A Novel Method for Deriving Adverse Event Prevalence in Randomized Controlled Trials: Potential for Improved Understanding of Benefit-Risk Ratio and Application to Drug Labels.

INTRODUCTION: Adverse event (AE) data in randomized controlled trials (RCTs) allow quantification of a drug's safety risk relative to placebo and comparison across medications. The standard US label for Food and Drug Administration-approved drugs typically lists AEs by MedDRA Preferred Term that occur at ≥ 2% in drug and with greater incidence than in placebo. We suggest that the drug label can be more informative for both patients and physicians if it includes, in addition to AE incidence (percent of subjects who reported the AE out of the total subjects in treatment), the absolute prevalence (percent of subject-days spent with an AE out of the total subject-days spent in treatment) and expected duration (days required for AE incidence to be reduced by half). We also propose a new method to analyze AEs in RCTs using drug-placebo difference in AE prevalence to improve safety signal detection. METHODS: AE data from six RCTs in schizophrenia were analyzed (five RCTs of the dopamine D2 receptor-based antipsychotic lurasidone and one RCT of the novel trace amine-associated receptor 1 [TAAR1] agonist ulotaront). We determined incidence, absolute prevalence, and expected duration of AEs for lurasidone and ulotaront vs respective placebo. We also calculated areas under the curve of drug-placebo difference in AE prevalence and mean percent contribution of each AE to this difference. RESULTS: A number of AEs with the same incidence had different absolute prevalence and expected duration. When accounting for these two parameters, AEs that did not appear in the 2% incidence tables of the drug label turned out to contribute substantially to drug tolerability. The percent contribution of a drug-related AE to the overall side effect burden increased the drug-placebo difference in AE prevalence, whereas the percent contribution of a placebo-related AE decreased such difference, revealing a continuum of risk between drug and placebo. AE prevalence curves for drug were generally greater than those for placebo. Ulotaront exhibited a small drug-placebo difference in AE prevalence curves due to a relatively low incidence and short duration of AEs in the ulotaront treatment arm as well as the emergence of disease-related AEs in the placebo arm. CONCLUSION: Reporting AE absolute prevalence and expected duration for each RCT and incorporating them in the drug label is possible, is clinically relevant, and allows standardized comparison of medications. Our new metric, the drug-placebo difference in AE prevalence, facilitates signal detection in RCTs. We piloted this metric in RCTs of several neuropsychiatric indications and drugs, offering a new way to compare AE burden and tolerability among treatments using existing clinical trial information.

Competitively coupled maps and spatial pattern formation.

Spatial pattern formation is a key feature of many natural systems in physics, chemistry, and biology. The essential theoretical issue in understanding pattern formation is to explain how a spatially homogeneous initial state can undergo spontaneous symmetry breaking leading to a stable spatial pattern. This problem is most commonly studied using partial differential equations to model a reaction-diffusion system of the type introduced by Turing. We report here on a much simpler and more robust model of spatial pattern formation, which is formulated as a novel type of coupled map lattice. In our model, the local site dynamics are coupled through a competitive, rather than diffusive, interaction. Depending only on the strength of the interaction, this competitive coupling results in spontaneous symmetry breaking of a homogeneous initial configuration and the formation of stable spatial patterns. This mechanism is very robust and produces stable pattern formation for a wide variety of spatial geometries, even when the local site dynamics is trivial.