Estimation of a common odds ratio in paired-cluster randomization designs

We develop two estimators of a common odds ratio psi for designs in which the investigator randomly assigns each of two clusters to interventions within strata. The estimators rely on an empirical adjustment for clustering to provide improved estimators of psi relative to the standard Woolf and Mantel-Haenszel estimators, respectively. The results of a simulation study show that the suggested adjustment improves the accuracy of both of these well-known estimators under conditions likely to arise in practice. We find the clustered Woolf estimator as particularly effective in terms of mean squared error reduction. We also discuss interval estimation.     

关于社区健康促进项目评价设计的理论探讨

社区健康促进项目广泛开展,由于项目的复杂性,项目评价往往存在一些问题。结合社区项目评价实践,指出社区项目评价设计方面存在的主要问题以及解决思路,主要在项目评价设计时机、构建项目理论和抽样调查样本量计算等方面进行了理论探讨以及案例分析,以期对完善社区干预项目的评价设计提供参考。     

Using deep learning and electronic health records to detect Noonan syndrome in pediatric patients

PurposeThe variable expressivity and multisystem features of Noonan syndrome (NS) make it difficult for patients to obtain a timely diagnosis. Genetic testing can confirm a diagnosis, but underdiagnosis is prevalent owing to a lack of recognition and referral for testing. Our study investigated the utility of using electronic health records (EHRs) to identify patients at high risk of NS.MethodsUsing diagnosis texts extracted from Cincinnati Children’s Hospital’s EHR database, we constructed deep learning models from 162 NS cases and 16,200 putative controls. Performance was evaluated on 2 independent test sets, one containing patients with NS who were previously diagnosed and the other containing patients with undiagnosed NS.ResultsOur novel method performed significantly better than the previous method, with the convolutional neural network model achieving the highest area under the precision-recall curve in both test sets (diagnosed: 0.43, undiagnosed: 0.16).ConclusionThe results suggested the validity of using text-based deep learning methods to analyze EHR and showed the value of this approach as a potential tool to identify patients with features of rare diseases. Given the paucity of medical geneticists, this has the potential to reduce disease underdiagnosis by prioritizing patients who will benefit most from a genetics referral.     

A gene-to-patient approach uplifts novel disease gene discovery and identifies 18 putative novel disease genes

PurposeExome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck.MethodsWe apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries.ResultsResults from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction–constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries.ConclusionUsing large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.     

Biallelic loss-of-function variants in RABGAP1 cause a novel neurodevelopmental syndrome

PurposeRABGAP1 is a GTPase-activating protein implicated in a variety of cellular and molecular processes, including mitosis, cell migration, vesicular trafficking, and mTOR signaling. There are no known Mendelian diseases caused by variants in RABGAP1.MethodsThrough GeneMatcher, we identified 5 patients from 3 unrelated families with homozygous variants in the RABGAP1 gene found on exome sequencing. We established lymphoblastoid cells lines derived from an affected individual and her parents and performed RNA sequencing and functional studies. Rabgap1 knockout mice were generated and phenotyped.ResultsWe report 5 patients presenting with a common constellation of features, including global developmental delay/intellectual disability, microcephaly, bilateral sensorineural hearing loss, and seizures, as well as overlapping dysmorphic features. Neuroimaging revealed common features, including delayed myelination, white matter volume loss, ventriculomegaly, and thinning of the corpus callosum. Functional analysis of patient cells revealed downregulated mTOR signaling and abnormal localization of early endosomes and lysosomes. Rabgap1 knockout mice exhibited several features in common with the patient cohort, including microcephaly, thinning of the corpus callosum, and ventriculomegaly.ConclusionCollectively, our results provide evidence of a novel neurodevelopmental syndrome caused by biallelic loss-of-function variants in RABGAP1.     

