Net efficacy adjusted for risk (NEAR): a simple procedure for measuring risk:benefit balance.
BACKGROUND
Although several mathematical models have been proposed to assess the risk:benefit of drugs in one measure, their use in practice has been rather limited. Our objective was to design a simple, easily applicable model. In this respect, measuring the proportion of patients who respond favorably to treatment without being affected by adverse drug reactions (ADR) could be a suitable endpoint. However, remarkably few published clinical trials report the data required to calculate this proportion. As an approach to the problem, we calculated the expected proportion of this type of patients.


RESULTS
Theoretically, responders without ADR may be obtained by multiplying the total number of responders by the total number of subjects that did not suffer ADR, and dividing the product by the total number of subjects studied. When two drugs are studied, the same calculation may be repeated for the second drug. Then, by constructing a 2 x 2 table with the expected frequencies of responders with and without ADR, and non-responders with and without ADR, the odds ratio and relative risk with their confidence intervals may be easily calculated and graphically represented on a logarithmic scale. Such measures represent "net efficacy adjusted for risk" (NEAR). We assayed the model with results extracted from several published clinical trials or meta-analyses. On comparing our results with those originally reported by the authors, marked differences were found in some cases, with ADR arising as a relevant factor to balance the clinical benefit obtained. The particular features of the adverse reaction that must be weighed against benefit is discussed in the paper.


CONCLUSIONS
NEAR representing overall risk-benefit may contribute to improving knowledge of drug clinical usefulness. As most published clinical trials tend to overestimate benefits and underestimate toxicity, our measure represents an effort to change this trend.
Boada
José N
JN
Clinical Pharmacology Unit, Pharmacovigilance Centre, University Hospital of Canary Islands, University of La Laguna, La Laguna, Santa Cruz de Tenerife, Spain. boadajuarez@gmail.com
Boada
Carlos
C
García-Sáiz
Mar
M
García
Marcelino
M
Fernández
Eduardo
E
Gómez
Eugenio
E
D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000009:adverse effects; D000893:Anti-Inflammatory Agents / Q000008:administration & dosage / Q000009:adverse effects; D002318:Cardiovascular Diseases / Q000517:prevention & control; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology; D024841:Fluoroquinolones / Q000008:administration & dosage / Q000009:adverse effects; D000077735:Gemifloxacin; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000009:adverse effects / Q000627:therapeutic use; D008962:Models, Theoretical; D009287:Naphthyridines / Q000008:administration & dosage / Q000009:adverse effects; D011014:Pneumonia / Q000188:drug therapy; D011315:Preventive Medicine / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000379:methods; D016896:Treatment Outcome
D000900:Anti-Bacterial Agents; D000893:Anti-Inflammatory Agents; D024841:Fluoroquinolones; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D009287:Naphthyridines; C080163:trovafloxacin; D000077735:Gemifloxacin