Robust covariate-adjusted log-rank statistics and corresponding sample size formula for recurrent events data

Summary .   Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate-adjusted log-rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log-rank tests are robust with respect to different data-generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika 84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate-adjusted log-rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics 39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study.     

Accounting for variability in sample size estimation with applications to nonadherence and estimation of variance and effect size

We consider sample size calculations for testing differences in means between two samples and allowing for different variances in the two groups. Typically, the power functions depend on the sample size and a set of parameters assumed known, and the sample size needed to obtain a prespecified power is calculated. Here, we account for two sources of variability: we allow the sample size in the power function to be a stochastic variable, and we consider estimating the parameters from preliminary data. An example of the first source of variability is nonadherence (noncompliance). We assume that the proportion of subjects who will adhere to their treatment regimen is not known before the study, but that the proportion is a stochastic variable with a known distribution. Under this assumption, we develop simple closed form sample size calculations based on asymptotic normality. The second source of variability is in parameter estimates that are estimated from prior data. For example, we account for variability in estimating the variance of the normal response from existing data which are assumed to have the same variance as the study for which we are calculating the sample size. We show that we can account for the variability of the variance estimate by simply using a slightly larger nominal power in the usual sample size calculation, which we call the calibrated power. We show that the calculation of the calibrated power depends only on the sample size of the existing data, and we give a table of calibrated power by sample size. Further, we consider the calculation of the sample size in the rarer situation where we account for the variability in estimating the standardized effect size from some existing data. This latter situation, as well as several of the previous ones, is motivated by sample size calculations for a Phase II trial of a malaria vaccine candidate.     

Power analysis for trend in ordinal cover classes: implications for long‐term vegetation monitoring

Question: We provide a method to calculate the power of ordinal regression models for detecting temporal trends in plant abundance measured as ordinal cover classes. Does power depend on the shape of the unobserved (latent) distribution of percentage cover? How do cover class schemes that differ in the number of categories affect power? Methods: We simulated cover class data by “cutting‐up” a continuous logit‐beta distributed variable using 7‐point and 15‐point cover classification schemes. We used Monte Carlo simulation to estimate power for detecting trends with two ordinal models, proportional odds logistic regression (POM) and logistic regression with cover classes re‐binned into two categories, a model we term an assessment point model (APM). We include a model fit to the logit‐transformed percentage cover data for comparison, which is a latent model. Results: The POM had equal or higher power compared to the APM and latent model, but power varied in complex ways as a function of the assumed latent beta distribution. We discovered that if the latent distribution is skewed, a cover class scheme with more categories might yield higher power to detect trend. Conclusions: Our power analysis method maintains the connection between the observed ordinal cover classes and the unmeasured (latent) percentage cover variable, allowing for a biologically meaningful trend to be defined on the percentage cover scale. Both the shape of the latent beta distribution and the alternative hypothesis should be considered carefully when determining sample size requirements for long‐term vegetation monitoring using cover class measurements.     

Design and analysis of group sequential clinical trials with multiple primary endpoints

In many phase III clinical trials, it is desirable to separately assess the treatment effect on two or more primary endpoints. Consider the MERIT-HF study, where two endpoints of primary interest were time to death and the earliest of time to first hospitalization or death (The International Steering Committee on Behalf of the MERIT-HF Study Group, 1997, American Journal of Cardiology 80[9B], 54J-58J). It is possible that treatment has no effect on death but a beneficial effect on first hospitalization time, or it has a detrimental effect on death but no effect on hospitalization. A good clinical trial design should permit early stopping as soon as the treatment effect on both endpoints becomes clear. Previous work in this area has not resolved how to stop the study early when one or more endpoints have no treatment effect or how to assess and control the many possible error rates for concluding wrong hypotheses. In this article, we develop a general methodology for group sequential clinical trials with multiple primary endpoints. This method uses a global alpha-spending function to control the overall type I error and a multiple decision rule to control error rates for concluding wrong alternative hypotheses. The method is demonstrated with two simulated examples based on the MERIT-HF study.     

