The use of permutation tests for the analysis of parallel and stepped‐wedge cluster‐randomized trials

We investigate the use of permutation tests for the analysis of parallel and stepped‐wedge cluster‐randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time‐to‐event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster‐randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair‐specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster‐randomized trial evaluating the effect of an intervention to reduce the incidence of hospital‐acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped‐wedge designs and compare its performance with mixed‐effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd.     

Exposure enriched outcome dependent designs for longitudinal studies of gene–environment interaction

Joint effects of genetic and environmental factors have been increasingly recognized in the development of many complex human diseases. Despite the popularity of case‐control and case‐only designs, longitudinal cohort studies that can capture time‐varying outcome and exposure information have long been recommended for gene–environment (G × E) interactions. To date, literature on sampling designs for longitudinal studies of G × E interaction is quite limited. We therefore consider designs that can prioritize a subsample of the existing cohort for retrospective genotyping on the basis of currently available outcome, exposure, and covariate data. In this work, we propose stratified sampling based on summaries of individual exposures and outcome trajectories and develop a full conditional likelihood approach for estimation that adjusts for the biased sample. We compare the performance of our proposed design and analysis with combinations of different sampling designs and estimation approaches via simulation. We observe that the full conditional likelihood provides improved estimates for the G × E interaction and joint exposure effects over uncorrected complete‐case analysis, and the exposure enriched outcome trajectory dependent design outperforms other designs in terms of estimation efficiency and power for detection of the G × E interaction. We also illustrate our design and analysis using data from the Normative Aging Study, an ongoing longitudinal cohort study initiated by the Veterans Administration in 1963. Copyright © 2017 John Wiley & Sons, Ltd.     

A method to address between‐subject heterogeneity for identification of principal surrogate markers in repeated low‐dose challenge HIV vaccine studies

Repeated low‐dose challenge designs in nonhuman primate studies have recently received attention in the literature as a means of evaluating vaccines for HIV prevention and identifying immune surrogates for their protective effects. Existing methods for surrogate identification in this type of study design rely on the assumption of homogeneity across subjects (namely, independent infection risks after each challenge within each subject and conditional on covariates). In practice, random variation across subjects is likely to occur because of unmeasured biologic factors. Failure to account for this heterogeneity or within‐subject correlation can result in biased inference regarding the surrogate value of immune biomarkers and underpowered study designs for detecting surrogate endpoints. In this paper, we adopt a discrete‐time survival model with random effects to account for between‐subject heterogeneity, and we develop estimators and testing procedures for evaluating principal surrogacy of immune biomarkers. Simulation studies reveal that the heterogeneous model achieves substantial bias reduction compared to the homogeneous model, with little cost of efficiency. We recommend the use of this heterogeneous model as a complementary tool to existing methods when designing and analyzing repeated low‐dose challenge studies for evaluating surrogate endpoints.     

Childhood Asthma and Environmental Exposures at Swimming Pools: State of the Science and Research Recommendations

Objectives

Recent studies have explored the potential for swimming pool disinfection by-products (DBPs), which are respiratory irritants, to cause asthma in young children. Here we describe the state of the science on methods for understanding children’s exposure to DBPs and biologics at swimming pools and associations with new-onset childhood asthma and recommend a research agenda to improve our understanding of this issue.

Data sources

A workshop was held in Leuven, Belgium, 21–23 August 2007, to evaluate the literature and to develop a research agenda to better understand children’s exposures in the swimming pool environment and their potential associations with new-onset asthma. Participants, including clinicians, epidemiologists, exposure scientists, pool operations experts, and chemists, reviewed the literature, prepared background summaries, and held extensive discussions on the relevant published studies, knowledge of asthma characterization and exposures at swimming pools, and epidemiologic study designs.

Synthesis

Childhood swimming and new-onset childhood asthma have clear implications for public health. If attendance at indoor pools increases risk of childhood asthma, then concerns are warranted and action is necessary. If there is no such relationship, these concerns could unnecessarily deter children from indoor swimming and/or compromise water disinfection.

Conclusions

Current evidence of an association between childhood swimming and new-onset asthma is suggestive but not conclusive. Important data gaps need to be filled, particularly in exposure assessment and characterization of asthma in the very young. Participants recommended that additional evaluations using a multidisciplinary approach are needed to determine whether a clear association exists.     

