Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.Comment: Published at http://dx.doi.org/10.1214/088342306000000330 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

SPCalc: A Web-based Calculator for Sample Size and Power Calculations in Micro-array Studies

Calculation of the appropriate sample size in planning microarray studies is important because sample collection can be expensive and time-consuming. Sample-size calculation is also a challenging issue for microarray studies because the number of genes is far larger than the number of samples so that traditional methods of sample-size calculation cannot be directly applied. To help investigators answer the question of how many samples are needed in their microarray studies, we developed a user-friendly web-based calculator, SPCalc, for calculating sample size and power for a variety of commonly used experimental designs, including completely randomized treatment control design, matched-pairs design, multiple-treatment design having an isolated treatment effect, and randomized block design

Threshold regression with threg

In this presentation, I introduce a new Stata command called threg. The command estimates regression coefficients of a threshold regression model based on the first hitting time of a boundary by the sample path of a Wiener diffusion process. The regression methodology is well suited to applications involving survival and time-to-event data. This new command uses the MLE routine in Stata for calculating regression coefficient estimates, asymptotic standard errors, and p-values. An initialization option is also allowed, as in the conventional MLE routine. The threg command can be carried out with either calendar or analytical time scales. Hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates) can also be calculated by this command. Furthermore, curves of estimated hazard functions, survival functions, and probability distribution functions of the first hitting time can be plotted. Function curves corresponding to different scenarios can be overlaid in the same plot for a comparative analysis to give added research insights.

Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank

Wilcoxon rank-based tests are distribution-free alternatives to the popular two-sample and paired t tests. For independent data, they are available in several R packages such as stats and coin. For clustered data, in spite of the recent methodological developments, there did not exist an R package that makes them available at one place. We present a package clusrank where the latest developments are implemented and wrapped under a unified user-friendly interface. With different methods dispatched based on the inputs, this package offers great flexibility in rank-based tests for various clustered data. Exact tests based on permutations are also provided for some methods. Details of the major schools of different methods are briefly reviewed. Usages of the package clusrank are illustrated with simulated data as well as a real dataset from an ophthalmological study. The package also enables convenient comparison between selected methods under settings that have not been studied before and the results are discussed

Arguments

Description This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. License LGPL biocViews Microarray R topics documented: power.matched....................................... 2 power.multi......................................... 3 power.randomized...................................... 4 sampleSize.matched..................................... 5 sampleSize.randomized...................................

2-(3-Morpholino­prop­yl)-2,3-dihydro-1H-pyrrolo­[3,4-b]quinolin-1-one monohydrate

In the title compound, C18H21N3O2·H2O, the fused-ring system is approximately planar [maximum atomic deviation = 0.028 (3) Å]; the morpholine ring displays a chair conformation. The crystal packing is stabilized by classical inter­molecular O—H⋯O and O—H⋯N hydrogen bonds and weak C—H⋯O hydrogen bonds between the organic mol­ecules and the water mol­ecules

Mindfulness-based cognitive therapy v. group psychoeducation for people with generalised anxiety disorder: randomised controlled trial

Background: Research suggests that an 8-week mindfulness-based cognitive therapy (MBCT) course may be effective for generalised anxiety disorder (GAD). Aims: To compare changes in anxiety levels among participants with GAD randomly assigned to MBCT, cognitive–behavioural therapy-based psychoeducation and usual care. Method: In total, 182 participants with GAD were recruited (trial registration number: CUHK_CCT00267) and assigned to the three groups and followed for 5 months after baseline assessment with the two intervention groups followed for an additional 6 months. Primary outcomes were anxiety and worry levels. Results: Linear mixed models demonstrated significant group × time interaction (F(4,148) = 5.10, P = 0.001) effects for decreased anxiety for both the intervention groups relative to usual care. Significant group × time interaction effects were observed for worry and depressive symptoms and mental health-related quality of life for the psychoeducation group only. Conclusions: These results suggest that both of the interventions appear to be superior to usual care for the reduction of anxiety symptoms