Measures of Expected Influence Provide Useful Constraints to Enrollment in Randomized Multi-Center Clinical Trials for Binomial, Continuous and Time-to-Event Endpoints.

Avoiding excessive enrollment of a single cohort in a clinical trial is prudent in order to avoid imbalances and to prevent one cohort from having a disproportionate influence on the results of a trial and perhaps even negating positive findings of the clinical trial. Numerical criteria are provided here to evaluate the expected influence of a large cohort as a function of both its size and the relative effect of interventions, in comparison to those of other groups. Measures of expected influence are obtained as a function of the parameters of the distribution of statistics measuring influence. Calculated numerical criteria for the binomial, continuous and time-to-event contexts are presented. Details of the application of this method and sensitivity analyses conducted during the planning stages of a multiple myeloma clinical trial are provided. Numerical criteria are derived under asymptotic conditions and thus results hold for large cohorts. The numerical criteria are easy to compute and are useful tools to assess possible detrimental effects of large cohorts during the design of a study or during enrollment prior to any un-blinding. The numerical criteria allow for a-priori sensitivity analyses of the likely influence of large cohorts under varying conditions.

Machine Learning and Pattern Analysis Identify Distinctive Influences from Long-term Weekly Net Ecosystem Exchange at Four Deciduous Woodland Locations

A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.