The Risk Factors for Developing Clustered Vertebral Compression Fractures: A Single-Center Study

ObjectiveVertebral compression fractures (VCFs) are common among elderly individuals, but clustered VCFs (C-VCFs) are rare and more severe. The risk factors for C-VCFs remain unclear. Thus, we investigated the clinical characteristics of C-VCFs to identify the imminent fracture risk and improve the treatment for such patients.MethodsWe reviewed the records of patients with VCF at a single medical center between January 2011 and September 2020. Patients who had 4 or more VCFs within 1 year were categorized into the C-VCF group, and the remaining patients were paired into the control group at a ratio of 2:1. We collected demographic, clinical, laboratory, and radiologic information regarding these patients. Univariate analyses, stratified analyses, and multivariate logistic regression were performed to identify the risk factors for C-VCFs.ResultsA total of 156 patients were enrolled, of whom 52 were patients with C-VCF. Patients with C-VCF had more severe fractures and pain, with fractures occurring at uncommon sites of the spine. The independent risk factors for C-VCFs included glucocorticoid (GC) treatment (P < .001; hazard ratio [HR], 12.7), recent fracture history (P = .021; HR, 5.5), and lower trabecular bone score (TBS) (P = .044; HR, 1.6). TBS and bone mineral density had greater predictive values in patients without GC treatment (P < .001). Sex, age, and bone turnover biomarkers were not independent risk factors for C-VCFs.ConclusionC-VCFs are rare adverse consequences of severe osteoporosis, for which GC treatment, recent fracture history, and lower TBS are unique risk factors that are valuable for the early identification and prevention of C-VCFs.     

Parametric inference of neuronal response latency in presence of a background signal

Neurons are commonly characterized by spontaneous generation of action potentials (spikes), which appear without any apparent or controlled stimulation. When a stimulus is applied, the spontaneous firing may prevail and hamper identification of the effect of the stimulus. Therefore, for any rigorous analysis of evoked neuronal activity, the presence of spontaneous firing has to be taken into account. If the background signal is ignored, however small it is compared to the response activity, and however large is the delay, estimation of the response latency will be wrong, and the error will persist even when sample size is increasing. The first question is: what is the response latency to the stimulus? Answering this question becomes even more difficult if the latency is of a complex nature, for example composed of a physically implied deterministic part and a stochastic part. This scenario is considered here, where the response time is a sum of two components; the delay and the relative latency. Parametric estimators for the time delay and the response latency are derived. These estimators are evaluated on simulated data and their properties are discussed. Finally, we show that the mean of the response latency is always satisfactorily estimated, even assuming a wrong distribution for the response latency.     

Improved confidence intervals for a difference of two cause-specific cumulative incidence functions estimated in the presence of competing risks and random censoring

A cause-specific cumulative incidence function (CIF) is the probability of failure from a specific cause as a function of time. In randomized trials, a difference of cause-specific CIFs (treatment minus control) represents a treatment effect. Cause-specific CIF in each intervention arm can be estimated based on the usual non-parametric Aalen–Johansen estimator which generalizes the Kaplan–Meier estimator of CIF in the presence of competing risks. Under random censoring, asymptotically valid Wald-type confidence intervals (CIs) for a difference of cause-specific CIFs at a specific time point can be constructed using one of the published variance estimators. Unfortunately, these intervals can suffer from substantial under-coverage when the outcome of interest is a rare event, as may be the case for example in the analysis of uncommon adverse events. We propose two new approximate interval estimators for a difference of cause-specific CIFs estimated in the presence of competing risks and random censoring. Theoretical analysis and simulations indicate that the new interval estimators are superior to the Wald CIs in the sense of avoiding substantial under-coverage with rare events, while being equivalent to the Wald CIs asymptotically. In the absence of censoring, one of the two proposed interval estimators reduces to the well-known Agresti–Caffo CI for a difference of two binomial parameters. The new methods can be easily implemented with any software package producing point and variance estimates for the Aalen–Johansen estimator, as illustrated in a real data example.     

Comparison of normal distribution–based and nonparametric decision limits on the GH‐2000 score for detecting growth hormone misuse (doping) in sport

This paper is motivated by the GH‐2000 biomarker test, though the discussion is applicable to other diagnostic tests. The GH‐2000 biomarker test has been developed as a powerful technique to detect growth hormone misuse by athletes, based on the GH‐2000 score. Decision limits on the GH‐2000 score have been developed and incorporated into the guidelines of the World Anti‐Doping Agency (WADA). These decision limits are constructed, however, under the assumption that the GH‐2000 score follows a normal distribution. As it is difficult to affirm the normality of a distribution based on a finite sample, nonparametric decision limits, readily available in the statistical literature, are viable alternatives. In this paper, we compare the normal distribution–based and nonparametric decision limits. We show that the decision limit based on the normal distribution may deviate significantly from the nominal confidence level or nominal FPR when the distribution of the GH‐2000 score departs only slightly from the normal distribution. While a nonparametric decision limit does not assume any specific distribution of the GH‐2000 score and always guarantees the nominal confidence level and FPR, it requires a much larger sample size than the normal distribution–based decision limit. Due to the stringent FPR of the GH‐2000 biomarker test used by WADA, the sample sizes currently available are much too small, and it will take many years of testing to have the minimum sample size required, in order to use the nonparametric decision limits. Large sample theory about the normal distribution–based and nonparametric decision limits is also developed in this paper to help understanding their behaviours when the sample size is large.     

A comparison of methods for constructing confidence intervals after phase II/III clinical trials

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.     

台风干扰海岸林受损及灾后恢复的影响因素研究进展

随着气候变暖,台风登陆的强度及频率均呈增加趋势,其对海岸森林的影响也日益严重,从而危及沿海生态安全。但因台风登陆的不确定性、海岸森林恢复的滞后性和数据源受限等影响,当前对台风后海岸森林受损及灾后恢复的影响因素研究仍存在很大的不确定性。台风登陆导致海岸森林枝叶脱落、树干折断和根系裸露等,受树种特征(冠层、树干和根系),林分结构(密度、组成),立地条件(地形、土壤)和台风特征(强度、频率)等影响,海岸森林在不同研究尺度的受损存在明显的空间异质性,单一因素难以完全解释台风后海岸森林的受损格局。台风后部分海岸森林因受损严重而死亡,其余仍以种子萌发、幼苗/幼树生长或萌生更新的方式持续恢复。海岸森林在灾后的恢复周期从几个月到几十年不等,其具体时长与树种更新策略(实生、萌生),森林类型(人工林、天然林),树木受损程度(轻度、重度)及区域水热条件(水分、热量)等有关。未来仍需借助多源遥感数据,并结合台风后的固定样方调查、野外采样和室内样品分析,对海岸森林的受损和恢复格局开展多时空尺度研究,阐明不同受损程度下树木的水分传输和光合产物分配过程及其差异,全面揭示台风干扰对海岸森林的影响机制。