Mean Response Models of Repeated Measurements in Presence of Varying Effectiveness Onset

Repeated measurements are often collected over time to evaluate treatment efficacy in clinical trials. Most of the statistical models of the repeated measurements have been focusing on their mean response as function of time. These models usually assume that the treatment has persistent effect of constant additivity or multiplicity on the mean response functions throughout the observation period of time. In reality, however, such assumption may be confounded by the potential existence of the so-called effectiveness action onset, although they are often unobserved or difficult to obtain. Instead of including nonparametric time-varying coefficients in the mean response models, we propose and study some semiparametric mean response models to accommodate such effectiveness times. Our methodologies will be demonstrated by a real randomised clinical trial data

Semiparametric Regression Analysis of Mean Residual Life with Censored Survival Data

As a function of time t, mean residual life is the remaining life expectancy of a subject given survival up to t. The proportional mean residual life model, proposed by Oakes & Dasu (1990), provides an alternative to the Cox proportional hazards model to study the association between survival times and covariates. In the presence of censoring, we develop semiparametric inference procedures for the regression coefficients of the Oakes-Dasu model using martingale theory for counting processes. We also present simulation studies and an application to the Veterans\u27 Administration lung cancer data

2-(4-Bromo­phen­yl)-1-ethyl-1H-1,3-benzodiazole

In the title compound, C15H13BrN2, the benzimidazole group is almost planar, as indicated by the dihedral angle of 2.6 (3)° between the best planes through the benzene and imidazole rings. The best plane through the attached benzene makes an angle of 44.5 (2)° with the best plane through the benzimidazole system. C—H⋯π inter­actions are observed in the crystal structure

Estimating a Treatment Effect with Repeated Measurements Accounting for Varying Effectiveness Duration

To assess treatment efficacy in clinical trials, certain clinical outcomes are repeatedly measured for same subject over time. They can be regarded as function of time. The difference in their mean functions between the treatment arms usually characterises a treatment effect. Due to the potential existence of subject-specific treatment effectiveness lag and saturation times, erosion of treatment effect in the difference may occur during the observation period of time. Instead of using ad hoc parametric or purely nonparametric time-varying coefficients in statistical modeling, we first propose to model the treatment effectiveness durations, which are the varying time intervals between the lag and saturation times. Then some mean response models are used to include such treatment effectiveness durations. Our methodologies are demonstrated by simulations and an application to the dataset of a landmark HIV/AIDS clinical trial of short-course nevirapine against mother-to-child HIV vertical transmission during labour and delivery

Poly[tetra-n-butyl­ammonium [(μ5-benzene-1,3,5-tricarboxyl­ato)(μ4-benzene-1,3,5-tricarboxyl­ato)-μ3-hydroxido-trizincate] 0.25-hydrate]

In the asymmetric unit of title coordination polymer, {(C16H36N)[Zn3(C9H3O6)2(OH)]·0.25H2O}n, there are three independent Zn2+ cations, two benzene-1,3,5-tricarboxyl­ate ligands and a μ3-bridging hydroxide group, together with a tetra-n-butyl­ammonium counter-cation and a partially occupied water molecule of solvation (occupancy 0.25). Each Zn ion is coordinated by three carboxyl­ate O atoms and one O atom from the bridging hydroxide ion, displaying a slightly distorted tetra­hedral stereochemistry [overall Zn—O range = 1.875 (3)–1.987 (2) Å]. An intra­molecular hydrogen bond involving the hydroxide H atom and a carboxyl­ate O-atom acceptor is also present in the complex unit. The bridging benzene-1,3,5-tricarboxyl­ate anions generate a three-dimensional framework structure

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

We hypothesized that blood lactate concentration([Lac]blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry

Methyl 1-(2,6-difluoro­benz­yl)-1H-1,2,3-triazole-4-carboxyl­ate

In the title compound, C11H9F2N3O2, the triazole ring is planar, with an r.m.s. deviation of 0.0048 Å, and makes a dihedral angle of 77.3 (1)° with the benzene ring. In the crystal, weak inter­molecular C—H⋯O and C—H⋯N hydrogen bonds link the mol­ecules into chains along the b axis