Exploring the Interdependence of Couples' Rest‐Wake Cycles: An Actigraphic Study

Within western societies, it is commonplace for couples to share a bed. Yet there has been remarkably little research carried out on couples' sleep. This paper draws upon actigraphy, audio diary, and questionnaire data from both partners of 36 heterosexual couples (age 20–59 yrs) and aims to quantify the extent to which it is important to take into account the dyadic nature of sleep‐wake cycles. It achieves this through two interrelated aims: to use hierarchical linear models to measure dyadic interdependence in actigraphically recorded variables, and to investigate how much of this dyadic interdependence truly results from couple dynamics. The variables with the most significant couple interdependency were actual bed time, sleep latency, light/dark ratio, and wake bouts. The paper concludes by suggesting that interdependence may be the defining feature of couples' sleep, and that we need to employ analytic approaches that both acknowledge this and are sensitive to the possibilities that not all aspects of sleep will behave in the same way.     

Application of multivariate statistical techniques in microbial ecology

Recent advances in high‐throughput methods of molecular analyses have led to an explosion of studies generating large‐scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in‐depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high‐throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.     

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.     

Probabilistic prediction of unknown metabolic and signal-transduction networks.

Regulatory networks provide control over complex cell behavior in all kingdoms of life. Here we describe a statistical model, based on representing proteins as collections of domains or motifs, which predicts unknown molecular interactions within these biological networks. Using known protein-protein interactions of Saccharomyces cerevisiae as training data, we were able to predict the links within this network with only 7% false-negative and 10% false-positive error rates. We also use Markov chain Monte Carlo simulation for the prediction of networks with maximum probability under our model. This model can be applied across species, where interaction data from one (or several) species can be used to infer interactions in another. In addition, the model is extensible and can be analogously applied to other molecular data (e.g., DNA sequences).     

Dyadic associations between testosterone and relationship quality in couples

Testosterone is thought to be positively associated with “mating effort”, or the initiation and establishment of sexual relationships (Wingfield et al., 1990). Yet, because testosterone is negatively associated with nurturance (van Anders et al., 2011), high levels of testosterone may be incompatible with relationship maintenance. For instance, partnered men with high testosterone report lower relationship quality compared to partnered men with low testosterone (e.g., Booth and Dabbs, 1993). Findings for women are inconsistent, however, and even less is known about potential dyadic associations between testosterone and relationship quality in couples. In the current report, we assessed relationship satisfaction, commitment, and investment in heterosexual couples and tested the hypothesis that these aspects of relationship quality would be negatively associated with an individual's own and his/her partner's testosterone levels. We found that testosterone was in fact negatively associated with relationship satisfaction and commitment in both men and women. There was also evidence for dyadic associations: Participants' satisfaction and commitment were negatively related to their partners' levels of testosterone, and these associations were larger for women's than men's testosterone. Our findings are consistent with the idea that high testosterone may be incompatible with the maintenance of nurturant relationships. The current findings also provide some of the first evidence for dyadic associations between testosterone and relationship quality in couples, highlighting the interdependent nature of close relationship processes and the importance of considering this interdependence in social neuroendocrine research.     

Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using false discovery rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods.     

A Network-based Analysis of the 1861 Hagelloch Measles Data

Summary In this article, we demonstrate a statistical method for fitting the parameters of a sophisticated network and epidemic model to disease data. The pattern of contacts between hosts is described by a class of dyadic independence exponential-family random graph models (ERGMs), whereas the transmission process that runs over the network is modeled as a stochastic susceptible-exposed-infectious-removed (SEIR) epidemic. We fit these models to very detailed data from the 1861 measles outbreak in Hagelloch, Germany. The network models include parameters for all recorded host covariates including age, sex, household, and classroom membership and household location whereas the SEIR epidemic model has exponentially distributed transmission times with gamma-distributed latent and infective periods. This approach allows us to make meaningful statements about the structure of the population-separate from the transmission process-as well as to provide estimates of various biological quantities of interest, such as the effective reproductive number, R. Using reversible jump Markov chain Monte Carlo, we produce samples from the joint posterior distribution of all the parameters of this model-the network, transmission tree, network parameters, and SEIR parameters-and perform Bayesian model selection to find the best-fitting network model. We compare our results with those of previous analyses and show that the ERGM network model better fits the data than a Bernoulli network model previously used. We also provide a software package, written in R, that performs this type of analysis.     

