Weighted logrank tests for interval censored data when assessment times depend on treatment

A comparative study of two-sample tests for interval-censored data

Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can be used for assessing and comparing treatment effects over the control group. Alternatively, as commonly perceived, parametric tests can also be used assuming data are generated from a parametric family of distributions. To provide some guidance on choosing an appropriate method, in this paper, the performance of parametric tests and a series of nonparametric tests are compared through extensive simulation studies that cover a wide range of scenarios with varying sample sizes, varying censoring mechanisms and varying alternative hypotheses. For the purpose of illustration, we also apply these procedures to analyse three real datasets.

A nonparametric test for the association between longitudinal covariates and censored survival data

Many biomedical studies focus on the association between a longitudinal measurement and a time-to-event outcome while quantifying this association by means of a longitudinal-survival joint model. In this article we propose using the $LLR$ test - a longitudinal extension of the log-rank test statistic given by Peto and Peto (1972) - to provide evidence of a plausible association between a time-to-event outcome (right- or interval-censored) and a time-dependent covariate. As joint model methods are complex and hard to interpret, it is wise to conduct a preliminary test such as $LLR$ for checking the association between both processes. The $LLR$ statistic can be expressed in the form of a weighted difference of hazards, yielding a broad class of weighted log-rank test statistics known as $LWLR$, which allow a specific emphasis along the time axis of the effects of the time-dependent covariate on the survival. The asymptotic distribution of $LLR$ is derived by means of a permutation approach under the assumption that the censoring mechanism is independent of the survival time and the longitudinal covariate. A simulation study is conducted to evaluate the performance of the test statistics $LLR$ and $LWLR$, showing that the empirical size is close to the nominal significance level and that the power of the test depends on the association between the covariates and the survival time. A data set together with a toy example are used to illustrate the $LLR$ test. The data set explores the study Epidemiology of Diabetes Interventions and Complications (Sparling and others, 2006) which includes interval-censored data. A software implementation of our method is available on github (https://github.com/RamonOller/LWLRtest).

Survival trees for interval-censored survival data

Interval-censored data, in which the event time is only known to lie in some time interval, arise commonly in practice, for example, in a medical study in which patients visit clinics or hospitals at prescheduled times and the events of interest occur between visits. Such data are appropriately analyzed using methods that account for this uncertainty in event time measurement. In this paper, we propose a survival tree method for interval-censored data based on the conditional inference framework. Using Monte Carlo simulations, we find that the tree is effective in uncovering underlying tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and performs at least as well as (and in the presence of right-censoring outperforms) the Cox model when the true relationship is not linear. Further, the interval-censored tree outperforms survival trees based on imputing the event time as an endpoint or the midpoint of the censoring interval. We illustrate the application of the method on tooth emergence data.

Dominance from the perspective of gene-gene and gene-chemical interactions

In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

Incidence of active pulmonary tuberculosis in patients with coincident filarial and/or intestinal helminth infections followed longitudinally in South India

BACKGROUND

Filarial (and other helminth) infections are known to modulate mycobacteria-specific pro-inflammatory cytokine responses necessary for maintaining latency in tuberculosis (TB). We sought to address whether helminth co-infection alters progression to active pulmonary TB in a co-endemic area of South India.

METHODS/PRINCIPAL FINDINGS

Incidence of active pulmonary TB was assessed in 5096 subjects from five villages among helminth-infected (hel⁺) and -uninfected (hel⁻) groups. Baseline stool examinations, circulating filarial antigen, and tuberculin skin testing (PPD) were performed along with chest radiographs, sputum microscopy, and culture. During three follow-up visits each 2.5 years, patients were assessed using PPD tests and questionnaires and--for those with potential symptoms of TB--sputum microscopy and culture. Of the 5096 subjects, 1923 were found to be hel⁺ and 3173 were hel⁻. Follow up interval stool examination could not be performed. In each group, 21 developed active TB over the course of the study. After adjusting for sex, age, BCG vaccination status, and PPD positivity, no difference was seen in active TB incidence between hel⁺ and hel- groups either at baseline (relative risk (RR) 1.60; 95% confidence interval (CI): 0.69, 3.71, P = 0·27), or when followed prospectively (RR 1.24; 95% CI: 0.48, 3.18, P = 0·66).

CONCLUSIONS/SIGNIFICANCE

Our findings suggest that, despite the immunomodulatory effects of helminth infection, baseline co-morbid infection with these parasites had little effect on the clinical progression from latent to active pulmonary TB.

Hazard ratio estimation for two-sample case under interval-censored failure time data

This paper discusses two-sample comparison in the case of interval-censored failure time data. For the problem, one common approach is to employ some nonparametric test procedures, which usually give some p-values but not a direct or exact quantitative measure of the survival or treatment difference of interest. In particular, these procedures cannot provide a hazard ratio estimate, which is commonly used to measure the difference between the two treatments or samples. For interval-censored data, a few nonparametric test procedures have been developed, but it does not seem to exist as a procedure for hazard ratio estimation. Corresponding to this,we present two procedures for nonparametric estimation of the hazard ratio of the two samples for interval-censored data situations. They are generalizations of the corresponding procedures for right-censored failure time data. An extensive simulation study is conducted to evaluate the performance of the two procedures and indicates that they work reasonably well in practice. For illustration, they are applied to a set of interval-censored data arising from a breast cancer study.