Absolute benefit, number needed to treat and gain in life expectancy: which efficacy indices for measuring the treatment benefit?

The absolute benefit (AB) is extensively used to summarize the results of clinical trials. As the AB depends directly on the patient's baseline risk, therapeutic decisions based on AB tend to favor patients at high risk. To evaluate the consequences of this decision's procedure for life-long therapy, we compare the AB with the gain in event-free life expectancy in a simulated hypertensive population. Our results show that the AB goes through a maximum and then declines as the duration of treatment increases. The amplitude of the variation of AB is independent of the baseline risks but the maximum is reached more quickly in the high-risk patients. Considering the gain in event-free life expectancy, low-risk patients benefit more than high-risk patients do, at the expense of a longer treatment exposure. The interpretation of the AB changes depending on follow-up.     

When should an effective treatment be used? Derivation of the threshold number needed to treat and the minimum event rate for treatment

Clinicians and patients must decide when treatment effects are large enough to more than offset the adverse effects and costs of therapy. Calculation of the number of patients one needs to treat (NNT) in order to prevent one patient from having the target event is one tool to help with this decision. Clinicians should treat patients when the NNT is lower than a threshold NNT at which point the therapeutic risk equals the therapeutic benefit. We aimed: (1) to identify the determinants of the threshold NNT, and (2) to derive equations for the explicit estimation of the threshold NNT and of the minimum expected rate of target event, without treatment, above which treatment is justified. We conceived the threshold number needed to treat to prevent one target event as the point at which the benefits of treating that number of patients equal the negative consequences of treating that same number of patients. After identifying the various elements comprising the treatment impact, we equated the benefits and negative consequences and solved the equation for threshold NNT. We then derived the minimum expected rate of target event which would justify treatment. We derived an equation that relates the threshold NNT to the treatment-attributable adverse event rates (AER) and the relative values (RV) of the adverse events compared to that of the target event prevented. Specifically, the threshold NNT, denoted NNT(T) is given by NNT(T) = 1/(AER(1).RV(1) +...+ AER(n).RV(n)). We also derived a more complex equation which addresses the costs incurred by treatment and costs avoided by preventing target events. Solving the equation that includes costs requires specifying both the value of preventing a target event and the values of adverse treatment effects in economic units. The threshold NNT and the relative risk reduction (RRR) for the target event determine the minimum target event rate above which treatment is justified. This minimum event rate for treatment is 1/(NNT(T).RRR). The values that clinicians and patients use determine the threshold NNT and therefore also the minimum target event rate, without treatment, above which treatment is justified. Quantification of the determinants of the threshold NNT and of the minimum event rate to justify treatment can assist clinicians and patients in the explicit use of underlying values when making treatment decisions.     

Number needed to treat: easily understood and intuitively meaningful? Theoretical considerations and a randomized trial

Graphic representation was used to explore to what extent the number needed to treat (NNT) conveys the appropriate notion of benefit for the individual patient in interventions aimed at delaying adverse events. A sample of the Danish population (n = 675) was interviewed face to face, and asked whether they would consent to a hypothetical drug that reduces the risk of heart attack. The benefit of the drug was expressed in terms of NNT and was randomly set at 10, 25, 50, 100, 200, and 400. NNT does not convey information on the proportion of patients being helped by an intervention or the size of the delay of the adverse event intended to be prevented. The proportion of people consenting to the hypothetical drug was about 80%, irrespective of NNT, and some of those who rejected the drug misinterpreted the meaning of NNT. Lay people may have difficulties in understanding the meaning of NNT, and clinicians may do well to use the NNT with caution until more is known about how patients comprehend it.     

How and why researchers use the number needed to vaccinate to inform decision making—A systematic review

BackgroundThe number needed to vaccinate (NNV) is a measure that has been widely used in the scientific literature to draw conclusions about the usefulness and cost-effectiveness of various immunization programmes. The main objective of this review is to examine how and why the NNV has been used and reported in the published literature.MethodsElectronic databases were searched and records were screened against the eligibility criteria by two independent authors. We included papers that reported and interpreted NNV.ResultsWe identified 27 studies, the designs including observational studies, economic analyses, systematic reviews, and commentaries. The NNV has been used in the literature to describe three main themes: potential benefits of vaccination programmes, cost-effectiveness, and economic analyses, and modelling studies to compare different vaccination strategies.ConclusionsNNV has been used in a wide variety of ways in the literature, yet there are no defined thresholds for what is a favourable NNV. Furthermore, the generalizability of the NNV is usually limited. Further work is required to determine the most appropriate use of this measure.     

Should physicians team up to treat chronic diseases?

This paper studies referral strategy and effort provision of a primary care physician and a specialist who are responsible for the treatment of chronically ill patients who can be in a mild or severe condition. Two organizational settings are compared, a team in which physicians cooperate and solo practices in which they do not. Team care is strictly superior to solo practice care if the difference in expected treatment costs between disease severities is relatively larger for the primary care physician. Otherwise, solo practice care is weakly superior to team care under reasonable assumptions.     

What is the best way to treat tinea cruris?

After clinical diagnosis and microscopic confirmation, tinea cruris is best treated with a topical allylamine or an azole antifungal (strength of recommendation: A, based on multiple randomized controlled trials [RCTs]). Differences in current comparison data are insufficient to stratify the 2 groups of topical antifungals. Determining which group to use depends on patient compliance, medication accessibility, and cost. The fungicidal allylamines (naftifine and terbinafine) and butenafine (allylamine derivative) are a more costly group of topical tinea treatments, yet they are more convenient as they allow for a shorter duration of treatment compared with fungistatic azoles (clotrimazole, econazole, ketoconazole, oxiconazole, miconazole, and sulconazole).     

May a dentist refuse to treat an HIV-positive patient?

Medicine, Health Care and Philosophy -     

How much subjectivity is needed to understand our lives objectively?

The author provides afirst-person account, based on diaries and reminiscences, of a woman living in an abusive relationship. Using parallel texts, she juxtaposes the experience of spousal abuse against information derived from research literature and raises questions concerning objectivity in scholarship.     

What type of information is needed to inform mental health policy?

The most valuable research integrates from thre levels of investigation: clinical efficacy, "real life" effectiveness (including cost-effectiveness) and policy research. Successful applications of systematic reviews have largely been limited to clinical efficacy questions. The contribution of systematic reviews/meta-analyses to effectiveness and economic questions in mental health has been very minor and their contribution to inform policy is negligible. The latter is unlikely to change due to the different type of information that policy makers need.