Analysis of time-to-pregnancy data

Statistical models for time-to-pregnancy data are usually formulated in discrete time. Two approaches were surveyed, including methods of accounting for both known heterogeneity (covariates, possibly time-dependent) and unknown heterogeneity (frailty). Delicate censoring and truncation patterns arose for prospective and, particularly, retrospective sampling designs. The inclusion of several pregnancies per couple presents new challenges and possibilities.     

Analysis of longitudinal Gaussian data with missing data on the response variable

BACKGROUND: Using an application and a simulation study we show the bias induced by missing data in the outcome in longitudinal studies and discuss suitable statistical methods according to the type of missing responses when the variable under study is gaussian. Method: The model used for the analysis of gaussian longitudinal data is the mixed effects linear model. When the probability of response does not depend on the missing values of the outcome and on the parameters of the linear model, missing data are ignorable, and parameters of the mixed effects linear model may be estimated by the maximum likelihood method with classical softwares. When the missing data are non ignorable, several methods have been proposed. We describe the method proposed by Diggle and Kenward (1994) (DK method) for which a software is available. This model consists in the combination of a linear mixed effects model for the outcome variable and a logistic model for the probability of response which depends on the outcome variable. RESULTS: A simulation study shows the efficacy of this method and its limits when the data are not normal. In this case, estimators obtained by the DK approach may be more biased than estimators obtained under the hypothesis of ignorable missing data even if the data are non ignorable. Data of the Paquid cohort about the evolution of the scores to a neuropsychological test among elderly subjects show the bias of a naive analysis using all available data. Although missing responses are not ignorable in this study, estimates of the linear mixed effects model are not very different using the DK approach and the hypothesis of ignorable missing data. CONCLUSION: Statistical methods for longitudinal data including non ignorable missing responses are sensitive to hypotheses difficult to verify. Thus, it will be better in practical applications to perform an analysis under the hypothesis of ignorable missing responses and compare the results obtained with several approaches for non ignorable missing data. However, such a strategy requires development of new softwares.     

Analysis of dynamic cohort data

Left-truncated and interval-censored data, termed dynamic cohort data, arise in longitudinal studies with rolling admissions and only occasional follow-up. The authors compared four approaches for analyzing such data: a constant hazard model; maximum likelihood estimation with flexible parametric models; the midpoint method, in which the midpoint of the last negative and first positive test result is used in a Cox proportional hazards model that accounts for left truncation; and a semiparametric method that uses imputed failure times in the Cox model. By using a simulation study, they assessed the performance of these approaches under conditions that can arise in observational studies: changes in disease incidence and changes in the underlying population. The simulation results indicated that the constant hazard model and midpoint method were inadequate and that the flexible parametric model was useful when enough parameters were used in modeling the baseline hazard. The semiparametric method ensured correct parameter (odds ratio) estimation when the baseline hazard was misspecified, but the trade-off increased computational complexity. In this paper, a study of the incidence of human immunodeficiency virus in patients repeatedly tested for the virus at a sexually transmitted disease clinic in New Orleans, Louisiana, illustrates the methods used.     

Analysis of clustered matched-pair data

Evaluation of the performance of a new diagnostic procedure with respect to a standard procedure arises frequently in practice. The response of interest, often in a dichotomous form, is measured twice, once with each procedure. The two procedures are administered to either two matched individuals, or when practical, to the same individual. A large sample test for matched-pair data is the McNemar test. The main assumption of this test is independent paired responses; however, when more than one outcome from an individual is measured by each procedure, the data are clustered. Examples of such cases can be seen in dental and ophthalmology studies. Variance adjustment methods for the analysis of clustered matched-pair data have been proposed; however, because of unequal cluster sizes, variability of correlation structures within a cluster (within paired responses in a cluster as well as between paired responses in a cluster), and unequal success probabilities among the clusters, the performances of some available methods are not consistent. This research proposes a simple adjustment to the McNemar test for the analysis of clustered matched-pair data. Method of moments is used to calculate a consistent variance estimator. Using Monte Carlo simulation, the size and power of the proposed test are compared to those of two currently available methods. To illustrate practical application, clustered matched-pair data from two clinical studies are analysed.     