臭氧雾化联合壳聚糖治疗混合性阴道炎临床分析

目的评价臭氧雾化联合壳聚糖治疗混合性阴道炎的有效性和安全性。方法随机选取2014年1月至5月在长兴县妇幼保健院门诊就诊的混合性阴道炎患者186例,分为观察组和对照组,观察组用臭氧雾化联合壳聚糖治疗,对照组用定君生联合甲硝唑片口服,治疗时间为7 d,观察两组患者治疗后的临床疗效和安全性。结果治疗7 d后,观察组治疗有效率为92.47%,对照组治疗有效率为93.55%,两组无明显药物不良反应。结论臭氧雾化联合壳聚糖治疗混合性阴道炎疗效好,安全性好,无明显副作用,可作为治疗混合性阴道炎的一个选择。     

Statistical methods for analysis of radiation effects with tumor and dose location-specific information with application to the WECARE study of asynchronous contralateral breast cancer

Summary .  Methods for the analysis of individually matched case-control studies with location-specific radiation dose and tumor location information are described. These include likelihood methods for analyses that just use cases with precise location of tumor information and methods that also include cases with imprecise tumor location information. The theory establishes that each of these likelihood based methods estimates the same radiation rate ratio parameters, within the context of the appropriate model for location and subject level covariate effects. The underlying assumptions are characterized and the potential strengths and limitations of each method are described. The methods are illustrated and compared using the WECARE study of radiation and asynchronous contralateral breast cancer.     

Rational design and optimization of downstream processes of virus particles for biopharmaceutical applications: current advances

The advent of advanced therapies in the pharmaceutical industry has moved the spotlight into virus-like particles and viral vectors produced in cell culture holding great promise in a myriad of clinical targets, including cancer prophylaxis and treatment. Even though a couple of cases have reached the clinic, these products have yet to overcome a number of biological and technological challenges before broad utilization. Concerning the manufacturing processes, there is significant research focusing on the optimization of current cell culture systems and, more recently, on developing scalable downstream processes to generate material for pre-clinical and clinical trials. We review the current options for downstream processing of these complex biopharmaceuticals and underline current advances on knowledge-based toolboxes proposed for rational optimization of their processing. Rational tools developed to increase the yet scarce knowledge on the purification processes of complex biologicals are discussed as alternative to empirical, “black-boxed” based strategies classically used for process development. Innovative methodologies based on surface plasmon resonance, dynamic light scattering, scale-down high-throughput screening and mathematical modeling for supporting ion-exchange chromatography show great potential for a more efficient and cost-effective process design, optimization and equipment prototyping.     

Reproducibility of fluctuating asymmetry measurements in plants: Sources of variation and implications for study design

Fluctuating asymmetry (FA), i.e. small, non-directional deviations from perfect symmetry in morphological characters, increases under genetic and/or environmental stress. Ecological and evolutionary studies addressing FA became popular in past decades; however, their outcomes remain controversial. The discrepancies might be at least partly explained by inconsistent and non-standardised methodology. Our aim was to improve the methodology of these studies by identifying factors that affect the reproducibility of FA measurements in plant leaves. Six observers used a highly standardised measurement protocol to measure FA using the width, area and weight of the same set of leaves of 10 plant species that differed in leaf size, shape of the leaf margin and other leaf traits. On average, 24% of the total variation in the data was due to measurement error. Reproducibility of measurements varied with the shape of leaf margin, leaf size, the measured character and the experience of the observer. The lowest reproducibility of the width of leaf halves was found for simple leaves with serrate margins and the highest for simple leaves with entire margins and for compound pinnate leaves. The reproducibility was significantly lower for the weight of leaf halves than for either their width or area, especially for plants with small leaves. The reproducibility was also lower for measurements made by experienced observers than by naïve observers. The size of press-dried leaves decreased slightly but significantly relative to fresh leaves, but the FA of press-dried leaves adequately reflected the FA of fresh leaves. In contrast, preservation in 60% ethanol did not affect leaf size, but it decreased the width-based values of FA to 89.3% of the values measured from fresh leaves. We suggest that although reproducibility of leaf FA measurements depends upon many factors, the shape of the leaf margin is the most important source of variation. We recommend, whenever possible, choosing large-leaved plants with entire leaf margins as model objects for studies involving measurements of FA using the width of leaf halves. These measurements should be conducted with high accuracy from images of fresh or press-dried leaves.     