Understanding primate communities: Recent developments and future directions

In 1999, the edited volume Primate Communities presented several studies that examined broad‐scale patterns of primate diversity.¹ Similar studies were being conducted on nonprimate taxa; advances in data availability and statistical approaches were allowing scientists to investigate a variety of new questions and to reexamine classical questions in novel ways. While such studies on nonprimate taxa have continued at a steady pace, they have only crept forward for primate species (Fig. 1 ). In the intervening time, the field of macroecology (Box 1) rapidly developed and has resulted in several books 2 - 4 and the establishment of new research institutes. We suggest that examining primate communities, especially in a macroecological context, is an important line of research for our field to embrace and an area where biological anthropologists can provide major contributions. We review the current state of research, describe new datasets and research tools, and suggest future research directions.     

Optimal Stimulus Coding by Neural Populations Using Rate Codes

We create a framework based on Fisher information for determining the most effective population coding scheme for representing a continuous-valued stimulus attribute over its entire range. Using this scheme, we derive optimal single- and multi-neuron rate codes for homogeneous populations using several statistical models frequently used to describe neural data. We show that each neuron's discharge rate should increase quadratically with the stimulus and that statistically independent neural outputs provides optimal coding. Only cooperative populations can achieve this condition in an informationally effective way.     

Hierarchical Analysis of Nucleotide Diversity in Geographically Structured Populations

Existing methods for analyzing nucleotide diversity require investigators to identify relevant hierarchical levels before beginning the analysis. We describe a method that partitions diversity into hierarchical components while allowing any structure present in the data to emerge naturally. We present an unbiased version of NEI's nucleotide diversity statistics and show that our modification has the same properties as WRIGHT's F(ST). We compare its statistical properties with several other F(ST) estimators, and we describe how to use these statistics to produce a rooted tree of relationships among the sampled populations in which the mean time to coalescence of haplotypes drawn from populations belonging to the same node is smaller than the mean time to coalescence of haplotypes drawn from populations belonging to different nodes. We illustrate the method by applying it to data from a recent survey of restriction site variation in the chloroplast genome of Coreopsis grandiflora.     

The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data.

Accurate estimation of the phases and amplitude of the endogenous circadian pacemaker from constant-routine core-temperature series is crucial for making inferences about the properties of the human biological clock from data collected under this protocol. This paper presents a set of statistical methods based on a harmonic-regression-plus-correlated-noise model for estimating the phases and the amplitude of the endogenous circadian pacemaker from constant-routine core-temperature data. The methods include a Bayesian Monte Carlo procedure for computing the uncertainty in these circadian functions. We illustrate the techniques with a detailed study of a single subject's core-temperature series and describe their relationship to other statistical methods for circadian data analysis. In our laboratory, these methods have been successfully used to analyze more than 300 constant routines and provide a highly reliable means of extracting phase and amplitude information from core-temperature data.     

Changes in blood chemistry and hematology variables during aging in captive rhesus macaques (Macaca mulatta). J Med Primatol 30:161-173, 2001

The Primate Aging Database (PAD) is being developed to assist research using nonhuman primate models for various gerontological applications. We provide now an update of an earlier report providing data on hematological and blood chemistry values for rhesus monkeys across the adult lifespan. These data were collected from several research colonies and have been submitted to rigorous statistical analyses to identify relationships with chronological age.     

