Calculating power for the Finkelstein and Schoenfeld test statistic for a composite endpoint with two components.

The Finkelstein and Schoenfeld (FS) test is a popular generalized pairwise comparison approach to analyze prioritized composite endpoints (eg, components are assessed in order of clinical importance). Power and sample size estimation for the FS test, however, are generally done via simulation studies. This simulation approach can be extremely computationally burdensome, compounded by increasing number of composite endpoints and with increasing sample size. Here we propose an analytical solution to calculate power and sample size for commonly encountered two-component hierarchical composite endpoints. The power formulas are derived assuming underlying distributions in each of the component outcomes on the population level, which provide a computationally efficient and practical alternative to the standard simulation approach. Monte Carlo simulation results demonstrate that performance of the proposed power formulas are consistent with that of the simulation approach, and have generally desirable objective properties including robustness to mis-specified distributional assumptions. We demonstrate the application of the proposed formulas by calculating power and sample size for the Transthyretin Amyloidosis Cardiomyopathy Clinical Trial.

Methods of assessing categorical agreement between correlated screening tests in clinical studies.

Advances in breast imaging and other screening tests have prompted studies to evaluate and compare the consistency between experts' ratings of existing with new screening tests. In clinical settings, medical experts make subjective assessments of screening test results such as mammograms. Consistency between experts' ratings is evaluated by measures of inter-rater agreement or association. However, conventional measures, such as Cohen's and Fleiss' kappas, are unable to be applied or may perform poorly when studies consist of many experts, unbalanced data, or dependencies between experts' ratings exist. Here we assess the performance of existing approaches including recently developed summary measures for assessing the agreement between experts' binary and ordinal ratings when patients undergo two screening procedures. Methods to assess consistency between repeated measurements by the same experts are also described. We present applications to three large-scale clinical screening studies. Properties of these agreement measures are illustrated via simulation studies. Generally, a model-based approach provides several advantages over alternative methods including the ability to flexibly incorporate various measurement scales (i.e. binary or ordinal), large numbers of experts and patients, sparse data, and robustness to prevalence of underlying disease.

Predictors of Loss to Follow-Up among Men with Tuberculosis in Puducherry and Tamil Nadu, India.

Identifying predictors of loss to follow-up (LTFU; treatment lapse ≥ 2 months) among people with tuberculosis (TB) may assist programmatic efforts in controlling the spread of TB. Newly diagnosed smear-positive TB patients were enrolled in the Regional Prospective Observational Research for TB study in Puducherry and Tamil Nadu, India. Treatment records were used to identify LTFU of those who were enrolled from May 2014 through December 2017. This nested case-control study evaluated male TB patients. Predictors were assessed using multivariable logistic regression. Of 425 men with TB, 82 (19%) were LTFU. In the adjusted analyses of males, divorced/separated marital status (adjusted odds ratio [aOR] 3.80; 95% CI: 1.39-10.38) and at-risk alcohol use (aOR 1.92; 95% CI: 1.12-3.27) were significant predictors for increased risk of LTFU, and diabetes was a significant predictor for decreased risk of LTFU (aOR 0.52; 95% CI: 0.29-0.92). Of 53 men with recorded date of last treatment visit, 23 (43%) and 43 (81%) had LTFU within the first 2 and first 4 months of treatment, respectively. Addressing at-risk alcohol use and providing more intensive follow-up could lead to improved treatment completion.

Measuring intrarater association between correlated ordinal ratings.

Variability between raters' ordinal scores is commonly observed in imaging tests, leading to uncertainty in the diagnostic process. In breast cancer screening, a radiologist visually interprets mammograms and MRIs, while skin diseases, Alzheimer's disease, and psychiatric conditions are graded based on clinical judgment. Consequently, studies are often conducted in clinical settings to investigate whether a new training tool can improve the interpretive performance of raters. In such studies, a large group of experts each classify a set of patients' test results on two separate occasions, before and after some form of training with the goal of assessing the impact of training on experts' paired ratings. However, due to the correlated nature of the ordinal ratings, few statistical approaches are available to measure association between raters' paired scores. Existing measures are restricted to assessing association at just one time point for a single screening test. We propose here a novel paired kappa to provide a summary measure of association between many raters' paired ordinal assessments of patients' test results before versus after rater training. Intrarater association also provides valuable insight into the consistency of ratings when raters view a patient's test results on two occasions with no intervention undertaken between viewings. In contrast to existing correlated measures, the proposed kappa is a measure that provides an overall evaluation of the association among multiple raters' scores from two time points and is robust to the underlying disease prevalence. We implement our proposed approach in two recent breast-imaging studies and conduct extensive simulation studies to evaluate properties and performance of our summary measure of association.

Heightened brain response to pain anticipation in high-functioning adults with autism spectrum disorder.

Autism spectrum disorder (ASD) is marked by both socio-communicative difficulties and abnormalities in sensory processing. Much of the work on sensory deficits in ASD has focused on tactile sensations and the perceptual aspects of somatosensation, such as encoding of stimulus intensity and location. Although aberrant pain processing has often been noted in clinical observations of patients with ASD, it remains largely uninvestigated. Importantly, the neural mechanism underlying higher order cognitive aspects of pain processing such as pain anticipation also remains unknown. Here we examined both pain perception and anticipation in high-functioning adults with ASD and matched healthy controls (HC) using an anticipatory pain paradigm in combination with functional magnetic resonance imaging (fMRI) and concurrent skin conductance response (SCR) recording. Participants were asked to choose a level of electrical stimulation that would feel moderately painful to them. Compared to HC group, ASD group chose a lower level of stimulation prior to fMRI. However, ASD participants showed greater activation in both rostral and dorsal anterior cingulate cortex during the anticipation of stimulation, but not during stimulation delivery. There was no significant group difference in insular activation during either pain anticipation or perception. However, activity in the left anterior insula correlated with SCR during pain anticipation. Taken together, these results suggest that ASD is marked with aberrantly higher level of sensitivity to upcoming aversive stimuli, which may reflect abnormal attentional orientation to nociceptive signals and a failure in interoceptive inference.