Semiparametric transformation models with random effects for joint analysis of recurrent and terminal events

Summary .  We propose a broad class of semiparametric transformation models with random effects for the joint analysis of recurrent events and a terminal event. The transformation models include proportional hazards/intensity and proportional odds models. We estimate the model parameters by the nonparametric maximum likelihood approach. The estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Simple and stable numerical algorithms are provided to calculate the parameter estimators and to estimate their variances. Extensive simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two HIV/AIDS studies are presented.     

Simple methods for the estimation and sensitivity analysis of principal strata effects using marginal structural models: Application to a bone fracture prevention trial

In randomized clinical trials, it is often of interest to estimate the effect of treatment on quality of life (QOL), in addition to those on the event itself. When an event occurs in some patients prior to QOL score assessment, investigators may compare QOL scores between patient subgroups defined by the event after randomization. However, owing to postrandomization selection bias, this analysis can mislead investigators about treatment efficacy and result in paradoxical findings. The recent Japanese Osteoporosis Intervention Trial (JOINT‐02), which compared the benefits of a combination therapy for fracture prevention with those of a monotherapy, exemplifies the case in point; the average QOL score was higher in the combination therapy arm for the unfractured subgroup but was lower for the fractured subgroup. To address this issue, principal strata effects (PSEs), which are treatment effects estimated within subgroups of individuals stratified by potential intermediate variable, have been discussed in the literature. In this paper, we describe a simple procedure for estimating the PSEs using marginal structural models. This procedure utilizes SAS code for the estimation. In addition, we present a simple sensitivity analysis method for examining the resulting estimates. The analyses of JOINT‐02 data using these methods revealed that QOL scores were higher in the combination therapy arm than in the monotherapy arm for both subgroups.     

Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions

Knowledge discovery from the exponentially growing body of structurally characterised protein-ligand complexes as a source of information in structure-based drug design is a major challenge in contemporary drug research. Given the need for powerful data retrieval, integration and analysis tools, Relibase was developed as a database system particularly designed to handle protein-ligand related problems and tasks. Here, we describe the design and functionality of the Relibase core database system. Features of Relibase include, e.g. the detailed analysis of superimposed ligand binding sites, ligand similarity and substructure searches, and 3D searches for protein-ligand and protein-protein interaction patterns. The broad range of functions provided in Relibase and its high level of data integration, along with its flexible and intuitive interface, makes Relibase an invaluable data mining tool which can significantly enhance the drug development process. An example, illustrating a 3D query for quarternary ligand nitrogen atoms interacting with aromatic ring systems in proteins, a pattern found in pharmaceutically relevant target proteins such as, e.g. acetylcholine-esterase, is discussed.     

Effect of early adverse events on response and survival outcomes of advanced melanoma patients treated with vemurafenib or vemurafenib plus cobimetinib: A pooled analysis of clinical trial data

This study aimed to evaluate the impact of early adverse events on overall survival (OS), progression‐free survival (PFS) and objective response within a pooled secondary analysis of participants treated with first‐line vemurafenib or vemurafenib plus cobimetinib in the clinical trials BRIM3 and coBRIM. The study included 583 participants who received vemurafenib monotherapy and 247 who received vemurafenib plus cobimetinib. Adverse events requiring vemurafenib/cobimetinib dose adjustment within the first 28 days of therapy were significantly associated with OS (hazard ratio (HR) [95% CI]: dose reduced/interrupted = 0.79 [0.65–0.96]; drug withdrawn = 1.18 [0.71–1.96]; p = 0.032), PFS (HR [95% CI]: dose reduced/interrupted = 0.82 [0.67–0.99]; drug withdrawn = 1.58 [0.97–2.58]; p = 0.017) and objective response (odds ratio (OR) [95% CI]: dose reduced/interrupted = 1.35 [0.99–1.85]; drug withdrawn = 0.17 [0.06–0.43]; p = <0.001). Arthralgia occurring within the first 28 days of vemurafenib or vemurafenib plus cobimetinib therapy was also significantly associated with favourable OS (p = 0.026), PFS (p = 0.042) and objective response (p = 0.047).     

