On the use of historical control data for trend test in carcinogenicity studies

Historical control data are often available in carcinogenicity studies and are included for testing dose effects in current studies. A new method is developed for incorporating the historical control information into a dose effect test. The method generalizes the test procedures proposed by Tarone (1982, Biometrics 38, 215-220) and Ibrahim and Ryan (1996, Biometrics 52, 1478-1485) by taking into account the variation resulting from parameter estimation based on historical data. Two examples are discussed for illustrating the proposed method.     

Estimating age‐dependent survival from age‐aggregated ringing data—extending the use of historical records

Bird ring‐recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid‐20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring‐recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age‐dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651–795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring‐recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651–795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring‐recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.     

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.     

Modelling climate response to historical land cover change

In order to estimate the effect of historical land cover change (deforestation) on climate, we perform a set of experiments with a climate system model of intermediate complexity – CLIMBER-2. We focus on the biophysical effect of the land cover change on climate and do not explicitly account for the biogeochemical effect. A dynamic scenario of deforestation during the last millennium is formulated based on the rates of land conversion to agriculture. The deforestation scenario causes a global cooling of 0.35 °C with a more notable cooling of the northern hemisphere (0.5 °C). The cooling is most pronounced in the northern middle and high latitudes, especially during the spring season. To compare the effect of deforestation on climate with other forcings, climate responses to the changing atmospheric CO₂ concentration and solar irradiance are also analysed. When all three factors are taken into account, dynamics of northern hemisphere temperature during the last 300 years within the model are generally in agreement with the observed (reconstructed) temperature trend. We conclude that the impact of historical land cover changes on climate is comparable with the impact of the other climate forcings and that land cover forcing is important for reproducing historical climate change.     

Month 2 Culture Status and Treatment Duration as Predictors of Tuberculosis Relapse Risk in a Meta-Regression Model

Background

New drugs and regimens with the potential to transform tuberculosis treatment are presently in early stage clinical trials.

Objective

The goal of the present study was to infer the required duration of these treatments.

Method

A meta-regression model was developed to predict relapse risk using treatment duration and month 2 sputum culture positive rate as predictors, based on published historical data from 24 studies describing 58 regimens in 7793 patients. Regimens in which rifampin was administered for the first 2 months but not subsequently were excluded. The model treated study as a random effect.

Results

The model predicted that new regimens of 4 or 5 months duration with rates of culture positivity after 2 months of 1% or 3%, would yield relapse rates of 4.0% or 4.1%, respectively. In both cases, the upper limit of the 2-sided 80% prediction interval for relapse for a hypothetical trial with 680 subjects per arm was <10%. Analysis using this model of published month 2 data for moxifloxacin-containing regimens indicated they would result in relapse rates similar to standard therapy only if administered for ≥5 months.

Conclusions

This model is proposed to inform the required duration of treatment of new TB regimens, potentially hastening their accelerated approval by several years.     

Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.     

Estimating cumulative treatment effects in the presence of nonproportional hazards

Summary .   Often in medical studies of time to an event, the treatment effect is not constant over time. In the context of Cox regression modeling, the most frequent solution is to apply a model that assumes the treatment effect is either piecewise constant or varies smoothly over time, i.e., the Cox nonproportional hazards model. This approach has at least two major limitations. First, it is generally difficult to assess whether the parametric form chosen for the treatment effect is correct. Second, in the presence of nonproportional hazards, investigators are usually more interested in the cumulative than the instantaneous treatment effect (e.g., determining if and when the survival functions cross). Therefore, we propose an estimator for the aggregate treatment effect in the presence of nonproportional hazards. Our estimator is based on the treatment-specific baseline cumulative hazards estimated under a stratified Cox model. No functional form for the nonproportionality need be assumed. Asymptotic properties of the proposed estimators are derived, and the finite-sample properties are assessed in simulation studies. Pointwise and simultaneous confidence bands of the estimator can be computed. The proposed method is applied to data from a national organ failure registry.     

RF-cell: A model for populations of randomly interconnected neurons

This paper is concerned with a population of neurons with dense random interconnections, in which the stimulations between neurons are independent of their distance apart. This study is conducted from the viewpoint of the General System Theory. Proposed and used for the first time in studies on the above subject is a new concept referred to as the ‘historical report’ of the mentioned population. It will be shown that the population exhibits cyclic modes of behaviour which are dependent on its structure and historical report and which in the phase space correspond to cycles of hysteresis. A simple model in discrete time is developed and demonstrates, by the help of a computer study, the existence of the cycles of hysteresis.     

