Biometrical Journal最新文献

Motivated by the study of genetic effect modifiers of cancer, we examined weighting approaches to correct for ascertainment bias in survival analysis. Outcome-dependent sampling is common in genetic epidemiology leading to study samples with too many events in comparison to the population and an overrepresentation of young, affected subjects. A usual approach to correct for ascertainment bias in this setting is to use an inverse probability-weighted Cox model, using weights based on external available population-based age-specific incidence rates of the type of cancer under investigation. However, the current approach is not general enough leading to invalid weights in relevant practical settings if oversampling of cases is not observed in all age groups. Based on the same principle of weighting observations by their inverse probability of selection, we propose a new, more general approach, called the generalized weighted approach. We show the advantage of the new generalized weighted cohort method using simulations and two real data sets. In both applications, the goal is to assess the association between common susceptibility loci identified in genome-wide association studies (GWAS) and cancer (colorectal and breast) using data collected through genetic testing in clinical genetics centers.

Utilizing non-concurrent control (NCC) data in the analysis of late-entering arms in platform trials has recently received considerable attention. While incorporating NCC can lead to increased power and lower sample sizes, it might introduce bias to the effect estimators if temporal drifts are present. Aiming to mitigate this potential bias, we propose various frequentist model-based approaches that leverage the NCC, while adjusting for time. One of the currently available models incorporates time as a categorical fixed effect, separating the trial duration into periods, defined as time intervals bounded by any arm entering or leaving the platform. In this work, we propose two extensions of this model. First, we consider an alternative definition of time by dividing the trial into fixed-length calendar time intervals. Second, we propose alternative model-based time adjustments. Specifically, we investigate adjusting for random effects and employing splines to model time with a polynomial function. We evaluate the performance of the proposed approaches in a simulation study and illustrate their use through a case study. We show that adjusting for time via a spline function controls the type I error in trials with a sufficiently smooth time trend pattern and may lead to power gains compared to the standard fixed effect model. However, the fixed effect model with period adjustment is the most robust model for arbitrary time trends, provided that the trend is equal across all arms. Especially, in trials with sudden changes in the time trend, the period-adjustment model is preferred if NCCs are included.

Outlying studies are prevalent in meta-analyses of diagnostic test accuracy studies and may lead to misleading inferences and decision-making unless their negative effect is appropriately dealt with. Statistical methods for detecting and down-weighting the impact of such studies have recently gained the attention of many researchers. However, these methods dichotomize each study in the meta-analysis as outlying or non-outlying and focus on examining the effect of outlying studies on the summary sensitivity and specificity only. We developed and evaluated a robust and flexible random-effects bivariate finite mixture model for meta-analyzing diagnostic test accuracy studies. The proposed model accounts for both the within- and across-study heterogeneity in diagnostic test results, generates the probability that each study in a meta-analysis is outlying instead of dichotomizing the status of the studies, and allows assessing the impact of outlying studies on the pooled sensitivity, pooled specificity, and between-study heterogeneity. Our simulation study and real-life data examples demonstrated that the proposed model was robust to the existence of outlying studies, produced precise point and interval estimates of the pooled sensitivity and specificity, and yielded similar results to the standard models when there were no outliers. Extensive simulations demonstrated relatively better bias and confidence interval width, but comparable root mean squared error and lesser coverage probability of the proposed model. Practitioners can use our proposed model as a stand-alone model to conduct a meta-analysis of diagnostic test accuracy studies or as an alternative sensitivity analysis model when outlying studies are present in a meta-analysis.

Censoring makes time-to-event data special and requires customized statistical techniques. Survival and event history analysis therefore builds on hazards as the identifiable quantities in the presence of rather general censoring schemes. The reason is that hazards are conditional quantities, given previous survival, which enables estimation based on the current risk set—those still alive and under observation. But it is precisely their conditional nature that has made hazards subject of critique from a causal perspective: A beneficial treatment will help patients survive longer than had they remained untreated. Hence, in a randomized trial, randomization is broken in later risk sets, which, however, are the basis for statistical inference. We survey this dilemma—after all, mapping analyses of hazards onto probabilities in randomized trials is viewed as still having a causal interpretation—and argue that a causal interpretation is possible taking a functional point of view. We illustrate matters with examples from benefit–risk assessment: Prolonged survival may lead to more adverse events, but this need not imply a worse safety profile of the novel treatment. These examples illustrate that the situation at hand is conveniently parameterized using hazards, that the need to use survival techniques is not always fully appreciated and that censoring not necessarily leads to the question of “what, if no censoring?” The discussion should concentrate on how to correctly interpret causal hazard contrasts and analyses of hazards should routinely be translated onto probabilities.

Prostatectomized patients are at risk of resurgence, and for this reason, during a follow-up period, they are monitored for prostate-specific antigen (PSA) growth, an indicator of tumor progression. The presence of tumors can be evaluated with an expensive exam, called positron emission tomography with prostate-specific membrane antigen (PET-PSMA). To justify the high cost of the PET-PSMA and, at the same time, to contain the risk for the patient, this exam should be recommended only when the evidence of tumor progression is strong. With the aim of estimating the optimal time to recommend the exam based on the patient's history and collected data, we build a hierarchical Bayesian model that describes, jointly, the PSA growth curve and the probability of a positive PET-PSMA. With our proposal, we process all past and present information about the patients PSA measurement and PET-PSMA results, in order to give an informed estimate of the optimal time, improving current practice.

The parallel cluster randomized trial with baseline (PB-CRT) is a common variant of the standard parallel cluster randomized trial (P-CRT). We define two natural estimands in the context of PB-CRTs with informative cluster sizes, the individual-average treatment effect (iATE) and cluster-average treatment effect (cATE), to address individual and cluster-level hypotheses. In this work, we theoretically derive the convergence of the unweighted and inverse cluster-period size weighted (i) independence estimating equation (IEE), (ii) fixed-effects (FE) model, (iii) exchangeable mixed-effects (EME) model, and (iv) nested-exchangeable mixed-effects (NEME) model treatment effect estimators in a PB-CRT with informative cluster sizes and continuous outcomes. Overall, we theoretically show that the unweighted and weighted IEE and FE models yield consistent estimators for the iATE and cATE estimands. Although mixed-effects models yield inconsistent estimators to these two natural estimands under informative cluster sizes, we empirically demonstrate that the EME model is surprisingly robust to bias. This is in sharp contrast to the corresponding analyses in P-CRTs and the NEME model in PB-CRTs when informative cluster sizes are present, carrying implications for practice. We report a simulation study and conclude with a re-analysis of a PB-CRT examining the effects of community youth teams on improving mental health among adolescent girls in rural eastern India.