The number needed to treat: a clinically useful measure of treatment effect.

The relative benefit of an active treatment over a control is usually expressed as the relative risk, the relative risk reduction, or the odds ratio. These measures are used extensively in both clinical and epidemiological investigations. For clinical decision making, however, it is more meaningful to use the measure "number needed to treat." This measure is calculated on the inverse of the absolute risk reduction. It has the advantage that it conveys both statistical and clinical significance to the doctor. Furthermore, it can be used to extrapolate published findings to a patient at an arbitrary specified baseline risk when the relative risk reduction associated with treatment is constant for all levels of risk.     

The number needed to treat: a clinically useful nomogram in its proper context.

The number needed to treat is a meaningful way of expressing the benefit of an active treatment over a control. It can be used either for summarising the results of a therapeutic trial or for medical decision making about an individual patient, but its use at the bedside has been impeded by the need for time consuming calculations. A nomogram has therefore been devised that will greatly simplify the calculations. Since calculations are now easy, the number needed to treat can be used to access the value of several interventions, although it does have its limitations. In particular it should not be used when it is not known whether the relative risk reduction associated with an intervention is constant for all levels of risk, or for periods of time longer than that studied in the original trials.     

A closer look at the distribution of number needed to treat (NNT): a Bayesian approach

In this paper, I present a Bayesian approach to estimation of the number needed to treat (NNT). The use of NNT as a measure of clinical benefit is now becoming commonplace. Various methods of estimation have been proposed, but none of them seem to provide entirely good estimates. Very little has been done to understand the statistical properties of NNT. Here, I derive the posterior distribution of NNT and use simulations to investigate the general behaviour of the distribution. The posterior mode of the distribution is proposed as a point estimate and results are compared with the conventional method of estimation of NNT done by inversion.     

On the estimation of average treatment effects with right-censored time to event outcome and competing risks

We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.     

Effectiveness as an outcome measure for treatment trials in psychiatry

There is at present some confusion about the relative value of clinical trials performed to investigate efficacy vs. those designed to investigate effectiveness. This is particularly challenging when studies performed as experiments for regulators by companies are used to shape and inform clinical practice, especially if studies conducted under more real life conditions fail to support predicted benefits. We review the field in relation to the new antipsychotics, in particular. Other indications, including mood disorders, which are also briefly touched upon, have so far received less definitive attention, but are likely to encounter the same difficulties. We conclude that, where the results of efficacy trials are positive and an effectiveness trial is negative, one should not necessarily prefer the effectiveness trial – it may simply have failed. Where efficacy trials and effectiveness trials point to similar conclusions, then the findings are mutually supportive.     

Numbers needed to treat calculated from responder rates give a better indication of efficacy in osteoarthritis trials than mean pain scores

Introduction  

Osteoarthritis trials usually report average changes in visual analogue scale (VAS) pain, and examine the difference between treatment and placebo. We investigated whether dichotomous responder analysis provides a more informative interpretation of drug efficacy.