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

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.

This work deals with consumers' preferences about coffee. Firstly, a choice experiment is performed on a sample of potential consumers. Following this, a sensory test involving the tasting of two varieties of coffee is carried out with the respondents, after which the same choice experiment is supplied to them again. An innovative approach for building heterogeneous choice designs is specifically developed for the case-study, based on approximate design theory and compound design criterion. Panel Mixed Logit models are used, thereby allowing for the inclusion of correlation among consumers' responses; choice-sets are supplied to a proportion of respondents according to optimal weights. The estimation results of the Panel Mixed Logit model are satisfactory, confirming the validity of the proposed approach.

This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes. Small area estimators are predominantly conceptualised within the regression-setting and rely on linear mixed models to account for the hierarchical structure of the survey data. In contrast, machine learning methods offer non-linear and non-parametric alternatives, combining excellent predictive performance and a reduced risk of model-misspecification. Mixed effects random forests combine advantages of regression forests with the ability to model hierarchical dependencies. This paper provides a coherent framework based on mixed effects random forests for estimating small area averages and proposes a non-parametric bootstrap estimator for assessing the uncertainty of the estimates. We illustrate advantages of our proposed methodology using Mexican income-data from the state Nuevo León. Finally, the methodology is evaluated in model-based and design-based simulations comparing the proposed methodology to traditional regression-based approaches for estimating small area averages.

Estimating school completion is crucial for monitoring Sustainable Development Goal (SDG) 4 on education. The recently introduced SDG indicator 4.1.2, defined as the percentage of children aged 3–5 years above the expected completion age of a given level of education that have completed the respective level, differs from enrolment indicators in that it relies primarily on household surveys. This introduces a number of challenges including gaps between survey waves, conflicting estimates, age misreporting and delayed completion. We introduce the Adjusted Bayesian Completion Rates (ABCR) model to address these challenges and produce the first complete and consistent time series for SDG indicator 4.1.2, by school level and sex, for 164 countries. Validation exercises indicate that the model appears well-calibrated and offers a meaningful improvement over simpler approaches in predictive performance. The ABCR model is now used by the United Nations to monitor completion rates for all countries with available survey data.

Recurrent events are often encountered in clinical and epidemiological studies where a terminal event is also observed. With recurrent events data it is of great interest to estimate the marginal mean of the cumulative number of recurrent events experienced prior to the terminal event. The standard nonparametric estimator was suggested in Cook and Lawless and further developed in Ghosh and Lin. We here investigate the efficiency of this estimator that, surprisingly, has not been studied before. We rewrite the standard estimator as an inverse probability of censoring weighted estimator. From this representation we derive an efficient augmented estimator using efficient estimation theory for right-censored data. We show that the standard estimator is efficient in settings with no heterogeneity. In other settings with different sources of heterogeneity, we show theoretically and by simulations that the efficiency can be greatly improved when an efficient augmented estimator based on dynamic predictions is employed, at no extra cost to robustness. The estimators are applied and compared to study the mean number of catheter-related bloodstream infections in heterogeneous patients with chronic intestinal failure who can possibly die, and the efficiency gain is highlighted in the resulting point-wise confidence intervals.

Boundaries on spatial fields divide regions with particular features from surrounding background areas. Methods to identify boundary lines from interpolated spatial fields are well established. Less attention has been paid to how to model sequences of connected spatial points. Such models are needed for physical boundaries. For example, in the Arctic ocean, large contiguous areas are covered by sea ice, or frozen ocean water. We define the ice edge contour as the ordered sequences of spatial points that connect to form a line around set(s) of contiguous grid boxes with sea ice present. Polar scientists need to describe how this contiguous area behaves in present and historical data and under future climate change scenarios. We introduce the Gaussian Star-shaped Contour Model (GSCM) for modelling boundaries represented as connected sequences of spatial points such as the sea ice edge. GSCMs generate sequences of spatial points via generating sets of distances in various directions from a fixed starting point. The GSCM can be applied to contours that enclose regions that are star-shaped polygons or approximately star-shaped polygons. Metrics are introduced to assess the extent to which a polygon deviates from star-shapedness. Simulation studies illustrate the performance of the GSCM in different situations.