Biometrical Journal最新文献

Bayesian nonparametric (BNP) approaches for meta-analysis have been developed to relax distributional assumptions and handle the heterogeneity of random effects distributions. These models account for possible clustering and multimodality of the random effects distribution. However, when we combine studies of varying quality, the resulting posterior is not only a combination of the results of interest but also factors threatening the integrity of the studies' results. We refer to these factors as the studies' internal validity biases (e.g., reporting bias, data quality, and patient selection bias). In this paper, we introduce a new meta-analysis model called the bias-corrected Bayesian nonparametric (BC-BNP) model, which aims to automatically correct for internal validity bias in meta-analysis by only using the reported effects and their standard errors. The BC-BNP model is based on a mixture of a parametric random effects distribution, which represents the model of interest, and a BNP model for the bias component. This model relaxes the parametric assumptions of the bias distribution of the model introduced by Verde. Using simulated data sets, we evaluate the BC-BNP model and illustrate its applications with two real case studies. Our results show several potential advantages of the BC-BNP model: (1) It can detect bias when present while producing results similar to a simple normal–normal random effects model when bias is absent. (2) Relaxing the parametric assumptions of the bias component does not affect the model of interest and yields consistent results with the model of Verde. (3) In some applications, a BNP model of bias offers a better understanding of the studies' biases by clustering studies with similar biases. We implemented the BC-BNP model in the R package jarbes, facilitating its practical application.

This paper proposes a general approach for handling multiple contrast tests for normally distributed data in the presence of partial heteroskedasticity. In contrast to the usual case of complete heteroskedasticity, the treatments belong to subgroups according to their variances. Treatments within these subgroups are homoskedastic, whereas treatments of different subgroups are heteroskedastic. New candidate as well as already existing approaches are described and compared by $�\alpha$-simulations. Power simulations show that a gain in power is achieved when the partial heteroskedasticity is taken into account compared to procedures which wrongly assume complete heteroskedasticity. The new approaches will be applied to a phytopathological experiment.

Correct measurement results from in vitro diagnostic (IVD) medical devices (MD) are crucial for optimal patient care. The performance of IVD-MDs is often assessed through method comparison studies. Such studies can be compromised by the influence of various factors. The effect of these factors must be examined in every method comparison study, for example, nonselectivity differences between compared IVD-MDs are examined. Historically, selectivity or nonselectivity has been defined as a qualitative term. However, a quantification of nonselectivity differences between IVD-MDs is needed. This paper fills this need by introducing a novel measure for quantifying differences in nonselectivity (DINS) between a pair of IVD-MDs. Assuming one of the IVD-MDs involved in the comparison exhibits high selectivity for the analyte, it becomes feasible to quantify nonselectivity in the other IVD-MD by employing this DINS measure. Our approach leverages elements from univariate ordinary least squares regression and incorporates repeatability IVD-MD variances, resulting in a normalized measure. We also introduce a plug-in estimator for this measure, which is notably linked to the average relative increase in prediction interval widths attributable to DINS. This connection is exploited to establish a criterion for identifying excessive DINS utilizing a proof-of-hazard approach. Utilizing Monte Carlo simulations, we investigate how the estimator relates to population characteristics like DINS and heteroskedasticity. We find that DINS impacts the mean, variance, and 99th percentile of the estimator, while heteroskedasticity affects only the latter two, and to a considerably smaller extent compared to DINS. Importantly, the size of the study design modulates these effects. We also confirm, when using clinical data, that DINS between pairs of IVD-MDs influence the estimator correspondingly to those of simulated data. Thus, the proposed estimator serves as an effective metric for quantifying DINS between IVD-MDs and helping to determine the quality of a method comparison study.

There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed superiority. In such trials, it can be important to ensure that error rates are controlled. This paper introduces a multi-stage design that enables the addition of new treatment arms, at any point, in a preplanned manner within a platform trial, while still maintaining control over the family-wise error rate. This paper focuses on finding the required sample size to achieve a desired level of statistical power when treatments are continued to be tested even after a superior treatment has already been found. This may be of interest if there are treatments from different sponsors which are also superior to the current control or multiple doses being tested. The calculations to determine the expected sample size is given. A motivating trial is presented in which the sample size of different configurations is studied. In addition, the approach is compared to running multiple separate trials and it is shown that in many scenarios if family-wise error rate control is needed there may not be benefit in using a platform trial when comparing the sample size of the trial.

In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person-specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allow to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA data set. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data.

Gene set analysis, a popular approach for analyzing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditions. In addition to the multitude of methods available for this task, users are typically left with many options when creating the required input and specifying the internal parameters of the chosen method. This flexibility can lead to uncertainty about the “right” choice, further reinforced by a lack of evidence-based guidance. Especially when their statistical experience is scarce, this uncertainty might entice users to produce preferable results using a “trial-and-error” approach. While it may seem unproblematic at first glance, this practice can be viewed as a form of “cherry-picking” and cause an optimistic bias, rendering the results nonreplicable on independent data. After this problem has attracted a lot of attention in the context of classical hypothesis testing, we now aim to raise awareness of such overoptimism in the different and more complex context of gene set analyses. We mimic a hypothetical researcher who systematically selects the analysis variants yielding their preferred results, thereby considering three distinct goals they might pursue. Using a selection of popular gene set analysis methods, we tweak the results in this way for two frequently used benchmark gene expression data sets. Our study indicates that the potential for overoptimism is particularly high for a group of methods frequently used despite being commonly criticized. We conclude by providing practical recommendations to counter overoptimism in research findings in gene set analysis and beyond.

The progression-free-survival ratio is a popular endpoint in oncology trials, which is frequently applied to evaluate the efficacy of molecularly targeted treatments in late-stage patients. Using elementary calculations and simulations, numerous shortcomings of the current methodology are pointed out. As a remedy to these shortcomings, an alternative methodology is proposed, using a marginal Cox model or a marginal accelerated failure time model for clustered time-to-event data. Using comprehensive simulations, it is shown that this methodology outperforms existing methods in settings where the intrapatient correlation is low to moderate. The performance of the model is further demonstrated in a real data example from a molecularly aided tumor trial. Sample size considerations are discussed.