中药儿童群体药动学试验设计要点

儿童由于身体发育尚未完善,药物在体内分布代谢有其自身的特点,且在不同年龄、体质量区间的差别很大,使得儿童用药剂量不能简单地参考成人用药剂量进行折算。群体药动学是研究目标人群药物代谢参数的有效方法,尤其适用于儿童这一特殊群体,可有效避免经典药物代谢取血点多所带来的伤害,并可充分了解协变量影响因素的大小,可见掌握药物在儿童中的群体典型值意义重大。提出了中药在儿童群体中开展药动学研究的试验要点与技术方法。     

鞘磷脂-胆固醇为膜材的绞股蓝总皂苷脂质体的制备及其质量评价研究

目的: 探讨用鞘磷脂与胆固醇制备绞股蓝总皂苷脂质体的可行性,并获得最佳制备工艺与处方。方法:以鞘磷脂和胆固醇为膜材制备绞股蓝总皂苷脂质体;脂质体以鱼精蛋白沉淀法进行包封率的测定;以包封率为评定指标,筛选该新型绞股蓝总皂苷脂质体的最佳制备方法;用正交设计优化该脂质体的制备工艺;用粒径、电位、AFM电镜表征该新型绞股蓝总皂苷脂质体。结果: 用鞘磷脂和胆固醇制备绞股蓝总皂苷脂质体的最佳制备方法为乙醇注入法,通过正交设计得最佳工艺为绞股蓝总皂苷与鞘磷脂质量比为1:10,鞘磷脂与胆固醇的比例4:1,缓冲液pH 7.0,水化温度45 ℃,包封率79.06%,粒径191.4 nm,电位-33.16 mV,电镜下呈规则圆形。结论: 用鞘磷脂与胆固醇制备绞股蓝总皂苷脂质体方法可行,采用最佳工艺制备的绞股蓝总皂苷包封率高,形态和粒径均较好,重复性好。     

抗流感中药冰香散提取工艺的正交实验研究

目的优选冰香散的最佳提取工艺。方法以得油率为评价指标,采用正交试验设计法考察颗粒粒度、加水量、浸泡时间、蒸馏时间4个因素对提取效果的影响,通过综合评分筛选出冰香散的最佳提取工艺。结果综合考虑各因素的影响及生产实际需要,确定的最佳提取工艺为：冰香散药材打成中粉,加入10倍量水,浸泡1．5小时,共水蒸馏提取6小时。结论该工艺条件对冰香散挥发油的生产具有一定的指导和参考意义。     

细柱五加茎多糖闪式提取工艺优化及其免疫活性研究

目的 优化细柱五加茎多糖的闪式提取工艺,并研究其细胞毒性和免疫活性。方法 运用球面对称设计试验,综合考虑料液比、提取温度、提取时间3因素,优化细柱五加茎多糖的闪式提取工艺;对闪式提取法与回流、超声2种传统提取方法进行比较。通过体外试验对提取所得多糖的细胞毒性和免疫活性进行初步研究。结果 细柱五加茎多糖闪式提取最佳工艺为料液比1∶13.2,提取温度80 ℃,提取时间420 s。在此条件下,细柱五加茎中多糖提取率为0.78%,且优于回流和超声法提取所得多糖提取率。体外活性实验结果显示,细柱五加茎多糖质量浓度在10～20 μg/mL对RAW 264.7细胞无明显毒性,在10～40 μg/mL能够促进RAW 264.7细胞NO的分泌。结论 建立了稳定、简便的细柱五加茎多糖的闪式提取工艺方法, 初步研究了其多糖的免疫活性,为合理的开发细柱五加资源提供理论依据。     

Globally optimal trial design for local decision making

Value of information methods allows decision makers to identify efficient trial design following a principle of maximizing the expected value to decision makers of information from potential trial designs relative to their expected cost. However, in health technology assessment (HTA) the restrictive assumption has been made that, prospectively, there is only expected value of sample information from research commissioned within jurisdiction. This paper extends the framework for optimal trial design and decision making within jurisdiction to allow for optimal trial design across jurisdictions. This is illustrated in identifying an optimal trial design for decision making across the US, the UK and Australia for early versus late external cephalic version for pregnant women presenting in the breech position. The expected net gain from locally optimal trial designs of US$0.72M is shown to increase to US$1.14M with a globally optimal trial design. In general, the proposed method of globally optimal trial design improves on optimal trial design within jurisdictions by: (i) reflecting the global value of non-rival information; (ii) allowing optimal allocation of trial sample across jurisdictions; (iii) avoiding market failure associated with free-rider effects, sub-optimal spreading of fixed costs and heterogeneity of trial information with multiple trials.     

Sample size for two‐stage studies with maintenance therapy

An adaptive treatment strategy (ATS) is defined as a sequence of treatments and intermediate responses. ATS' arise when chronic diseases such as cancer and depression are treated over time with various treatment alternatives depending on intermediate responses to earlier treatments. Clinical trials are often designed to compare ATSs based on appropriate designs such as sequential randomization designs. Although recent literature provides statistical methods for analyzing data from such trials, very few articles have focused on statistical power and sample size issues. This paper presents a sample size formula for comparing the survival probabilities under two treatment strategies sharing same initial, but different maintenance treatment. The formula is based on the large sample properties of inverse‐probability‐weighted estimator. Simulation study shows strong evidence that the proposed sample size formula guarantees desired power, regardless of the true distributions of survival times. Copyright © 2009 John Wiley & Sons, Ltd.