Analysis of repeated binary data: sensitivity to missing data

BACKGROUND: In longitudinal studies, it is extremely rare that all the planned measurements are actually performed. Missing data are often consecutive to drop-outs, but may also be intermittent. In both cases, the analysis of incomplete data necessarily requires assumptions that are generally unverifiable, and the need for sensitivity analyses has been advocated over the past few years. In this article, the attention will be given to longitudinal binary data. METHODS: A method is proposed, which is based on a log-linear model. A sensitivity parameter is introduced that represents the relationship between the response mechanism and the missing data mechanism. It is recommended not to estimate this parameter, but to consider a range of plausible values, and to estimate the parameters of interest conditionally on these plausible values. This allows to assess the sensitivity of the conclusion of a study to various assumptions regarding the missing data mechanism. RESULTS: This method was applied to a randomized clinical trial comparing the efficacy of two treatment regimens in patients with persistent asthma. The sensitivity analysis showed that the conclusion of this study was robust to missing data.     

214例自杀住院患者资料分析

目的了解沿海中等城市自杀住院患者的特征，为预防和减少自杀提供依据。方法对广东省汕头市中心医院2002年1月至2004年12月214例自杀住院患者进行综合分析。结果214例自杀住院患者年龄最小12岁，最大95岁；21～40岁115例，占53．74％。男女之比为1：1．46；但41岁以上男性多于女性，男：女：1．61：1。自杀多发季节顺序为：秋，夏，春，冬。第一自杀原因是“与人争吵”，占43．46％，其中“与家人（包括配偶、父母、公婆等）争吵”占83．87％。但城市男性与61岁以上老年人第一自杀原因是“躯体疾病”，分别占35．48％和62．50％。自杀方式多达10种，以服毒（药）为多，占85．50％。农村自杀患者25．90％服除草剂。治疗无效率9．35％；死亡率5．14％。结论构建和谐社会，避免家庭矛盾，关爱身心疾患者，加强自杀高危人群的健康教育和危机干预，是预防自杀的重要措施；对中年男性尤其城市男性的身心健康问题，社会应给予足够重视。对发生情感激烈冲突者及时给予疏导，并管理好各种毒物和有毒性作用的药品、用品，可以减少冲动型自杀行为的发生。     

Analysis of survival data by the proportional odds model

A model is presented for the analysis of lifetime data in which the rates of mortality for separate groups of patients converge with time. A non-parametric estimate is given for the survivor function. The theoretical basis for the model assumes that prognostic factors have a multiplicative effect on the odds against survival beyond any given time. The model is fitted to data using maximum likelihood estimation, and an example of its use in the analysis of a lung cancer trial is given.     

Analysis of incomplete public health data

The problem of dealing with missing values is common throughout statistics and is very prominent with epidemiologic data in the broad sense. Not only do data collection procedures break down, but subjects may be lost to follow up, or simply withdraw their consent without further providing a reason for doing so. In this paper, we review a framework for handling incomplete studies, and then concentrate on a specific case. It comes from a complex health interview survey, conducted in Belgium in 1997, where different types of missingness arise at various levels of the hierarchical sampling procedure.     

Analysis of longitudinal data from twins

Longitudinal data from twin pairs may be used to determine how the genetic effects influencing a quantitative trait change with age. Here a model for mixed longitudinal data of Huggins and Loesch [1998] on unrelated individuals is extended to twin studies. The model is fitted using robust statistical methods and a bootstrap procedure is proposed to estimate the percentiles. The method is applied to longitudinal twin data on body mass index in male and female twin pairs aged 5-18 years.     

Analysis of drug dissolution data.

Drug absorption in the human body depends on the dissolution rate of the drug. Suitable dissolution characteristics are important to ensure that the drug will achieve the desired therapeutic effects. To assess the similarity of dissolution rates of several drug lots, we apply a general growth curve model with different covariance structures. The Box-Cox power transformation and the naive log transformation are applied to a function of the dissolution rate. The predictive sample-reuse, or cross-validation, method is employed in selecting an appropriate model with best predictive accuracy. A testing procedure for examining the similarity among the drug lots is also conducted. A partially Bayesian approach is used for the assessment of dissolution equivalence.     