FiSSE: A simple nonparametric test for the effects of a binary character on lineage diversification rates

It is widely assumed that phenotypic traits can influence rates of speciation and extinction, and several statistical approaches have been used to test for correlations between character states and lineage diversification. Recent work suggests that model‐based tests of state‐dependent speciation and extinction are sensitive to model inadequacy and phylogenetic pseudoreplication. We describe a simple nonparametric statistical test (“FiSSE”) to assess the effects of a binary character on lineage diversification rates. The method involves computing a test statistic that compares the distributions of branch lengths for lineages with and without a character state of interest. The value of the test statistic is compared to a null distribution generated by simulating character histories on the observed phylogeny. Our tests show that FiSSE can reliably infer trait‐dependent speciation on phylogenies of several hundred tips. The method has low power to detect trait‐dependent extinction but can infer state‐dependent differences in speciation even when net diversification rates are constant. We assemble a range of macroevolutionary scenarios that are problematic for likelihood‐based methods, and we find that FiSSE does not show similarly elevated false positive rates. We suggest that nonparametric statistical approaches, such as FiSSE, provide an important complement to formal process‐based models for trait‐dependent diversification.     

Adaptive trial designs: a review of barriers and opportunities

ABSTRACT: Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.     

HIV and dyadic intervention: an interdependence and communal coping analysis

Background

The most common form of HIV transmission in sub-Saharan Africa is heterosexual sex between two partners. While most HIV prevention interventions are aimed at the individual, there is mounting evidence of the feasibility, acceptability, and efficacy of dyadic interventions. However, the mechanisms through which dyadic-level interventions achieve success remain little explored. We address this gap by using Lewis et al’s interdependence model of couple communal coping and behaviour change to analyse data from partners participating in an HIV prevention trial in Uganda and Zambia.

Methods and Findings

We conducted a comparative qualitative study using in-depth interviews. Thirty-three interviews were conducted in total; ten with couples and twenty-three with staff members at the two sites. The Ugandan site recruited a sero-discordant couple cohort and the Zambian site recruited women alone. Spouses’ transformation of motivation is strong where couples are recruited and both partners stand to gain considerably by participating in the research; it is weaker where this is not the case. As such, coping mechanisms differ in the two sites; among sero-discordant couples in Uganda, communal coping is evidenced through joint consent to participate, regular couple counselling and workshops, sharing of HIV test results, and strong spousal support for adherence and retention. By contrast, coping at the Zambian site is predominantly left to the individual woman and occurs against a backdrop of mutual mistrust and male disenfranchisement. We discuss these findings in light of practical and ethical considerations of recruiting couples to HIV research.

Conclusions

We argue for the need to consider the broader context within which behaviour change occurs and propose that future dyadic research be situated within the framework of the ‘risk environment’.     

Statistical analysis of antigen receptor spectratype data

MOTIVATION: The effectiveness of vertebrate adaptive immunity depends crucially on the establishment and maintenance of extreme diversity in the antigen receptor repertoire. Spectratype analysis is a method used in clinical and basic immunological settings in which antigen receptor length diversity is assessed as a surrogate for functional diversity. The purpose of this paper is to describe the systematic derivation and application of statistical methods for the analysis of spectratype data. RESULTS: The basic probability model used for spectratype analysis is the multinomial model with n, the total number of counts, indeterminate. We derive the appropriate statistics and statistical procedures for testing hypotheses regarding differences in antigen receptor distributions and variable repertoire diversity in different treatment groups.We then apply these methods to spectratype data obtained from several healthy donors to examine the differences between normal CD4+ and CD8+ T cell repertoires, and to data from a thymus transplant patient to examine the development of repertoire diversity following the transplant.     

Applications of Survival Analysis to Aging Research

Many research questions in aging research treat time as the outcome variable. Researchers ask how much time must pass before a specific type of change or event occurs, and whether these times differ by characteristics of the subjects' background, training and treatment. Because of serious technical problems that arise when analyzing duration data, specially derived statistical methods – the methods of survival analysis – are required to answer the questions. In this paper, we introduce the conceptual framework of these new methodologies for analyzing duration data. We begin by identifying the types of research question that might appropriately treat time as an outcome. We then describe the new statistical methods for addressing such questions, provide a broad overview of their application, and identify relevant published sources containing additional background and technical information.