Journal of the Royal Statistical Society Series C-Applied Statistics最新文献

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.

The Section 2 heading ‘A BNR MODEL’ should be corrected to read as ‘A BAYESIAN NONPARAMETRIC REGRESSION MODEL’.

Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects (‘treatment effect borrowing’, TEB) to borrowing over the subtrial groupwise responses (‘treatment response borrowing’, TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: (i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and (ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomisation approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.

Influenza A viruses (IAV) are the only influenza viruses known to cause flu pandemics. Understanding the evolution of different sub-types of IAV on their natural hosts is important for preventing and controlling the virus. We propose a mechanism-based Bayesian multi-level mixed-effects model for characterising influenza viral dynamics, described by a set of ordinary differential equations (ODE). Both strain-specific and subject-specific random effects are included for the ODE parameters. Our models can characterise the common features in the population while taking into account the variations among individuals. The random effects selection is conducted at strain level through re-parameterising the covariance parameters of the corresponding random effect distribution. Our method does not need to solve ODE directly. We demonstrate that the posterior computation can proceed via a simple and efficient Markov chain Monte Carlo algorithm. The methods are illustrated using simulated data and a real data from a study relating virus load estimates from influenza infections in ducks.

Latent class models are often used to characterise dietary patterns. Yet, when subtle variations exist across different sub-populations, overall population patterns can be masked and affect statistical inference on health outcomes. We address this concern with a flexible supervised clustering approach, introduced as Supervised Robust Profile Clustering, that identifies outcome-dependent population-based patterns, while partitioning out subpopulation pattern differences. Using dietary data from the 1997–2011 National Birth Defects Prevention Study, we determine how maternal dietary profiles associate with orofacial clefts among offspring. Results indicate mothers who consume a higher proportion of fruits and vegetables compared to land meats lower the proportion of progeny with orofacial cleft defect.