Population structure in the Connecticut Valley. I. Marital migration

This study reports on an analysis of marital migration among 12 communities in the Connecticut River Valley of Massachusetts during the years 1790-1849. Genetic inferences are drawn, and the requisite assumptions considered. The effect of geographic distance on genetic kinship is predicted using Malécot's isolation-by-distance model. The resulting estimates are discussed in terms of geographic and historical factors. The configuration of communities as predicted by kinship values approximates closely their actual geographic locations. Estimated genetic heterogeneity was low for the historical Connecticut Valley population, and community isolation breaks down rapidly over time. The region thus assumes its place among a number of sedentary, agricultural populations for which the isolation-by-distance model provides an adequate representation. A regression analysis which includes variables in addition to distance indicates that historical and economic factors contribute some additional explanatory power to the distribution of mating frequencies.     

历史生物地理学的进展

近年来历史生物地理学的进展主要是隔离分化学派的进展。对隔离分化学派的几个分支学派,特别是分支生物地理学和泛生物地理学在理论和方法方面的进展作了简要回顾和介绍。最近十年来分子手段广泛应用于历史生物地理学研究的各个方面,尤其是谱系生物地理学的快速崛起是历史生物地理学的一个明显特征。对本学科的发展做了初步的展望。     

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.     

Empirical likelihood for cumulative hazard ratio estimation with covariate adjustment

In medical studies, it is often of scientific interest to evaluate the treatment effect via the ratio of cumulative hazards, especially when those hazards may be nonproportional. To deal with nonproportionality in the Cox regression model, investigators usually assume that the treatment effect has some functional form. However, to do so may create a model misspecification problem because it is generally difficult to justify the specific parametric form chosen for the treatment effect. In this article, we employ empirical likelihood (EL) to develop a nonparametric estimator of the cumulative hazard ratio with covariate adjustment under two nonproportional hazard models, one that is stratified, as well as a less restrictive framework involving group-specific treatment adjustment. The asymptotic properties of the EL ratio statistic are derived in each situation and the finite-sample properties of EL-based estimators are assessed via simulation studies. Simultaneous confidence bands for all values of the adjusted cumulative hazard ratio in a fixed interval of interest are also developed. The proposed methods are illustrated using two different datasets concerning the survival experience of patients with non-Hodgkin's lymphoma or ovarian cancer.     

Meta-analysis of studies with missing data

Summary .  Consider a meta-analysis of studies with varying proportions of patient-level missing data, and assume that each primary study has made certain missing data adjustments so that the reported estimates of treatment effect size and variance are valid. These estimates of treatment effects can be combined across studies by standard meta-analytic methods, employing a random-effects model to account for heterogeneity across studies. However, we note that a meta-analysis based on the standard random-effects model will lead to biased estimates when the attrition rates of primary studies depend on the size of the underlying study-level treatment effect. Perhaps ignorable within each study, these types of missing data are in fact not ignorable in a meta-analysis. We propose three methods to correct the bias resulting from such missing data in a meta-analysis: reweighting the DerSimonian–Laird estimate by the completion rate; incorporating the completion rate into a Bayesian random-effects model; and inference based on a Bayesian shared-parameter model that includes the completion rate. We illustrate these methods through a meta-analysis of 16 published randomized trials that examined combined pharmacotherapy and psychological treatment for depression.     

A mathematical model of in vitro cancer cell growth and treatment with the antimitotic agent curacin A

A mathematical model of cancer cell growth and response to treatment with the experimental antimitotic agent curacin A is presented. Rate parameters for the untreated growth of MCF-7/LY2 breast cancer and A2780 ovarian cell lines are determined from in vitro growth studies. Subsequent growth studies following treatments with 2.5, 25 and 50 nanomolar (nM), concentrations of curacin A are used to determine effects on the cell cycle and cell viability. The model's system of ordinary differential equations yields an approximate analytical solution which predicts the minimum concentration necessary to prevent growth. The model shows that cell growth is arrested when the apoptotic rate is greater than the mitotic rate and that the S-phase transition rate acts to amplify this effect. Analysis of the data suggests that curacin A is rapidly absorbed into both cell lines causing an increase in the S-phase transition and a decrease in the M-phase transition. The model also indicates that the rate of apoptosis remains virtually constant for MCF-7/LY2 while that of A2780 increases 38% at 2.5 nM and 59% at 50 nM as compared to the untreated apoptotic rate.     

