Maximin 63

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four-step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose-finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design.

In this study, we report the antimicrobial planktonic and biofilm kill kinetics of ultrashort cationic lipopeptides previously demonstrated by our group to have a minimum biofilm eradication concentration (MBEC) in the microgram per mL (μg/mL) range against clinically relevant biofilm-forming micro-organisms. We compare the rate of kill for the most potent of these lipopeptides, dodecanoic (lauric) acid-conjugated C12 -Orn-Orn-Trp-Trp-NH2 against the tetrapeptide amide H-Orn-Orn-Trp-Trp-NH2 motif and the amphibian peptide Maximin-4 via a modification of the MBEC Assay(™) for Physiology & Genetics (P&G). Improved antimicrobial activity is achieved upon N-terminal lipidation of the tetrapeptide amide. Increased antimicrobial potency was demonstrated against both planktonic and biofilm forms of Gram-positive micro-organisms. We hypothesize rapid kill to be achieved by targeting of microbial membranes. Complete kill against established 24-h Gram-positive biofilms occurred within 4 h of exposure to C12 -OOWW-NH2 at MBEC values [methicillin-resistant Staphylococcus epidermidis (ATCC 35984): 15.63 μg/mL] close to the values for the planktonic minimum inhibitory concentration (MIC) [methicillin-resistant Staphylococcus epidermidis (ATCC 35984): 1.95 μg/mL]. Such rapid kill, especially against sessile biofilm forms, is indicative of a reduction in the likelihood of resistant strains developing with the potential for quicker resolution of pathogenic infection. Ultrashort antimicrobial lipopeptides have high potential as antimicrobial therapy.

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, with clusters being randomly assigned to treatment. The optimal sample size at the cluster and person level depends on the study cost per cluster and per person, and the outcome variance at the cluster and the person level. The variances are unknown in the design stage and can differ between treatment arms. As a solution, this paper presents a Maximin design that maximizes the minimum relative efficiency (relative to the optimal design) over the variance parameter space, for trials with two treatment arms and a quantitative outcome. This maximin relative efficiency design (MMRED) is compared with a published Maximin design which maximizes the minimum efficiency (MMED). Both designs are also compared with the optimal designs for homogeneous costs and variances (balanced design) and heterogeneous costs and homogeneous variances (cost-conscious design), for a range of variances based upon three published trials. Whereas the MMED is balanced under high uncertainty about the treatment-to-control variance ratio, the MMRED then tends towards a balanced budget allocation between arms, leading to an unbalanced sample size allocation if costs are heterogeneous, similar to the cost-conscious design. Further, the MMRED corresponds to an optimal design for an intraclass correlation (ICC) in the lower half of the assumed ICC range (optimistic), whereas the MMED is the optimal design for the maximum ICC within the ICC range (pessimistic). Attention is given to the effect of the Welch-Satterthwaite degrees of freedom for treatment effect testing on the design efficiencies.