50% of articles presenting NNTs (17 of 34 articles) [10]. In studies in which the effects of covariates are taken into account to estimate relative effect measures such as adjusted odds ratios, these covariates should also be used to estimate adjusted NNTs [13].…”

“…Additionally, the explanatory document of the Consolidated Standards of Reporting Trials (CONSORT) statement [9] exposes that NNTs are helpful for expressing the results of studies with binary and survival time outcomes. In spite of its widespread use, NNTs are frequently misused, incorrectly calculated, incompletely or misleadingly presented, and incorrectly interpreted [2,10,11]. There are various reasons for misleading applications of NNTs.…”

How the latest evidence from clinical research informs patient care

“…Cardiac events were reduced from 10.8 events/ 100 patient years to 7.8 events per 100 patient years, (HR 0.68; 95% CI 0.51-0.90) for a number needed to treat (NNT) of 33 patients for one year to prevent one cardiac event. 10 Treatment effects adjusted for Beta-blockers, angiotensin converting enzyme/angiotensin receptor blockers or sevelamer usages, serum calcium and phosphate levels were all well matched and no signifi cant interactions with mortality or primary composite endpoints were found. Traditional cardiac risk factors such as LDL-cholesterol, blood pressure (BP) and smoking were not associated with signifi cant cardiac outcomes.…”