Joint Modelling of Repeated Measures and Survival Time Data

In many clinical trials both repeated measures data and event history data are simultaneously observed from the same subject. These two types of responses are usually correlated, because they are from the same subject. In this article, we propose a joint model for the combined analysis of repeated measures data and event history data in the framework of hierarchical generalized linear models. The correlation between repeated measures and event time is modelled by introducing a shared random effect. The model parameters are estimated using the hierarchical‐likelihood approach. The proposed model is illustrated using a real data set for the renal transplant patients.     

Residual analysis for linear mixed models

Residuals are frequently used to evaluate the validity of the assumptions of statistical models and may also be employed as tools for model selection. For standard (normal) linear models, for example, residuals are used to verify homoscedasticity, linearity of effects, presence of outliers, normality and independence of the errors. Similar uses may be envisaged for three types of residuals that emerge from the fitting of linear mixed models. We review some of the residual analysis techniques that have been used in this context and propose a standardization of the conditional residual useful to identify outlying observations and clusters. We illustrate the procedures with a practical example.     

Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning

Soil basidiomycetes play key roles in forest nutrient and carbon cycling processes, yet the diversity and structure of below ground basidiomycete communities remain poorly understood. Prescribed burning is a commonly used forest management practice and there is evidence that single fire events can have an impact on soil fungal communities but little is known about the effects of repeated prescribed burning. We have used internal transcribed spacer (ITS) terminal restriction fragment length polymorphism (T-RFLP) analysis to investigate the impacts of repeated prescribed burning every two or four years over a period of 30 years on soil basidiomycete communities in an Australian wet sclerophyll forest. Detrended correspondence analysis of ITS T-RFLP profiles separated basidiomycete communities in unburned control plots from those in burned plots, with those burned every two years being the most different from controls. Burning had no effect on basidiomycete species richness, thus these differences appear to be due to changes in community structure. Basidiomycete communities in the unburned control plots were vertically stratified in the upper 20 cm of soil, but no evidence was found for stratification in the burned plots, suggesting that repeated prescribed burning results in more uniform basidiomycete communities. Overall, the results demonstrate that repeated prescribed burning alters soil basidiomycete communities, with the effect being greater with more frequent burning.     

A modified pseudolikelihood approach for analysis of longitudinal data

We consider the analysis of longitudinal data when the covariance function is modeled by additional parameters to the mean parameters. In general, inconsistent estimators of the covariance (variance/correlation) parameters will be produced when the "working" correlation matrix is misspecified, which may result in great loss of efficiency of the mean parameter estimators (albeit the consistency is preserved). We consider using different "working" correlation models for the variance and the mean parameters. In particular, we find that an independence working model should be used for estimating the variance parameters to ensure their consistency in case the correlation structure is misspecified. The designated "working" correlation matrices should be used for estimating the mean and the correlation parameters to attain high efficiency for estimating the mean parameters. Simulation studies indicate that the proposed algorithm performs very well. We also applied different estimation procedures to a data set from a clinical trial for illustration.     

Semiparametric bayesian inference for multilevel repeated measurement data

We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. Some inference questions relate to repeated measurements over days within cycle, while other questions are concerned with the dependence across cycles. When the desired inference relates to both levels of repetition, it becomes important to reflect the data structure in the model. We develop a semiparametric Bayesian modeling approach, restricting attention to two levels of repeated measurements. For the top-level longitudinal sampling model we use random effects to introduce the desired dependence across repeated measurements. We use a nonparametric prior for the random effects distribution. Inference about dependence across second-level repetition is implemented by the clustering implied in the nonparametric random effects model. Practical use of the model requires that the posterior distribution on the latent random effects be reasonably precise.     

