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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSION: 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.

Factors influencing cyclosporine blood concentration-dose ratio.

OBJECTIVE: To analyze the trough cyclosporine concentration-dose ratio (CDR) and its relationship to some commonly available factors such as cyclosporine dosage, patient age, grade of obesity, posttransplant days, serum creatinine, serum bilirubin, and serum cholesterol by multiple linear regression. METHODS: The study was performed on 866 samples from 90 transplant recipients (25 kidney, 25 heart, 17 bone marrow, 13 liver, 10 simultaneous pancreas-kidney). RESULTS: The results show differences between transplants both in cyclosporine CDR variability (expressed by the coefficients of variation) and in the capability of those factors to explain this variability (expressed by the coefficient of determination). Coefficients of variation were 41% for the 866 samples (from 34% in heart to 55% in pancreas-kidney transplantation) and 28% for the 90 patients' CDR mean values (from 24% in heart to 32% in pancreas-kidney transplantation). All factors, except for the grade of obesity, were related to the cyclosporine CDR for all transplants as a whole. However, differences in the influence of each factor on each transplant were observed. The coefficient of determination based on significant factors was R2 = 0.25 for all samples (from 0.18 in pancreas-kidney to 0.52 in liver transplantation) and R2 = 0.53 for the patients' CDR means (from 0.39 in heart to 0.83 in kidney transplantation). CONCLUSIONS: We have quantified the cyclosporine CDR, its variability, and its relationship with some commonly available factors and found significant differences between transplant types. The equations of regression obtained might improve trough cyclosporine CDR estimation as a first step in cyclosporine dosage adjustment in kidney and liver transplant recipients.