Todd Durham

Randomization-based nonparametric methods for the analysis of multicentre trials

Multicentre trials offer several advantages over single centre trials in clinical research, including the ability to recruit patients at a faster rate over the course of the study, increased generalizability through the use of a broader patient population, and the ability to shed light on the replication of findings at multiple centres in a single study. A nonparametric approach to the analysis of multicentre trial data provides a convenient way for addressing the role of centres as well as baseline covariables during data analysis. With the use of randomization-based nonparametric methods, the strategy for evaluating the null hypothesis of no treatment effect can be prespecified during study planning without requiring a specific structure for the relationship of response criteria (or endpoints) to centres, covariables, or potential interaction terms. Further, the basis of inference for the application of these methods is the randomization mechanism, and the population to which inference can be directly made is the study population itself. No assumptions about underlying distributions, data structures, likelihood functions, or samples from super populations of inference are required. A three-step approach is proposed for handling centres via randomization-based nonparametric methods. In Step 1, a test of overall treatment effect is carried out using data from all centres simultaneously, without any assumption about treatment by centre interaction. In Step 2, the question of treatment by centre interaction is addressed, usually through the use of parametric multiple regression methods. In cases with suggestion of such interaction, Step 3 is conducted to evaluate different weighting schemes in forming pairwise treatment comparisons averaged across centres to assess the robustness of treatment effects observed in Step 1. An attractive inferential feature of this three-step approach is that the Type I error for the test of treatment effect is controlled by requiring statistical significance at each step to proceed to the next step. Extended Mantel-Haenszel methods with stratification adjustment for centre can be used to provide a nonparametric assessment of treatment effect. When adjustment for other covariates, such as baseline values, is desired, the more recent nonparametric analysis of covariance methods are available. Both methods are easy to use, require no assumptions beyond that of a valid randomization mechanism, and can be applied in a similar manner to dichotomous, ordinal, failure time, or continuous response criteria (endpoints). The methods are illustrated using data from a confirmatory clinical trial of a therapeutic agent for the treatment of dry eye disease.

Introductory Statistics in Medical Research

Descriptive statistics are used to summarize individual observations from a study and estimate a typical value (measures of central tendency) and the spread of values (measures of dispersion). Measures of central tendency include the mean and median. Measures of dispersion include the standard deviation and the range. Hypothesis tests and confidence intervals are two general forms of inferential statistical methods, for which the aim is to make an inference from a sample of subjects to a relevant population. Confidence intervals represent a plausible range of values of a population parameter, such as the difference in mean response, the difference in proportions, or the relative risk. p values are reported from hypothesis tests. Small p values (e.g., <0.05) suggest that the observed result was unlikely to have occurred by chance alone. There are many statistical methods which may be appropriate for any given research study. The most appropriate statistical approaches must consider the research question and the study design.

Effects of denufosol on sinusitis-related complaints in a phase 3 trial in cystic fibrosis patients

The most common mutation in Cystic Fibrosis, F508del, causes defects in trafficking, channel gating and endocytosis of the CFTR (CF Transmembrane conductance Regulator) protein. We have previously developed an automatic assay to identify agents, termed correctors, directed at repairing these defects. This study shows the identification from our screening of a novel corrector, roscovitine, able to restore the membrane localization and functionality of F508del-CFTR protein on the human airway epithelial CF cell line JME/CF15. We found that roscovitine corrected F508del-CFTR with an EC50 of 56mM. Moreover, biochemical analysis and immunofluorescence imaging confirmed the restoration of a mature F508del-CFTR at the cell surface. To pinpoint the molecular mechanism of roscovitine, we realized competition studies between roscovitine and inhibitors of the endoplasmic reticulum quality control (ERQC). No potentiation of roscovitine correction occured after co-treatment by roscovitine and the degradation inhibitor MG132. Only a small potentiation was obtained after co-treatment by roscovitine and thapsigargin which inhibits interaction between CFTR and calnexin, a Ca2+-dependent ER chaperone. Additional studies showed that roscovitine stimulates an intracellular Ca2+ increase, consecutive of the ER emptying. Moreover a Ca2+-independent inhibition of proteasome activity (~50%) in CF15 cells was observed after a roscovitine treatment. We conclude from the present study that roscovitine restores the abnormal trafficking of F508del-CFTR via a Ca2+-dependent and a Ca2+-independent mechanisms of action allowing disturbance of the ability of the ERQC to interact and to degrade F508del-CFTR proteins. Supported by Vaincre la Mucovicidose and CNRS.