Randomization-based analysis of covariance for inference in the sequential parallel comparison design.

The sequential parallel comparison design (SPCD), with sequence groups P:P, P:T, and T:T, together with the exclusion of the second-period information from placebo responders in the first period, can serve usefully for studies with highly favorable placebo response, for example, psychiatric clinical trials. This paper presents a methodology for the first-period treatment difference in the overall population and the second-period treatment difference in the placebo nonresponders for the first period, as well as other available sources of information that could be of potential interest. Without any assumptions, a hypothesis testing method is proposed based on the randomization distribution of comparisons using the covariance structure for the randomized population under the null hypothesis to control type I error. Randomization-based analysis of covariance (ANCOVA) is introduced to adjust for baseline and for the observations that serve as baselines for the second period. Related methods are proposed for the study population as a simple random sample of an almost infinite population. The statistical properties of the proposed methods are described with simulation studies; and the use of the methods is illustrated for an example based on the data from the ADAPT-A clinical trial.

Sample size re-estimation and other midcourse adjustments with sequential parallel comparison design.

Sequential parallel comparison design (SPCD) was proposed to reduce placebo response in a randomized trial with placebo comparator. Subjects are randomized between placebo and drug in stage 1 of the trial, and then, placebo non-responders are re-randomized in stage 2. Efficacy analysis includes all data from stage 1 and all placebo non-responding subjects from stage 2. This article investigates the possibility to re-estimate the sample size and adjust the design parameters, allocation proportion to placebo in stage 1 of SPCD, and weight of stage 1 data in the overall efficacy test statistic during an interim analysis.

Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software.

PURPOSE: We determine whether the automated segmentation software, Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP), can measure, in a platform-independent manner, retinal thickness on Cirrus and Spectralis spectral domain optical coherence tomography (SD-OCT) images in eyes with diabetic macular edema (DME) under treatment in a clinical trial. METHODS: Automatic segmentation software was used to segment the internal limiting membrane (ILM), inner retinal pigment epithelium (RPE), and Bruch's membrane (BM) in SD-OCT images acquired by Cirrus and Spectralis commercial systems, from the same eye, on the same day during a clinical interventional DME trial. Mean retinal thickness differences were compared across commercial and DOCTRAP platforms using intraclass correlation (ICC) and Bland-Altman plots. RESULTS: The mean 1 mm central subfield thickness difference (standard error [SE]) comparing segmentation of Spectralis images with DOCTRAP versus HEYEX was 0.7 (0.3) µm (0.2 pixels). The corresponding values comparing segmentation of Cirrus images with DOCTRAP versus Cirrus software was 2.2 (0.7) µm. The mean 1 mm central subfield thickness difference (SE) comparing segmentation of Cirrus and Spectralis scan pairs with DOCTRAP using BM as the outer retinal boundary was -2.3 (0.9) µm compared to 2.8 (0.9) µm with inner RPE as the outer boundary. CONCLUSIONS: DOCTRAP segmentation of Cirrus and Spectralis images produces validated thickness measurements that are very similar to each other, and very similar to the values generated by the corresponding commercial software in eyes with treated DME. TRANSLATIONAL RELEVANCE: This software enables automatic total retinal thickness measurements across two OCT platforms, a process that is impractical to perform manually.

Defining the Incremental Utility of Prostate Multiparametric Magnetic Resonance Imaging at Standard and Specialized Read in Predicting Extracapsular Extension of Prostate Cancer.

UNLABELLED: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used in staging early prostate cancer (PCa) but remains heavily reader-dependent. We aim to define the incremental utility of mpMRI over clinical parameters in determining the pathologic extracapsular extension (pECE) of PCa interpreted in a standard radiologic setting and when further over-read by a specialized reader. We retrospectively reviewed 120 men with clinically localized PCa undergoing mpMRI and radical prostatectomy. We obtained radiologic prediction of pECE from standard radiologic reports (standard read) and by a specialized reader blinded to clinical and pathologic findings (specialized read). We determined the incremental benefit of standard read and specialized read by sequential addition to a baseline clinical parameters-only logistic regression model predicting pECE. The sensitivity and specificity of standard read were 77% and 44%, respectively, whereas those of specialized read were 86% and 81%. The positive likelihood ratio was 1.7 at baseline, 1.7 adding standard read, and 6.5 adding specialized read. The negative likelihood ratio was 0.6 at baseline, 0.5 adding standard read, and 0.1 adding specialized read. Standard read modestly improved prediction of pECE, whereas specialized read improved it moderately. PATIENT SUMMARY: The incremental benefit of mpMRI over clinical information is small but increases to moderate with a specialized second opinion. This second opinion may be useful when considering active surveillance, nerve-sparing surgery, or focal therapy.