Pooling data for number needed to treat: no problems for apples

Abstract

Objective: To consider the problem of the calculation of number needed to treat (NNT) derived from risk difference, odds ratio, and raw pooled events shown to give different results using data from a review of nursing interventions for smoking cessation.

Discussion: A review of nursing interventions for smoking cessation from the Cochrane Library provided different values for NNT depending on how NNTs were calculated. The Cochrane review was evaluated for clinical heterogeneity using L'Abbé plot and subsequent analysis by secondary and primary care settings.Three studies in primary care had low (4%) baseline quit rates, and nursing interventions were without effect. Seven trials in hospital settings with patients after cardiac surgery, or heart attack, or even with cancer, had high baseline quit rates (25%). Nursing intervention to stop smoking in the hospital setting was effective, with an NNT of 14 (95% confidence interval 9 to 26). The assumptions involved in using risk difference and odds ratio scales for calculating NNTs are discussed.

Summary: Clinical common sense and concentration on raw data helps to detect clinical heterogeneity. Once robust statistical tests have told us that an intervention works, we then need to know how well it works. The number needed to treat or harm is just one way of showing that, and when used sensibly can be a useful tool.

Figure 1

L'Abbé plot of nursing interventions versus control for smoking cessation at longest follow up. Dark blue symbols indicate studies in a hospital setting and light blue symbols those in a primary care setting.