Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Point estimation in adaptive confirmatory clinical trials with time-to-event data and treatment or subgroup selection

Adaptive designs are increasingly adopted to make the process of drug development more efficient. In particular, seamless phase II/III clinical trials allow interim adaptations such as early stopping for futility or selection of the most promising treatment. Furthermore, targeted therapy trials include an interim analysis to select a subgroup with the largest observed treatment effect. However, despite their efficiency, data dependent adaptations lead to multiplicity and selection issues. This is because data are used for both treatment or subgroup selection as well as for the confirmatory analysis of treatment efficacy. Specifically, selection rules applied at the interim stage lead to overoptimistic and thus biased effect estimates. In this thesis, we investigate the bias that arises due to selection and develop unbiased estimators that correct for treatment or subgroup selection in two-stage confirmatory clinical trials with time-to-event data. When analysis is based on time-to-event data, censoring at the interim analysis violates the assumption of independence between stage 1 and stage 2 data, which is a crucial assumption of existing methods for normally distributed data. The independent increments structure of stagewise log-rank test statistics has been beneficial for hypothesis testing in this setting, where group sequential methods have been utilised based on the log-rank test statistic for time-to-event data. We therefore incorporate the independent increments structure to derive unbiased estimators based on asymptotic normality of the log-rank test statistic. Additionally, when considering treatment selection, we address the issue of correlation between stage 1 estimates due to the common control arm for time-to-event outcomes. We give the joint distribution of stagewise log hazard ratios and using the technique of Rao-Blackwellisation, we derive asymptotically uniformly minimum variance unbiased estimators conditional on selection rules for time-to-event outcomes. We examine the bias and mean squared error of conventional estimates and compare these, by simulation, to our unbiased and efficient estimators, which correct for treatment or subgroup selection and correlation due to both censoring and the common control arm. We show that, due to the asymptotic normality assumptions, our estimators are appropriate for large samples and small to moderate effect sizes

Adjusting for treatment selection in phase II/III clinical trials with time to event data

Phase II/III clinical trials are efficient two‐stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control. Combined data of stage 1 and stage 2 are used for a confirmatory phase III analysis. Appropriate analysis needs to adjust for selection bias of the stage 1 data. Point estimators exist for normally distributed outcome data. Extending these estimators to time to event data is not straightforward because treatment selection is based on correlated treatment effects and stage 1 patients who do not get events in stage 1 are followed‐up in stage 2. We have derived an approximately uniformly minimum variance conditional unbiased estimator (UMVCUE) and compared its biases and mean squared errors to existing bias adjusted estimators. In simulations, one existing bias adjusted estimator has similar properties as the practically unbiased UMVCUE while the others can have noticeable biases but they are less variable than the UMVCUE. For confirmatory phase II/III clinical trials where unbiased estimators are desired, we recommend the UMVCUE or the existing estimator with which it has similar properties

Light Influences How the Fungal Toxin Deoxynivalenol Affects Plant Cell Death and Defense Responses

The Fusarium mycotoxin deoxynivalenol (DON) can cause cell death in wheat (Triticum aestivum), but can also reduce the level of cell death caused by heat shock in Arabidopsis (Arabidopsis thaliana) cell cultures. We show that 10 μg mL−1 DON does not cause cell death in Arabidopsis cell cultures, and its ability to retard heat-induced cell death is light dependent. Under dark conditions, it actually promoted heat-induced cell death. Wheat cultivars differ in their ability to resist this toxin, and we investigated if the ability of wheat to mount defense responses was light dependent. We found no evidence that light affected the transcription of defense genes in DON-treated roots of seedlings of two wheat cultivars, namely cultivar CM82036 that is resistant to DON-induced bleaching of spikelet tissue and cultivar Remus that is not. However, DON treatment of roots led to genotype-dependent and light-enhanced defense transcript accumulation in coleoptiles. Wheat transcripts encoding a phenylalanine ammonia lyase (PAL) gene (previously associated with Fusarium resistance), non-expressor of pathogenesis-related genes-1 (NPR1) and a class III plant peroxidase (POX) were DON-upregulated in coleoptiles of wheat cultivar CM82036 but not of cultivar Remus, and DON-upregulation of these transcripts in cultivar CM82036 was light enhanced. Light and genotype-dependent differences in the DON/DON derivative content of coleoptiles were also observed. These results, coupled with previous findings regarding the effect of DON on plants, show that light either directly or indirectly influences the plant defense responses to DON

Does Internal Financing Trigger the relationship between Managerial Optimism and Investment Inefficiency?

