Statistical Methods to Improve Efficiency in Composite Endpoint Analysis

Composite endpoints combine a number of outcomes to assess the efficacy of a treatment. They are used in situations where it is difficult to identify a single relevant endpoint, such as in complex multisystem diseases. Our focus in this thesis is on composite responder endpoints, which allocate patients as either ‘responders’ or ‘non-responders’ based on whether they cross predefined thresholds in the individual outcomes. These composites are often combinations of continuous and discrete measures and are typically collapsed into a single binary endpoint and analysed using logistic regression. However, this is at the expense of losing information on how close each patient was to the responder threshold. As well as being inefficient the analysis is sensitive to misclassification due to measurement error. The augmented binary method was introduced to improve the analysis of composite responder endpoints comprised of a single continuous and binary endpoint, by making use of the continuous information. In this thesis we build on this work to address some of the existing limitations. We implement small sample corrections for the standard binary and augmented binary methods and assess the performance for application in rare disease trials, where the gains are most needed. We find that employing the small sample corrected augmented binary method results in a reduction of required sample size of 32%. Motivated by systemic lupus erythematosus (SLE), we consider the case where the composite has multiple continuous, ordinal and binary components. We adapt latent variable models for application to these endpoints and assess the performance in simulated data and phase IIb trial data in SLE. Our findings show reductions in required sample size of at least 60%, however the magnitude of the gains depends on which components drive response. Finally, we develop a method for sample size estimation so that the model may be used as a primary analysis method in clinical trials. We assess the impact of correlation structure and drivers of response on the sample size required

Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints

Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints

Employing a latent variable framework to improve efficiency in composite endpoint analysis.

Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus, where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the systemic lupus erythematosus endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find that the method offers large efficiency gains over the standard analysis, the magnitude of which is highly dependent on the components driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe systemic lupus erythematosus. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size

Analysis of responder-based endpoints: improving power through utilising continuous components

Abstract: Background: Clinical trials and other studies commonly assess the effectiveness of an intervention through the use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by > 30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied. Methods: We reviewed a database of core outcome sets (COSs) that covered physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power. Results: Out of the 287 COSs reviewed, we identified 67 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Clinical areas that had particularly high numbers were rheumatology (11 clinical disorders identified), non-solid tumour oncology (10 identified), neurology (9 identified) and cardiovascular (8 identified). Conclusions: The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoints

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting

Differentiated neuroprogenitor cells incubated with human or canine adenovirus, or lentiviral vectors have distinct transcriptome profiles

Several studies have demonstrated the potential for vector-mediated gene transfer to the brain. Helper-dependent (HD) human (HAd) and canine (CAV-2) adenovirus, and VSV-G-pseudotyped self-inactivating HIV-1 vectors (LV) effectively transduce human brain cells and their toxicity has been partly analysed. However, their effect on the brain homeostasis is far from fully defined, especially because of the complexity of the central nervous system (CNS). With the goal of dissecting the toxicogenomic signatures of the three vectors for human neurons, we transduced a bona fide human neuronal system with HD-HAd, HD-CAV-2 and LV. We analysed the transcriptional response of more than 47,000 transcripts using gene chips. Chip data showed that HD-CAV-2 and LV vectors activated the innate arm of the immune response, including Toll-like receptors and hyaluronan circuits. LV vector also induced an IFN response. Moreover, HD-CAV-2 and LV vectors affected DNA damage pathways - but in opposite directions - suggesting a differential response of the p53 and ATM pathways to the vector genomes. As a general response to the vectors, human neurons activated pro-survival genes and neuron morphogenesis, presumably with the goal of re-establishing homeostasis. These data are complementary to in vivo studies on brain vector toxicity and allow a better understanding of the impact of viral vectors on human neurons, and mechanistic approaches to improve the therapeutic impact of brain-directed gene transfer

Mycoplasma pneumoniae infections, 11 countries in Europe and Israel, 2011 to 2016

Background: Mycoplasma pneumoniae is a leading cause of community-acquired pneumonia, with large epidemics previously described to occur every 4 to 7 years. Aim: To better understand the diagnostic methods used to detect M. pneumoniae; to better understand M. pneumoniae testing and surveillance in use; to identify epidemics; to determine detection number per age group, age demographics for positive detections, concurrence of epidemics and annual peaks across geographical areas; and to determine the effect of geographical location on the timing of epidemics. Methods: A questionnaire was sent in May 2016 to Mycoplasma experts with national or regional responsibility within the ESCMID Study Group for Mycoplasma and Chlamydia Infections in 17 countries across Europe and Israel, retrospectively requesting details on M. pneumoniae-positive samples from January 2011 to April 2016. The Moving Epidemic Method was used to determine epidemic periods and effect of country latitude across the countries for the five periods under investigation. Results: Representatives from 12 countries provided data on M. pneumoniae infections, accounting for 95,666 positive samples. Two laboratories initiated routine macrolide resistance testing since 2013. Between 2011 and 2016, three epidemics were identified: 2011/12, 2014/15 and 2015/16. The distribution of patient ages for M. pneumoniae-positive samples showed three patterns. During epidemic years, an association between country latitude and calendar week when epidemic periods began was noted. Conclusions: An association between epidemics and latitude was observed. Differences were noted in the age distribution of positive cases and detection methods used and practice. A lack of macrolide resistance monitoring was noted

Estimation of Relative Vaccine Effectiveness in Influenza: A Systematic Review of Methodology.

BACKGROUND: When new vaccine components or platforms are developed, they will typically need to demonstrate noninferiority or superiority over existing products, resulting in the assessment of relative vaccine effectiveness (rVE). This review aims to identify how rVE evaluation is being performed in studies of influenza to inform a more standardized approach. METHODS: We conducted a systematic search on PubMed, Google Scholar, and Web of Science for studies reporting rVE comparing vaccine components, dose, or vaccination schedules. We screened titles, abstracts, full texts, and references to identify relevant articles. We extracted information on the study design, relative comparison made, and the definition and statistical approach used to estimate rVE in each study. RESULTS: We identified 63 articles assessing rVE in influenza virus. Studies compared multiple vaccine components (n = 38), two or more doses of the same vaccine (n = 17), or vaccination timing or history (n = 9). One study compared a range of vaccine components and doses. Nearly two-thirds of all studies controlled for age, and nearly half for comorbidities, region, and sex. Assessment of 12 studies presenting both absolute and relative effect estimates suggested proportionality in the effects, resulting in implications for the interpretation of rVE effects. CONCLUSIONS: Approaches to rVE evaluation in practice is highly varied, with improvements in reporting required in many cases. Extensive consideration of methodologic issues relating to rVE is needed, including the stability of estimates and the impact of confounding structure on the validity of rVE estimates