Nonparametric rank-based methods for group sequential monitoring of paired censored survival data

This research gives methods for nonparametric sequential monitoring of paired censored survival data in the two-sample problem using paired weighted log-rank statistics with adjustments for dependence in survival and censoring outcomes. The joint asymptotic closed-form distribution of these sequentially monitored statistics has a dependent increments structure. Simulations validating operating characteristics of the proposed methods highlight power and size consequences of ignoring even mildly correlated data. A motivating example is presented via the Early Treatment Diabetic Retinopathy Study.     

Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches

A general method is presented integrating the concept of adaptive interim analyses into classical group sequential testing. This allows the researcher to represent every group sequential plan as an adaptive trial design and to make design changes during the course of the trial after every interim analysis in the same way as with adaptive designs. The concept of adaptive trial designing is thereby generalized to a large variety of possible sequential plans.     

Thermoluminescence characteristics of calcite with a Gaussian process regression model of machine learning

Thermoluminescence (TL) is defined as a luminescence phenomenon that can be detected when an insulator or semiconductor is thermally stimulated. Defective crystals store radiation until they are stimulated. Thermoluminescence is a method of monitoring the absorbed dose of dosimeters. The irradiation crystal is heated to 500°C to display the absorbed dose as a luminescent light. The TL dosimetric properties of calcite obtained from nature were investigated in this study. Machine learning was also examined using Gaussian process regression (GPR) for stimulated TL characteristics. According to the experimental output, the TL glow curve had two main peaks located at 90°C and 240°C with good dosimetric properties. In the four regression models of GPR, the data for the heating rate of 3°C s⁻¹ have the lowest residual.     

Credentialing of vertebral stereotactic ablative body radiotherapy in a multi-centre trial

Purpose/objectiveStereotactic ablative body radiotherapy (SABR) in multi-centre trials requires rigorous quality assurance to ensure safe and consistent treatment for all trial participants. We report results of vertebral SABR dosimetry credentialing for the ALTG/TROG NIVORAD trial.Material/methodsCentres with a previous SABR site visit performed axial film measurement of the benchmarking vertebral plan in a local phantom and submitted radiochromic film images for analysis. Remaining centres had on-site review of SABR processes and axial film measurement of the vertebral benchmarking plan. Films were analysed for dosimetric and positional accuracy: gamma analysis (>90% passing 2%/2mm/10% threshold) and ≤ 1 mm positional accuracy at target-cord interface was required.Results19 centres were credentialed; 11 had on-site measurement. Delivery devices included linear accelerator, TomoTherapy and CyberKnife systems. Five centres did not achieve 90% gamma passing rate. Of these, three were out of tolerance (OOT) in low (<5Gy) dose regions and > 80% passing rate and deemed acceptable. Two were OOT over the full dose range: one elected not to remeasure; the other also had positional discrepancy greater than 1 mm and repeat measurement with a new plan was in tolerance. The original OOT was attributed to inappropriate MLC constraints. All centres delivered planned target-cord dose gradient within 1 mm.ConclusionCredentialing measurements for vertebral SABR in a multi-centre trial showed although the majority of centres delivered accurate vertebral SABR, there is high value in independent audit measurements. One centre with inappropriate MLC settings was detected, which may have resulted in delivery of clinically unacceptable vertebral SABR plans.     

Assessing Toxicities in a Clinical Trial: Bayesian Inference for Ordinal Data Nested within Categories

Summary This article addresses modeling and inference for ordinal outcomes nested within categorical responses. We propose a mixture of normal distributions for latent variables associated with the ordinal data. This mixture model allows us to fix without loss of generality the cutpoint parameters that link the latent variable with the observed ordinal outcome. Moreover, the mixture model is shown to be more flexible in estimating cell probabilities when compared to the traditional Bayesian ordinal probit regression model with random cutpoint parameters. We extend our model to take into account possible dependence among the outcomes in different categories. We apply the model to a randomized phase III study to compare treatments on the basis of toxicities recorded by type of toxicity and grade within type. The data include the different (categorical) toxicity types exhibited in each patient. Each type of toxicity has an (ordinal) grade associated to it. The dependence among the different types of toxicity exhibited by the same patient is modeled by introducing patient‐specific random effects.     

Testing a Partial Ordering of Population Means with Application to Inference about Growth Habits of Cowpea Genotypes

Using general results available in the literature, we derive the likelihood ratio test for a particular partial ordering of means that naturally arises in a biological context. We then show that the conceptual and computational complexity of the derivation can be substantially reduced by equivalently deriving the test using the intersection-union principle for decomposing a complex null hypothesis into elemental forms. A Monte Carlo algorithm for obtaining the p-value of the test is proposed. The test procedure is illustrated with a data set of the competitive ability of several cowpea genotypes, where previous experiments have indicated the proposed partial order of the means. A simulation study is used to examine the power of the test.