Phylogenetic analysis of Magnoliales and Myristicaceae based on multiple data sets: implications for character evolution

Magnoliales, consisting of six families of tropical to warm-temperate woody angiosperms, were long considered the most archaic order of flowering plants, but molecular analyses nest them among other eumagnoliids. Based on separate and combined analyses of a morphological matrix (115 characters) and multiple molecular data sets (seven variable chloroplast loci and five more conserved genes; 14 536 aligned nucleotides), phylogenetic relationships were investigated simultaneously within Magnoliales and Myristicaceae, using Laurales, Winterales, and Piperales as outgroups. Despite apparent conflicts among data sets, parsimony and maximum likelihood analyses of combined data converged towards a fully resolved and well-supported topology, consistent with higher-level molecular analyses except for the position of Magnoliaceae: Myristicaceae + (Magnoliaceae + (( Degeneria + Galbulimima ) + ( Eupomatia + Annonaceae))). Based on these results, we discuss morphological evolution in Magnoliales and show that several supposedly plesiomorphic traits are synapomorphies of Magnoliineae, the sister group of Myristicaceae (e.g. laminar stamens). Relationships within Annonaceae are also resolved with strong support ( Anaxagorea basal, then ambavioids). In contrast, resolution of relationships within Myristicaceae is difficult and still incomplete, due to a very low level of molecular divergence within the family and a long stem lineage. However, our data provide good evidence that Mauloutchia is nested among other Afro-Malagasy genera, contradicting the view that its androecium and pollen are plesiomorphic  © 2003 The Linnean Society of London, Botanical Journal of the Linnean Society , 2003, 142 , 125–186.     

A multi-component integrated approach for the elimination of schistosomiasis in the People’s Republic of China: design and baseline results of a 4-year cluster-randomised intervention trial

Despite major successes in its control over the past 50 years, schistosomiasis japonica continues to be a public health problem in the People’s Republic of China (P.R. China). Historically, the major endemic foci occur in the lakes and marshlands along the Yangtze River, areas where transmission interruption has proven difficult. The current endemic situation may alter due to the closure of the Three Gorges Dam. Considerable environmental and ecological changes are anticipated that may result in new habitats for the oncomelanid intermediate snail host of Schistosoma japonicum (Sj), thereby increasing the risk of transmission. The current national control program for P.R. China involves a multi-component integrated strategy but, despite targeting multiple transmission pathways, certain challenges remain. As the Chinese government pushes towards elimination, there is a requirement for additional tools, such as vaccination, for long-term prevention. Whereas the zoonotic nature of schistosomiasis japonica adds to the complexity of control, it provides a unique opportunity to develop a transmission blocking vaccine targeting bovines to assist in the prevention of human infection and disease. Mathematical modelling has shown that control options targeting the various transmission pathways of schistosomiasis japonica and incorporating bovine vaccination, mass human chemotherapy and mollusciciding could lead to its elimination from P.R. China. Here we present the study design and baseline results of a four-year cluster randomised intervention trial we are undertaking around the schistosomiasis-endemic Dongting Lake in Hunan Province aimed at determining the impact on schistosome transmission of the multi-component integrated control strategy, including bovine vaccination using a heterologous “prime-boost” delivery platform based on the previously tested SjCTPI vaccine.     

Genetic analysis of the threatened American hart's-tongue fern (Asplenium scolopendrium var. americanum [Fernald] Kartesz and Gandhi): Insights into its mating system and implications for conservation

American hart's-tongue fern (AHTF) is one of the rarest ferns in the United States and concern over its conservation and management has highlighted the need for genetic analysis. Genetic analysis also provides insights into the species' mating system which contributes to our understanding of its rarity and persistence. We analyzed 88 individuals from 11 populations in NY and MI based on variations in 108 loci as revealed through ISSR markers using Nei's gene diversity index, percent polymorphic loci and other measures. Low genetic diversity predominates in the populations from NY, and even lower for the populations in MI. Our results also indicate that AHTF from NY and MI are genetically differentiated from each other, as well as the populations within them. There is no positive correlation between genetic and geographic distances, as well as between genetic distance and census population size. The significantly high among population genetic variation and low gene flow value are common indicators of a predominant inbreeding mating strategy within populations, limited spore dispersal, and genetic drift. Our results also indicate that each AHTF population is an important contributor to the overall genetic variation of the species and thus, represents a significant unit for conservation efforts.