队列研究中纵向缺失数据填补方法的模拟研究

目的 数据缺失是队列研究中几乎无法避免的问题。本文旨在通过模拟研究,比较当前常见的8种缺失数据处理方法在纵向缺失数据中的填补效果,为纵向缺失数据的处理提供有价值的参考。方法 模拟研究基于R语言编程实现,通过Monte Carlo方法产生纵向缺失数据,通过比较不同填补方法的平均绝对偏差、平均相对偏差和回归分析的Ⅰ类错误,评价不同填补方法对于纵向缺失数据的填补效果及对后续多因素分析的影响。结果 均值填补、k近邻填补（KNN）、回归填补和随机森林的填补效果接近,且表现稳定;多重插补和热卡填充次于以上填补方法;K均值聚类和EM算法填补效果最差,表现也最不稳定。均值填补、EM算法、随机森林、KNN和回归填补可较好地控制Ⅰ类错误,多重插补、热卡填充和K均值聚类不能有效控制Ⅰ类错误。结论 对于纵向缺失数据,在随机缺失机制下,均值填补、KNN、回归填补和随机森林均可作为较好的填补方法,当缺失比例不太大时,多重插补和热卡填充也表现较好,不推荐K均值聚类和EM算法。     

海南省传染病漏报调查分析

目的　为探讨法定传染病疫情的报告情况和漏报原因,为制订传染病防治策略提供理论依据。方法　采用分层抽样和随机抽样方法对１５ 家医疗机构和２ ９３７ 名疟区居民进行流行病学调查。结果　医疗机构传染病漏报率为４４ ．４０ ％ ,个体诊所最为严重,既不填写传染病登记本,也不报告疫情。漏报较严重的病种是疟疾、肝炎、梅毒、淋病。居民疟疾漏报率为９１ ．７３ ％ ,不同疟区差异具有显著性（ Ｐ＜ ０．０１）。结论　传染病疫情报告存在严重的漏报,应不断完善报告制度,提高报告质量     

ACCOUNTING FOR DROPOUT BIAS USING MIXED-EFFECTS MODELS

Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic when subjects discontinue (dropout) prior to completing the study. This study assessed the merits of likelihood-based repeated measures analyses (MMRM) compared with fixed-effects analysis of variance where missing values were imputed using the last observation carried forward approach (LOCF) in accounting for dropout bias. Comparisons were made in simulated data and in data from a randomized clinical trial. Subject dropout was introduced in the simulated data to generate ignorable and nonignorable missingness. Estimates of treatment group differences in mean change from baseline to endpoint from MMRM were, on average, markedly closer to the true value than estimates from LOCF in every scenario simulated. Standard errors and confidence intervals from MMRM accurately reflected the uncertainty of the estimates, whereas standard errors and confidence intervals from LOCF underestimated uncertainty.     

Total Talar Extrusion without Soft Tissue Attachments

Total talar extrusion without a soft tissue attachment is an extremely rare injury and is rarely reported. Appropriate treatment remains controversial. We describe the long-term outcomes of two patients who had complete talar extrusion without remaining soft tissue attachment treated with arthrodesis. Both of our patients had complications such as infection and progressive osteolysis. We suggest reimplantation of the extruded talus after thorough debridement as soon as possible as a reasonable option unless the talus is contaminated or missing, because an open wound may arise from inside to outside.     

The Role of Multiple Imputation in Noninferiority Trials for Binary Outcomes

We consider the role of multiple imputation (MI) when analyzing noninferiority (NI) clinical trials with missing data. When the endpoint is measured longitudinally, direct-likelihood methods can be used. In this article, the focus is on the situation in which the endpoint is not measured longitudinally but other relevant data are measured at or after baseline prior to planned collection of the primary endpoint data. Simulation results are presented for various scenarios based on the missingness mechanism, the dropout rate, and the size of NI margin. When the endpoint is binary, the ratio of the amount of missing data to the noninferiority margin will affect the operating characteristics of any analysis strategy (whether imputation based or not), an issue that is unique to noninferiority trials. Biased estimates of treatment effect under missingness, not completely at random, may arise when using a misspecified imputation model lacking treatment effect, resulting in substantially inflated Type I error rates in noninferiority trials by making the two groups appear more similar, opposite the usual impact in superiority trials. As in superiority trials, MI will have most benefit when data are missing at random, and the important predictor variables are included in the imputation model.     

Effects in Adherent Subjects

Dropouts confound the treatment effect when the outcome and the dropout process both depend on subject characteristics. If dropout is unrelated to treatment, there is an unconfounded effect, but it is the effect in a principal stratum, rather than a de jure effect. There are at least two different definitions of the effect if all subjects adhered, giving effects different numerically from each other and from the effect in the adherent principal stratum. Estimation of either of these two effects requires an assumption (MAR) different from but sometimes confused with the assumption that dropout is unrelated to treatment.     

Development of a food composition database to study complementary feeding: An Italian experience

Food Composition Databases (FCDBs) are important tools for epidemiological research, public health nutrition and education, clinical practice and nutrition declaration on food labels. The aim of this paper is to describe the methodology used to compile a FCDB for the analysis on the dietary intake of an Italian cohort of infants, and to assess its strengths and weaknesses. Dietary data were collected using a 3-DD records compiled at 6, 9 and 12 months of age of the infants. We developed a FCDB that contains data from the Italian and the USDA food composition databases and other sources. Our FCDB includes 563 food derived from the analysis of 623 3-DD records. Non-commercial products are more consumed than commercial products (25.5% vs. 9.1% at 6 months, 58.4% vs. 18.1% at 9 months and 77.8% vs. 11.3% at 12 months) but the latter are the main source of missing data (>70% in each database, with the exception of the energy components), which is one of the major weaknesses of this tool. An integrated system of data collection (NUTRIRETE.lab) that brings together food composition data from public and private laboratories will allow us to build a more complete and representative food composition database.     

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features.     

Missing Data in Noninferiority Trials

Missing data in clinical trials has been widely discussed in the literature, but issues specific to missing data in noninferiority trials have rarely been addressed. The goal of this article is to present missing data issues that are particularly important in noninferiority trials. Issues of assay sensitivity and the constancy assumption are affected by missing data. Importantly, these issues are not solved by per protocol analyses which remove patient data based on postrandomization criteria. We advocate collecting data to the extent possible for sensitivity analyses. We discuss some other issues that remain unresolved in assessing the impact of missing data in noninferiority trials. A simulation analysis of different strategies for assessing noninferiority in the presence of missing data is reported for a clinical trial comparing two treatments. Single imputation procedures and observed case analyses resulted in reduced power due to missing data and occasionally in inflation in Type I error rate or bias in estimates of treatment effect. The mixed-effect model repeated measures approach resulted in a method that controlled the Type I error rate when data are missing at random, and often with higher power than the other two methods. Further work on multiple imputation procedures is desired.