This study investigates the relationship between managerial optimism and investment efficiency moderated by internal financing. The sample for this study comprises the FTSE 100 index firms listed on Bursa Malaysia from the year 2013 to 2018. In this study, the dependent variable is investment efficiency whereby its measurement is adopted from Biddle et al. (2009); the independent variable is managerial optimism which is proxied by net buyer, and lastly, the intermediate variable is internal financing whereby the method of measurement is adopted from He et al. (2019). The findings of this research portray that managerial optimism and internal financing have an insignificant relationship with investment efficiency when examined separately. However, the interaction term between managerial optimism and internal financing has a positive significant relationship with investment efficiency. This study shows that when internal finance is sufficient, the existence of managerial optimism tends to result in investment inefficiency (overinvestment) in the firm. It is important to acknowledge the existence of managerial optimism in the corporation, hence our study suggests that firms may refine their strategies by developing a mechanism that controls the internal financing of the firm as an approach to prevent the effects of managerial optimism leads to investment inefficiency

Pathological and ecological host consequences of infection by an introduced fish parasite

The infection consequences of the introduced cestode fish parasite Bothriocephalus acheilognathi were studied in a cohort of wild, young-of-the-year common carp Cyprinus carpio that lacked co-evolution with the parasite. Within the cohort, parasite prevalence was 42% and parasite burdens were up to 12% body weight. Pathological changes within the intestinal tract of parasitized carp included distension of the gut wall, epithelial compression and degeneration, pressure necrosis and varied inflammatory changes. These were most pronounced in regions containing the largest proportion of mature proglottids. Although the body lengths of parasitized and non-parasitized fish were not significantly different, parasitized fish were of lower body condition and reduced weight compared to non-parasitized conspecifics. Stable isotope analysis (δ15N and δ13C) revealed trophic impacts associated with infection, particularly for δ15N where values for parasitized fish were significantly reduced as their parasite burden increased. In a controlled aquarium environment where the fish were fed ad libitum on an identical food source, there was no significant difference in values of δ15N and δ13C between parasitized and non-parasitized fish. The growth consequences remained, however, with parasitized fish growing significantly slower than non-parasitized fish, with their feeding rate (items s−1) also significantly lower. Thus, infection by an introduced parasite had multiple pathological, ecological and trophic impacts on a host with no experience of the parasite

The Dynamic Impact of Crude Oil Price and Real Estate Growth on Stock Market Performance

This paper examines the effect of crude oil price and real estate growth on Malaysian stock market performance by examining the monthly data from 1999-2016 using both linear and non-linear tests. These tests examine the long-run and short-run relationship among variables. Granger causality test is used to measure the short-run adjustments towards the long-run relationship among the variables. The results of Granger causality test indicates that a bidirectional relationship exists between stock market performance, crude oil price, real estate. In other words, there is a dynamic relationship among the stock market performance, crude oil and real estate

A20 regulates lymphocyte adhesion in murine neuroinflammation by restricting endothelial ICOSL expression in the CNS.

A20 is a ubiquitin-modifying protein that negatively regulates NF-κB signaling. Mutations in A20/TNFAIP3 are associated with a variety of autoimmune diseases, including multiple sclerosis (MS). We found that deletion of A20 in central nervous system (CNS) endothelial cells (ECs) enhances experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. A20∆CNS-EC mice showed increased numbers of CNS-infiltrating immune cells during neuroinflammation and in the steady state. While the integrity of the blood-brain barrier (BBB) was not impaired, we observed a strong activation of CNS-ECs in these mice, with dramatically increased levels of the adhesion molecules ICAM-1 and VCAM-1. We discovered ICOSL as adhesion molecule expressed by A20-deficient CNS-ECs. Silencing of ICOSL in CNS microvascular ECs partly reversed the phenotype of A20∆CNS-EC mice without reaching statistical significance and delayed the onset of EAE symptoms in wildtype mice. In addition, blocking of ICOSL on primary mouse brain microvascular endothelial cells (pMBMECs) impaired the adhesion of T cells in vitro. Taken together, we here propose that CNS EC-ICOSL contributes to the firm adhesion of T cells to the BBB, promoting their entry into the CNS and eventually driving neuroinflammation