Goodness-of-Fit Testing for a Regression Model With a Doubly Truncated Response.

In survival analysis and epidemiology, among other fields, interval sampling is often employed. With interval sampling, the individuals undergoing the event of interest within a calendar time interval are recruited. This results in doubly truncated event times.

Testing for an ignorable sampling bias under random double truncation.

In clinical and epidemiological research doubly truncated data often appear. This is the case, for instance, when the data registry is formed by interval sampling. Double truncation generally induces a sampling bias on the target variable, so proper corrections of ordinary estimation and inference procedures must be used.

Equalden.HD: An R Package for testing the equality of a high dimensional set of densities.

Background And Objective: Nowadays the "low sample size, large dimension" scenario is often encountered in genetics and in the omic sciences, where the microarray data is typically formed by a large number of possibly dependent small samples. Standard methods to solve the k-sample problem in such a setting are of limited applicability due to lack of theoretical validation for large k, lengthy computational times, missing software solutions, or inability to deal with statistical dependence among the samples. This paper presents the R package Equalden.

Assessment of assumptions of statistical analysis methods in randomised clinical trials: the what and how.

When analysing and presenting results of randomised clinical trials, trialists rarely report if or how underlying statistical assumptions were validated. To avoid data-driven biased trial results, it should be common practice to prospectively describe the assessments of underlying assumptions. In existing literature, there is no consensus on how trialists should assess and report underlying assumptions for the analyses of randomised clinical trials.

Nonparametric estimation of the cumulative incidences of competing risks under double truncation.

Registry data typically report incident cases within a certain calendar time interval. Such interval sampling induces double truncation on the incidence times, which may result in an observational bias. In this paper, we introduce nonparametric estimation for the cumulative incidences of competing risks when the incidence time is doubly truncated.

Assessing assumptions for statistical analyses in randomised clinical trials.

In order to ensure the validity of results of randomised clinical trials and under some circumstances to optimise statistical power, most statistical methods require validation of underlying statistical assumptions. The present paper describes how trialists in major medical journals report tests of underlying statistical assumptions when analysing results of randomised clinical trials. We also consider possible solutions how to improve current practice by adequate reporting of tests of underlying statistical assumptions.

Goodness-of-fit tests for disorder detection in NGS experiments.

Next-generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high-dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness-of-fit procedure for disorder detection is derived.

Nonparametric estimation of transition probabilities for a general progressive multi-state model under cross-sectional sampling.

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information before the truncation time, which, when exploited, increases efficiency.

Inverse probability weighted Cox regression for doubly truncated data.

Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software.

Pillar[5]arene-Based Supramolecular Plasmonic Thin Films for Label-Free, Quantitative and Multiplex SERS Detection.

Novel plasmonic thin films based on electrostatic layer-by-layer (LbL) deposition of citrate-stabilized Au nanoparticles (NPs) and ammonium pillar[5]arene (AP[5]A) have been developed. The supramolecular-induced LbL assembly of the plasmonic nanoparticles yields the formation of controlled hot spots with uniform interparticle distances. At the same time, this strategy allows modulating the density and dimensions of the Au aggregates, and therefore the optical response, on the thin film with the number of AuNP-AP[5]A deposition cycles.

Direct modeling of regression effects for transition probabilities in the progressive illness-death model.

In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness-death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score equations that are able to remove the bias due to censoring are introduced.

Relationship between damage clustering and mortality in systemic lupus erythematosus in early and late stages of the disease: cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry.

Objectives: To identify patterns (clusters) of damage manifestations within a large cohort of SLE patients and evaluate the potential association of these clusters with a higher risk of mortality.

Methods: This is a multicentre, descriptive, cross-sectional study of a cohort of 3656 SLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index.

Methods for testing the Markov condition in the illness-death model: a comparative study.

Markov three-state progressive and illness-death models are often used in biomedicine for describing survival data when an intermediate event of interest may be observed during the follow-up. However, the usual estimators for Markov models (e.g.

An extended sequential goodness-of-fit multiple testing method for discrete data.

The sequential goodness-of-fit (SGoF) multiple testing method has recently been proposed as an alternative to the familywise error rate- and the false discovery rate-controlling procedures in high-dimensional problems. For discrete data, the SGoF method may be very conservative. In this paper, we introduce an alternative SGoF-type procedure that takes into account the discreteness of the test statistics.

Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.

Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov.

Adjusted p-values for SGoF multiple test procedure.

In the field of multiple comparison procedures, adjusted p-values are an important tool to evaluate the significance of a test statistic while taking the multiplicity into account. In this paper, we introduce adjusted p-values for the recently proposed Sequential Goodness-of-Fit (SGoF) multiple test procedure by letting the level of the test vary on the unit interval. This extends previous research on the SGoF method, which is a method of high interest when one aims to increase the statistical power in a multiple testing scenario.

Estimation of a monotone percentile residual life function under random censorship.

In this paper, we introduce a new estimator of a percentile residual life function with censored data under a monotonicity constraint. Specifically, it is assumed that the percentile residual life is a decreasing function. This assumption is useful when estimating the percentile residual life of units, which degenerate with age.

A nonparametric test for Markovianity in the illness-death model.

Multistate models are useful tools for modeling disease progression when survival is the main outcome, but several intermediate events of interest are observed during the follow-up time. The illness-death model is a special multistate model with important applications in the biomedical literature. It provides a suitable representation of the individual's history when a unique intermediate event can be experienced before the main event of interest.

The Beta-Binomial SGoF method for multiple dependent tests.

In this paper a correction of SGoF multitesting method for dependent tests is introduced. The correction is based in the beta-binomial model, and therefore the new method is called Beta- Binomial SGoF (or BB-SGoF). Main properties of the new method are established, and its practical implementation is discussed.

Testing Markovianity in the three-state progressive model via future-past association.

The three-state progressive model is a special multi-state model with important applications in Survival Analysis. It provides a suitable representation of the individual's history when an intermediate event (with a possible influence on the survival prognosis) is experienced before the main event of interest. Estimation of transition probabilities in this and other multi-state models is usually performed through the Aalen-Johansen estimator.

Assessing significance in high-throughput experiments by sequential goodness of fit and q-value estimation.

We developed a new multiple hypothesis testing adjustment called SGoF+ implemented as a sequential goodness of fit metatest which is a modification of a previous algorithm, SGoF, taking advantage of the information of the distribution of p-values in order to fix the rejection region. The new method uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. This new approach shows a better power/pFDR ratio than SGoF.

A semiparametric estimator of the bivariate distribution function for censored gap times.

Let (T(1), T(2)) be gap times corresponding to two consecutive events, which are observed subject to random right-censoring. In this paper, a semiparametric estimator of the bivariate distribution function of (T(1), T(2)) and, more generally, of a functional E [φ(T(1),T(2))] is proposed. We assume that the probability of censoring for T(2) given the (possibly censored) gap times belongs to a parametric family of binary regression curves.

'SGoFicance Trace': assessing significance in high dimensional testing problems.

Recently, an exact binomial test called SGoF (Sequential Goodness-of-Fit) has been introduced as a new method for handling high dimensional testing problems. SGoF looks for statistical significance when comparing the amount of null hypotheses individually rejected at level γ = 0.05 with the expected amount under the intersection null, and then proceeds to declare a number of effects accordingly.

A semiparametric estimator of survival for doubly truncated data.

Doubly truncated data are often encountered in the analysis of survival times, when the sample reduces to those individuals with terminating event falling on a given observational window. In this paper we assume that some information about the bivariate distribution function (df) of the truncation times is available. More specifically, we represent this information by means of a parametric model for the joint df of the truncation times.