Production of paramylon,a β‐1,3‐glucan,by heterotrophic growth of Euglena gracilis on potato liquor in fed‐batch and repeated‐batch mode of cultivation

Recently, it had been shown that Euglena gracilis was able to grow heterotrophically not only on synthetic media, but also on media based on potato liquor. Supplementation with glucose in both cases led to the accumulation of paramylon, a β‐1,3‐glucan. Thus, such a process may yield a valuable product accompanied by the revaluation of an otherwise annoying waste stream of the potato‐starch industry. Actually, process strategies have been evaluated in order to optimise the concentration of paramylon obtained at the end of the cultivation process. Therefore, cultivation processes based on fed‐batch and in particular repeated‐batch strategies have been studied. It is shown that repeated‐batch operation maybe particularly suited for such a process since E. gracilis seems to adapt gradually to the cultivation medium so that the concentration of media components may be increased step by step. Repeated‐batch cultivation of E. gracilis leads to biomass concentrations in access of 20 g/L with a consistent paramylon mass fraction of about 75%. Cultivations have been carried out at an operating temperature of 27.5°C. As had been found earlier already, pH control is not required during cultivation. On the basis of these results it is clear that repeated‐batch cultivation represent a simple and economic way for the production of paramylon by heterotrophic cultivation of E. gracilis.     

Initiation of the microgene polymerization reaction with non-repetitive homo-duplexes

Microgene Polymerization Reaction (MPR) is used as an experimental system to artificially simulate evolution of short, non-repetitive homo-duplex DNA into multiply-repetitive products that can code for functional proteins. Blunt-end ligation by DNA polymerase is crucial in expansion of homo-duplexes (HDs) into head-to-tail multiple repeats in MPR. The propagation mechanism is known, but formation of the initial doublet (ID) by juxtaposing two HDs and polymerization through the gap has been ambiguous. Initiation events with pairs of HDs using Real-Time PCR were more frequent at higher HD concentrations and slightly below the melting temperature. A process molecularity of about 3.1, calculated from the amplification efficiency and the difference in PCR cycles at which propagation was detected at varying HD concentrations, led to a simple mechanism for ID formation: the gap between two HDs is bridged by a third. Considering thermodynamic aspects of the presumed intermediate “nucleation complex” can predict relative propensity for the process with other HDs.     

Modeling longitudinal biomarker data from multiple assays that have different known detection limits

Summary .   Assays to measure biomarkers are commonly subject to large amounts of measurement error and known detection limits. Studies with longitudinal biomarker measurements may use multiple assays in assessing outcome. I propose an approach for jointly modeling repeated measures of multiple assays when these assays are subject to measurement error and known lower detection limits. A commonly used approach is to perform an initial assay with a larger lower detection limit on all repeated samples, followed by only performing a second more expensive assay with a lower minimum level of detection when the initial assay value is below its lower limit of detection. I show how simply replacing the initial assay measurement with the second assay measurement may be a biased approach and investigate the performance of the proposed joint model in this situation. Additionally, I compare the performance of the joint model with an approach that only uses the initial assay measurements in analysis. Further, I consider alternative designs to only performing the second assay when the initial assay measurement is below its lower detection limit. Specifically, I show that one only needs to perform the second assay on a fraction of assays that are above the lower detection limit on the first assay to substantially increase the efficiency. Further, I show the efficiency advantages of performing the second assay at random without regard to the initial assay measurement over a design in which the second assay is only performed when the initial assay is below its lower limit of detection. The methodology is illustrated with a recent study examining the use of a vaccine in treating macaques with simian immunodeficiency virus.     

A preliminary investigation of the effect of repeated pedestrian approaches to Weddell seals (Leptonychotes weddellii)

Repeated exposure to human activity can change the behavioural response of wildlife, having implications for management. Weddell seals (Leptonychotes weddellii) breeding close to Antarctic research stations are easily accessible and regularly visited by people. To investigate the responses of Weddell seals to repeated pedestrian approaches, we tested the effect of regular visitation over a short-time period (<2 h) on the behaviour of lactating seals. Seals showed evidence of rapid habituation, as assessed by the higher proportion of seals that responded, with 67% looking up during the first approach compared to 18% during the 10th approach (R² = 0.398, P = 0.050), and by a decrease in the time spent looking at the approacher with repeated exposure (, P < 0.001). The effect of irregular pedestrian activity over a long-time period (approximately 3 weeks) was also examined, with results suggesting that such activity did not result in habituation, rather, adult female seals appeared to become sensitised to people (the majority of seals in both colonies looked up G₁ = 0.027, P = 0.870). Weddell seal pups observed during the same experiment also failed to display signs of habituation to irregular pedestrian activity, with 47% of pups looking up in the colony subjected to pedestrian activity compared to 10% in the control colony (G₁ = 5.811, P = 0.016). The implications of these results for managing human activity around breeding Weddell seals are discussed.