One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Robust inference for the stepped wedge design

Stepped wedge designed trials are a type of cluster-randomized study in which the intervention is introduced to each cluster in a random order over time. This design is often used to assess the effect of a new intervention as it is rolled out across a series of clinics or communities. Based on a permutation argument, we derive a closed-form expression for an estimate of the intervention effect, along with its standard error, for a stepped wedge design trial. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data-generating mechanism, thereby providing a robust approach to inference for the intervention effect in stepped wedge designs. We use simulations to evaluate the type 1 error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The limitations, possible extensions, and open problems regarding the method are discussed.     

Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is “partial interference,” in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right-censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two-sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.     

Phylogenetics of Escallonia (Escalloniaceae) based on plastid DNA sequence data

Escallonia (Escalloniaceae) is a New World genus of c. 39 species distributed mainly in the South American highlands. Plastid DNA sequence data from the intergenic spacers trnS‐trnG and 3′ trnV‐ndhC and the ndhF gene for 32 species were used to examine the relationships among species and related genera and to analyse the relationship between phylogeny and the geographical distribution of the species. Maximum parsimony and Bayesian inference were employed to analyse the data. The sister relationship of Escallonia to Forgesia and Valdivia was corroborated. We recovered five strongly supported clades that are geographically structured, suggesting that the evolutionary history of the genus may be linked to historical processes, including the uplift of mountainous systems in South America. © 2013 The Linnean Society of London, Botanical Journal of the Linnean Society, 2013, 173 , 442–451.     

Do species of Neacratus with anchor-shaped sclerite of endophallus really belong to Neacratus (Coleoptera: Brentidae: Acratini)?

A small group of nine nominal species belonging to the genus Neacratus Alonso-Zarazaga, Lyal, Sforzi & Bartolozzi, 1999 is studied in detail from a morphological point of view. Lectotypes are designated for Brentus obtusus Lund, 1800 and Nemocephalus fulgidus Kleine, 1928. Three new synonymies are proposed: Nemocephalus brevicostatus Kleine, 1922 n. syn. for Brenthus obtusus Lund, 1800, Nemocephalus longiceps Perroud, 1853 n. syn. for Brenthus famulus Boheman, 1840, and Nemocephalus fulgidus Kleine, 1928 n. syn. for B. famulus Boheman, 1840. A new species, Neacratus pascali n. sp., is described from French Guiana. New country records are provided for Neacratus obtusus (Bolivia, Ecuador, French Guiana, Grenada, Peru, Trinidad and Tobago, Venezuela), N. puncticeps (Sharp, 1895) (Colombia, Ecuador, Venezuela), N. guatemalensis (Senna, 1893) (Belize, Colombia, El Salvador), N. deplanatus (Sharp, 1895) (Colombia, Costa Rica, French Guiana, Mexico, Panama, Venezuela) and N. famulus (Paraguay). A phylogenetic analysis carried out on this group shows it forms a monophyletic lineage included in a clade containing most of other species of Neacratus, of which it is the type species; it is therefore not justified to create a new generic name. The possible polyphyletism of the genus Neacratus as a whole and the development of an excessively long rostrum in some male Acratini are discussed.     

On the limitations of standard statistical modeling in biological systems: A full Bayesian approach for biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.