Cancer: Towards a general theory of the target

General theories (GT) are reductionist explications of apparently independent facts. Here, in reviewing the literature, I develop a GT to simplify the cluttered landscape of cancer therapy targets by revealing they cluster parsimoniously according to only a few underlying principles. The first principle is that targets can be only exploited by either or both of two fundamentally different approaches: causality‐inhibition, and ‘acausal’ recognition of some marker or signature. Nonetheless, each approach must achieve both of two separate goals, efficacy (reduction in cancer burden) and selectivity (sparing of normal cells); if the mechanisms are known, this provides a definition of rational treatment. The second principle is target fragmentation, whereby the target may perform up to three categoric functions (cytoreduction, modulation, cytoprotection), potentially mediated by physically different target molecules, even on different cell types, or circulating freely. This GT remains incomplete until the minimal requirements for cure, or alternatively, proof that cure is impossible, become predictable.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

生态保护项目绩效评估的技术方法体系

自20世纪90年代以来,以各种形式的生态补偿政策和工程项目为代表的生态保护项目在全球广泛实施。1998年长江大洪水之后,我国也陆续实施了天然林保护工程、退耕还林还草工程、京津风沙源治理工程、森林生态效益补偿基金、退田还湖还湿工程、生态转移支付等一系列生态保护项目。近年来,政府、研究人员和社会各界越来越重视这些项目产生的生态、经济和社会效益。不过,我国生态保护项目绩效评估还处于起步阶段,主要体现在定量化不足、研究设计不严密、基准线选取不规范、评估方法过于简易、因果推理证据不足、评估结果可信度较差。不管是从学术研究、国家战略,还是实际生态管理的角度,都迫切需要推动和完善我国生态保护项目的绩效评估。结合过去十多年的理论和案例研究经验,对绩效评估的研究设计、基准线选取、评估方法、以及评估分析中面临的问题和挑战进行了系统地归纳与梳理,以期为进一步开展生态保护项目绩效评估提供技术参考。     

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.     

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.     

Selection on QTL and complex traits in complex environments

Understanding genetic variation for complex traits in heterogeneous environments is a fundamental problem in biology. In this issue of Molecular Ecology, Fournier‐Level et al. ( 2013 ) analyse quantitative trait loci (QTL) influencing ecologically important phenotypes in mapping populations of Arabidopsis thaliana grown in four habitats across its native European range. They used causal modelling to quantify the selective consequences of life history and morphological traits and QTL on components of fitness. They found phenology QTL colocalizing with known flowering time genes as well as novel loci. Most QTL influenced fitness via life history and size traits, rather than QTL having direct effects on fitness. Comparison of phenotypes among environments found no evidence for genetic trade‐offs for phenology or growth traits, but genetic trade‐offs for fitness resulted because flowering time had opposite fitness effects in different environments. These changes in QTL effects and selective consequences may maintain genetic variation among populations.     

Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal

Landscape resistance reflects how difficult it is for genes to move across an area with particular attributes (e.g. land cover, slope). An increasingly popular approach to estimate resistance uses Mantel and partial Mantel tests or causal modelling to relate observed genetic distances to effective distances under alternative sets of resistance parameters. Relatively few alternative sets of resistance parameters are tested, leading to relatively poor coverage of the parameter space. Although this approach does not explicitly model key stochastic processes of gene flow, including mating, dispersal, drift and inheritance, bias and precision of the resulting resistance parameters have not been assessed. We formally describe the most commonly used model as a set of equations and provide a formal approach for estimating resistance parameters. Our optimization finds the maximum Mantel r when an optimum exists and identifies the same resistance values as current approaches when the alternatives evaluated are near the optimum. Unfortunately, even where an optimum existed, estimates from the most commonly used model were imprecise and were typically much smaller than the simulated true resistance to dispersal. Causal modelling using Mantel significance tests also typically failed to support the true resistance to dispersal values. For a large range of scenarios, current approaches using a simple correlational model between genetic and effective distances do not yield accurate estimates of resistance to dispersal. We suggest that analysts consider the processes important to gene flow for their study species, model those processes explicitly and evaluate the quality of estimates resulting from their model.