TY - JOUR
T1 - Interpreting Results of Clinical Trials: A Conceptual Framework
JF - Clinical Journal of the American Society of Nephrology
JO - CLIN J AM SOC NEPHROL
SP - 1246
LP - 1252
DO - 10.2215/CJN.03580807
VL - 3
IS - 5
AU - Singh, Ajay K.
AU - Kelley, Ken
AU - Agarwal, Rajiv
Y1 - 2008/09/01
UR - http://cjasn.asnjournals.org/content/3/5/1246.abstract
N2 - Clinical trials are generally designed to test the superiority of an intervention (e.g., treatment, procedure, or device) as compared with a control. Trials that claim superiority of an intervention most often try to reject the null hypothesis, which generally states that the effect of an intervention of interest is no different from the control. In this editorial, we introduce a conceptual framework for readers, reviewers, and those involved in guideline development. This paradigm is based on evaluating a study on its statistical merits (result-based merit) as well as the clinical relevance of the potential treatment effect (process-based merit). We propose a decision matrix that incorporates these ideas in formulating the acceptability of a study for publication and/or inclusion in a guideline. Although noninferiority trials and equivalence trials are other valid trial designs, here we largely focus our discussion on superiority trials.Studies termed “negative” are commonly defined as those where the difference for the primary endpoint has a P value greater than or equal to 0.05 (P ≥ 0.05) (1), that is, where the null hypothesis is not rejected. These studies are difficult to publish because they are said to be “nonsignificant.” In other words, the data are not strong enough to persuade rejection of the null hypothesis. A high P value is frequently interpreted as proof that the null hypothesis is true; however, such an interpretation is a logical fallacy. A nonsignificant result implies that there was not enough evidence to infer probabilistically that the null hypothesis can be rejected. What is important to keep in mind is that the absence of evidence does not imply evidence of absence (2,3). On the other hand, if a small P value is observed, it implies there is evidence that the null hypothesis is false, which is why much …
ER -