A simulation approach for power calculation in large cohort studies based on multistate models

Realistic power calculations for large cohort studies and nested case control studies are essential for successfully answering important and complex research questions in epidemiology and clinical medicine. For this, we provide a methodical framework for general realistic power calculations via simulations that we put into practice by means of an R‐based template. We consider staggered recruitment and individual hazard rates, competing risks, interaction effects, and the misclassification of covariates. The study cohort is assembled with respect to given age‐, gender‐, and community distributions. Nested case‐control analyses with a varying number of controls enable comparisons of power with a full cohort analysis. Time‐to‐event generation under competing risks, including delayed study‐entry times, is realized on the basis of a six‐state Markov model. Incidence rates, prevalence of risk factors and prefixed hazard ratios allow for the assignment of age‐dependent transition rates given in the form of Cox models. These provide the basis for a central simulation‐algorithm, which is used for the generation of sample paths of the underlying time‐inhomogeneous Markov processes. With the inclusion of frailty terms into the Cox models the Markov property is specifically biased. An “individual Markov process given frailty” creates some unobserved heterogeneity between individuals. Different left‐truncation‐ and right‐censoring patterns call for the use of Cox models for data analysis. p‐values are recorded over repeated simulation runs to allow for the desired power calculations. For illustration, we consider scenarios with a “testing” character as well as realistic scenarios. This enables the validation of a correct implementation of theoretical concepts and concrete sample size recommendations against an actual epidemiological background, here given with possible substudy designs within the German National Cohort.     

Relationships among recent models for insect population dynamics with variable rates of development

A classification scheme for those population models which allow variation in development rates is proposed, based on two ways of modifying standard age-structured models. The resulting classes of models are termed development index models and sojourn time models. General formulations for the two classes of models are developed from two basic balance equations, and numerous specific models from the literature are shown to fit into the scheme. Concepts from competing risks theory are shown to be important in understanding the interplay between mortality and maturation. Relationships among the classes are investigated both for the most general forms of the models and for the simpler forms often used. The scheme can provide guidance in developing appropriate insect population models for specific modelling situations.Contribution 3878871     

A probabilistic approach to the construction of competing-risk life tables

"This paper aims to identify net and partial-crude probabilities in the competing-risk life table context, by using probabilistic approaches. Five types of lifelength random variables are defined to formulate these nonidentifiable probabilities. General expressions for net and partial-crude probabilities are first derived under independent risks assumptions. Two sets of explicit formulas for estimating the net and partial-crude probabilities are then derived in terms of the identifiable overall and crude probabilities by making the additional assumption of piecewise uniform distribution of the lifelength random variables. A study of the degree to which nonidentifiability can affect the net and partial-crude probabilities in a variety of situations is developed. An example from cross-sectional studies is employed to illustrate the methodology developed."     

Soil ingestion in children: Outdoor soil or indoor dust?

Soil ingestion estimates may play a prominent role in exposure estimation for risk assessments involving tightly bound soil contaminants such as dioxin, PCBs, and lead in soil. Since contamination is often localized to specific areas, the relative contribution of ingested soil due to outdoor soil and indoor dust may have a large impact on the risk assessment. This article examines data on 64 preschool children over 2 weeks to estimate the relative contribution of ingested soil from outdoor soil and indoor dust. Four principal methodological approaches are developed and presented to form the estimates, and their relative strengths and weaknesses are discussed.

The four approaches differ in their assumptions and their ability to detail differences in ingestion source. Two approaches (i.e., duration correlation method — approach 1 and group tracer ratio method — approach 2) were used that can only estimate the average ingestion source, where averages are calculated over subjects and weeks. Both of these approaches have sufficient limitations to preclude confidence in the resulting estimates.

The final two approaches (approach 3 — individual tracer ratio method and approach 4 — multiple statistical model method) were able to characterize ingestion source for individual subject‐weeks and offered more plausible estimates of soil ingestion. Greater emphasis is placed on approach 3 since it was biologically plausible and conceptually straightforward. Approach 3 indicated that 49.2% ± 29.2% of the residual fecal tracers were estimated to be of soil origin. These findings, which represent the first quantitative differentiation of soil vs. dust ingestion, have considerable application for a variety of environmental settings requiring exposure assessment.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

A critical look on CRISPR-based genome editing in plants

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing, derived from prokaryotic immunity system, is rapidly emerging as an alternative platform for introducing targeted alterations in genomes. The CRISPR-based tools have been deployed for several other applications including gene expression studies, detection of mutation patterns in genomes, epigenetic regulation, chromatin imaging, etc. Unlike the traditional genetic engineering approaches, it is simple, cost-effective, and highly specific in inducing genetic variations. Despite its popularity, the technology has limitations such as off-targets, low mutagenesis efficiency, and its dependency on in-vitro regeneration protocols for the recovery of stable plant lines. Several other issues such as persisted CRISPR activity in subsequent generations, the potential for transferring to its wild type population, the risk of reversion of edited version to its original phenotype particularly in cross-pollinated plant species when released into the environment and the scarcity of validated targets have been overlooked. This article briefly highlights these undermined aspects, which may challenge the wider applications of this platform for improving crop genetics.