Multilevel data analysis of a crossover designed human nutritional intervention study

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p < 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.     

Biomechanical testing in experimental bone interventions--May the power be with you

Total variation in any measured variable, in conjunction with expected treatment effect, defines the minimum sample size (minSS) required to detect the expected effect with statistical confidence should the effect truly exist. A comprehensive literature survey of 3472 original studies was carried out to identify studies with biomechanical testing of whole bones. Total variation in common biomechanical traits and expected treatment effects in typical interventions were statistically determined. According to this survey, total variation in biomechanical traits between different species of experimental animals was similar, justifying the use of rat femur as a model in further analyses. Due to poorer precision, stiffness and energy absorption assessment require substantially larger sample size than breaking load. Due to same reason, minSS for femoral neck compression test is considerably larger than for femoral shaft three-point bending test. For the bending test, minSS to show a 10% treatment effect in the breaking load with 80% statistical power is 11rats/group, while corresponding minSS is 23 for the stiffness, and 53 for the energy absorption. For the femoral neck compression test, minSSs are 16, 51, and 134rats/group, respectively. Among the reviewed studies, the mean sample size was 11animals/group. This underscores the need for considerably larger sample sizes in experimental bone interventions which employ mechanical traits as primary outcome variables. In particular, poor precision and generally small expected treatment effects compromise the utility of stiffness and energy absorption assessments in experimental bone interventions.     

Double Inverse‐Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring

Summary In medical studies of time‐to‐event data, nonproportional hazards and dependent censoring are very common issues when estimating the treatment effect. A traditional method for dealing with time‐dependent treatment effects is to model the time‐dependence parametrically. Limitations of this approach include the difficulty to verify the correctness of the specified functional form and the fact that, in the presence of a treatment effect that varies over time, investigators are usually interested in the cumulative as opposed to instantaneous treatment effect. In many applications, censoring time is not independent of event time. Therefore, we propose methods for estimating the cumulative treatment effect in the presence of nonproportional hazards and dependent censoring. Three measures are proposed, including the ratio of cumulative hazards, relative risk, and difference in restricted mean lifetime. For each measure, we propose a double inverse‐weighted estimator, constructed by first using inverse probability of treatment weighting (IPTW) to balance the treatment‐specific covariate distributions, then using inverse probability of censoring weighting (IPCW) to overcome the dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal. We study their finite‐sample properties through simulation. The proposed methods are used to compare kidney wait‐list mortality by race.     

HIV vaccine development in the nonhuman primate model of AIDS

Development of a prophylactic human immunodeficiency virus type 1 (HIV-1) vaccine is a leading priority in biomedical research. Much of this work has been done with the nonhuman primate model of AIDS. In a historical context, vaccine studies, which use this model, are summarized and discussed.     

Quantitative description of the adaptation of vagal effects by means of an electromechanical coupling model]

A four compartments model describes the electrotropic and the inotropic vagal effect time courses during long time stimulation. The model realises electro-mechanical coupling. A differential equation for the coupling model is developed, solutions are given and a numerical treatment is described. The model shows the essential properties of effect adaptation: time delay and smaller extent of electrotropic effect adaptation.     

Genetic isolation by distance in Arabidopsis thaliana: biogeography and postglacial colonization of Europe

Arabidopsis thaliana provides a useful model system for functional, evolutionary and ecological studies in plant biology. We have analysed natural genetic variation in A. thaliana in order to infer its biogeographical and historical distribution across Eurasia. We analysed 79 amplified fragment length polymorphism (AFLP) markers in 142 accessions from the species' native range, and found highly significant genetic isolation by distance among A. thaliana accessions from Eurasia and southern Europe. These spatial patterns of genetic variation suggest that A. thaliana colonized central and northern Europe from Asia and from Mediterranean Pleistocene refugia, a trend which has been identified in other species. Statistically significant levels of multilocus linkage disequilibrium suggest intermediate levels of disequilibrium among subsets of loci, and analysis of genetic relationships among accessions reveal a star or bush-like dendrogram with low bootstrap support. Taken together, it appears that there has been sufficient historical recombination in the A. thaliana genome such that accessions do not conform to a tree-like, bifurcating pattern of evolution - there is no 'ecotype phylogeny.' Nonetheless, significant isolation by distance provides a framework upon which studies of natural variation in A. thaliana may be designed and interpreted.