The EPIGENE network: A French initiative to harmonize and improve the nationwide diagnosis of monogenic epilepsies

BackgroundThe EPIGENE network was created in 2014 by four multidisciplinary teams composed of geneticists, pediatric neurologists and neurologists specialized in epileptology and neurophysiology. The ambition of the network was to harmonize and improve the diagnostic strategy of Mendelian epileptic disorders using next-generation sequencing, in France. Over the years, five additional centers have joined EPIGENE and the network has been working in close collaboration, since 2018, with the French reference center for rare epilepsies (CRéER).ResultsSince 2014, biannual meetings have led to the design of four successive versions of a monogenic epilepsy gene panel (PAGEM), increasing from 68 to 144 genes. A total of 4035 index cases with epileptic disorders have been analyzed with a diagnostic yield of 31% (n = 1265/4035). The top 10 genes, SCN1A, KCNQ2, STXBP1, SCN2A, SCN8A, PRRT2, PCDH19, KCNT1, SYNGAP1, and GRIN2A, account for one-sixth of patients and half of the diagnoses provided by the PAGEM.ConclusionThese results suggest that a gene-panel approach is an efficient first-tier test for the genetic diagnosis of Mendelian epileptic disorders. In a near future, French patients with “drug-resistant epilepsies with seizure-onset in the first two-years of life” can benefit from whole-genome sequencing (WGS), as a second line genetic screening with the implementation of the 2025 French Genomic Medicine Plan. The EPIGENE network has also promoted scientific collaborations on genetic epilepsies within CRéER.     

精神卫生科研如何严格遵守试验设计四原则之随机原则

明确阐释在进行精神卫生临床试验设计时,应正确把握"随机原则"的意义和要领。从基本常识出发,并基于精神卫生科研的特点,寻找和发现在此研究领域中,怎样做才能被称为严格遵守了"随机原则"。通过结合本专业的特点,并结合实例,获得如下的结果,即在进行精神卫生临床试验设计时,必须把握好以下三个方面:1正确选定随机化的种类;2正确方便地实现随机化;3必须尽可能避免违背随机化的做法。在如何严格遵守随机原则问题上,正确把握好前述提及的三个方面,就是抓住了问题的本质,是提高临床试验研究质量的一个重要环节。     

Sample size considerations for stratified cluster randomization design with binary outcomes and varying cluster size

Stratified cluster randomization trials (CRTs) have been frequently employed in clinical and healthcare research. Comparing with simple randomized CRTs, stratified CRTs reduce the imbalance of baseline prognostic factors among different intervention groups. Due to the popularity, there has been a growing interest in methodological development on sample size estimation and power analysis for stratified CRTs; however, existing work mostly assumes equal cluster size within each stratum and uses multilevel models. Clusters are often naturally formed with random sizes in CRTs. With varying cluster size, commonly used ad hoc approaches ignore the variability in cluster size, which may underestimate (overestimate) the required number of clusters for each group per stratum and lead to underpowered (overpowered) clinical trials. We propose closed-form sample size formulas for estimating the required total number of subjects and for estimating the number of clusters for each group per stratum, based on Cochran-Mantel-Haenszel statistic for stratified cluster randomization design with binary outcomes, accounting for both clustering and varying cluster size. We investigate the impact of various design parameters on the relative change in the required number of clusters for each group per stratum due to varying cluster size. Simulation studies are conducted to evaluate the finite-sample performance of the proposed sample size method. A real application example of a pragmatic stratified CRT of a triad of chronic kidney disease, diabetes, and hypertension is presented for illustration.     

抗击超级细菌:耐药酶的起源研究和变异预测

随着抗菌药物在临床和农、林、牧、副、鱼业上广泛应用,细菌的耐药性现象越来越严重,新出现的携带新德里金属β-内酰胺酶-1(new delhi metallo-β-lactamase-1,NDM-1)基因的超级细菌,几乎对临床上所有抗菌药物均具有耐药性,而且这一耐药基因位于质粒上,会在细菌中水平转移,对人类健康具有潜在的极大威胁。更严重的是,目前尚无法预知今后将出现何种新的耐药基因,但抗菌药物选择性压力的结果必然导致新的更为棘手的耐药性出现。国内外已有一些探索性工作试图揭示耐药基因的起源,并着力于预测这些基因今后可能的演变方向,以期在新的耐药性出现前作出应对。该文将以β-内酰胺耐药酶为主,概述国内外在耐药酶起源、自然进化、模拟进化等领域的研究进展,希望能为抗击超级细菌提供新的实验思路。