38876例住院患者梅毒监测数据分析

目的调查近1年武汉大学人民医院住院的手术前、输血前、介入前以及辅助生殖技术前患者血清梅毒螺旋体（Treponema pallidum,TP）抗体的检测情况,对梅毒的疾病监测数据进行分析。方法用甲苯胺红不加热血清试验（Toluidine Red Untreated Serum Test,TRUST）和化学发光微粒子免疫检测法（Chemiluminescence microparticle immunoassay,CMIA）分别检测TP非特异性抗体和特异性抗体。结果手术前、输血前、介入前和辅助生殖技术前患者TP特异性抗体阳性率分别为1．54％,1．40％,1．79％和1．44％,TP非特异性抗体阳性率分别为0．28％,0．23％,0．11％,0．41％。统计学分析发现,男、女组TP特异性抗体和非特异性抗体阳性率差异均无统计学意义（P〉0．05）;而不同年龄组间TP特异性抗体阳性率差异有统计学意义（P〈0．05）,其中男性组〉60岁患者TP抗体阳性率最高,女性组年龄41-50岁患者TP抗体阳性率最高。结论手术前、输血前、介入前及辅助生殖技术前患者的TP检出率均较高,且与年龄有关。有必要对手术前、输血前、介入前及辅助生殖技术前患者进行TP检测。     

浙江省2004年流感疫情报告与哨点监测的一致性评价

目的了解浙江省流感发病情况及传染病网络报告系统和流感监测系统一致性.方法收集2004年浙江省传染病网络报告系统资料和流感监测网络的监测资料,分析流感发病的时间、地点、和人群分布情况并对两种资料进行比较.结果流感发病4306例,男性2439例,女性1867例,男女性别比为1.3∶1.20岁以下年龄组发病较高,分别为0岁～组2185例(50.7%),10岁～组1382例(32.1%).年龄越小,发病数越多.儿童和学生发病较高,分别占33.5%和45.0%;从时间趋势来看,3月有个明显的高峰,9月发病数也较高;不同地区发病差异较大,嘉兴地区最高,其次是温州.监测资料分析,6个监测点共报告流感样病例70941例,门诊就诊病例总数612641例,ILI%为11.58%.将2004年流感监测系统资料与全省网络报告系统流感发病资料进行比较发现,全省两个系统资料反映的时间趋势基本一致,即3月份和9月份各有一个高峰期,7月份发病数较低.结论全年流感发病呈现两个流行峰,分别位于3月和9月,而且在上半年流行水平较高;流感监测资料和全省网络报告系统资料的一致性较高.     

麻醉机不良事件相关数据分析

目的:分析麻醉机不良事件相关数据,探讨影响麻醉机安全性的相关因素,为临床合理及安全使用麻醉机提供有效信息。方法:检索美国食品药品监督管理局(FDA)医疗器械制造商和用户设施设备体验(MAUDE)数据库,从MAUDE数据库中2018年8-12月麻醉机225例不良事件报告中整理归纳出221例有效报告数据,对不良事件报告来源、不良事件类型以及不良事件主要表现和原因进行统计分析。结果:麻醉机不良事件主要表现为患者死亡、患者受伤和仅设备故障未对患者造成伤害3类,其中患者死亡3例,占1.36%;患者受伤(包含轻伤及重伤)9例,占4.07%;设备故障209例,占94.57%,设备故障为不良事件多发区。结论:麻醉机生产单位应提高生产技术,加强故障检测及自动报警功能;使用单位应严格按照诊疗规范操作,提高临床医务人员和维修人员的风险意识,加强设备日常维护和保养,为医疗器械安全有效使用提供保障。     

Analysis of clinical data with breached blindness

In clinical trials, blinding is usually employed to prevent bias that may be introduced due to the knowledge of the identity of the treatment codes. This bias could alter the conclusion of statistical inference on the treatment effect. The purpose of this article is to propose a method for analysing clinical data with breached blindness. The example regarding the study of the effectiveness of an appetite suppressant in weight loss in obese woman as described in Brownell and Stunkard (Am. J. Psychiatry 1982; 139:1487-1489) is used to illustrate the application of the proposed methods.