Randomization with a posteriori constraints: Description and properties

Serum YKL-40 Levels, Leukocyte Profiles, and Acute Exacerbations of Advanced COPD

Little information exists on YKL-40-a key protein in tissue remodeling-and complete blood count (CBC) parameters during acute exacerbations of advanced chronic obstructive pulmonary disease (COPD). This pilot exploratory study (August 2020-January 2021) investigated the connection between serum YKL-40 levels and CBC profile in sex- and age-matched individuals with severe COPD (GOLD stage III, n = 23, median age = 66 years, 65.21% males) and very severe COPD (GOLD stage IV, n = 24, median age = 66.5 years, 74.81% males). The measured parameters were serum YKL-40, absolute leukocyte count (ALLC), absolute neutrophil count (ANC), neutrophil percentage, absolute lymphocyte count (ALC), lymphocyte percentage, neutrophil-to-lymphocyte ratio (NLR), absolute eosinophil count (AEC), eosinophil percentage, absolute monocyte count (AMC), monocyte percentage, absolute basophil count (ABC), basophil percentage, hemoglobin levels, and hematocrit concentrations. No significant inter-group differences were observed. However, high YKL-40 subjects (n = 23)-as stratified via median YKL-40 (3934.5 pg/mL)-showed significantly increased neutrophil percentage and NLR but significantly lower lymphocyte-, eosinophil-, and basophil-related parameters compared to low YKL-40 patients (n = 24). These results reveal multidimensional, YKL-40-associated changes in leukocyte profile of patients with advanced COPD during acute exacerbations, with potential implications for personalized treatment.

Comparisons of minimization and Atkinson's algorithm

Some general points regarding efficiency in clinical trials are made. Reasons as to why fitting many covariates to adjust the estimate of the treatment effect may be less problematic than commonly supposed are given. Two methods of dynamic allocation of patients based on covariates, minimization and Atkinson's approach, are compared and contrasted for the particular case where all covariates are binary. The results of Monte Carlo simulations are also presented. It is concluded that in the cases considered, Atkinson's approach is slightly more efficient than minimization although the difference is unlikely to be very important in practice. Both are more efficient than simple randomization, although it is concluded that fitting covariates may make a more valuable and instructive contribution to inferences about treatment effects than only balancing them.