Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi‐outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real‐world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.     

Sampling‐based estimation for massive survival data with additive hazards model

For massive survival data, we propose a subsampling algorithm to efficiently approximate the estimates of regression parameters in the additive hazards model. We establish consistency and asymptotic normality of the subsample‐based estimator given the full data. The optimal subsampling probabilities are obtained via minimizing asymptotic variance of the resulting estimator. The subsample‐based procedure can largely reduce the computational cost compared with the full data method. In numerical simulations, our method has low bias and satisfactory coverage probabilities. We provide an illustrative example on the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program.     

Mendelian randomization analysis with survival outcomes

Mendelian randomization (MR) is an established approach for assessing the causal effects of heritable exposures on outcomes. Outcomes of interest often include binary clinical endpoints, but may also include censored survival times. We explore the implications of both the Cox proportional hazard model and the additive hazard model in the context of MR, with a specific emphasis on two‐stage methods. We show that naive application of standard MR approaches to censored survival times may induce significant bias. Through simulations and analysis of data from the Women's Health Initiative, we provide practical advice on modeling survival outcomes in MRs.     

Development of a composite indicator to prioritize districts for implementation of human immunodeficiency virus programmes in Maharashtra,India

A key recommendation of the National AIDS Control Programme‐IV of India was to develop new strategies for geo‐prioritization of the human immunodeficiency virus (HIV) epidemic. We conducted this study to categorize the districts in Maharashtra (India) based on a multidimensional framework for geo‐prioritization of services. Programmatic data on trends of HIV prevalence, coverage of marginalized populations and vulnerability factors were included. A composite indicator based on these was developed, and the cumulative score was calculated for each district. HIV prevalence among general population has declined steadily from 0.60% in 2007 to 0.33% in 2017. The programme coverage was stable but inadequate for men who have sex with men (MSM). The coverage for female sex workers (FSWs) was inadequate and reduced over time. Nine districts were categorized as high priority, 13 as moderate priority and 11 were classified as low‐priority districts based on burden and vulnerability for HIV. The high‐priority districts were Pune, Solapur and Yavatmal for FSW interventions and Pune, Thane and Latur for MSM interventions. This multidimensional indicator is based on existing programmatic data, dynamic and can be made state‐specific. It is useful to categorize and prioritize districts for allocation of resources and geo‐prioritization of services in resource limited settings.     

医疗服务价格调整对公立医院收入结构的影响研究——基于成分数据分析方法

目的基于三级甲等公立医院运营数据,分析医疗服务价格调整对医院收入结构的影响。方法引入成分数据分析方法,利用浙江省12家三级甲等公立医院(综合医院7家、中医类医院2家、专科医院3家) 2019年1—7月收入结构数据模拟无政策干预下8—12月数据,比较实际数据与模拟数据的差异。结果调价后综合医院、中医类医院和专科医院的医疗服务收入占比分别提高了4.77、4.01和2.54个百分点,检验和检查收入占比分别减少了0.60、0.78和2.94个百分点。综合性医院和中医类医院的药占比降低了1.87和5.29个百分点。各类医院服务收入增加了10 890.14万元,药品收入减少了6 521.79万元,检验检查收入减少了3 065.96万元。结论医疗服务价格调整政策提高了服务收入占比,降低了物耗性收入占比。下一步需建立医疗服务价格动态调整机制,继续控制医疗费用不合理增长,并关注不同类型医院收入结构的差异。     

内蒙古4盟市后天梅毒报告病例数据质量调查

目的　了解内蒙古自治区梅毒报告病例数据的质量,发现存在的问题,提出改进措施。方法　选取2018年7月1日—2019年6月30日内蒙古自治区4个盟市的30家医疗机构的895例后天梅毒报告病例进行调查,包括核对传染病报告卡、临床和实验室原始资料等,评价其信息质量指标和诊断质量指标。结果　4个盟市的报卡填写完整率、正确率、信息录入正确率、病例报告及时率、过度报告率、重复报告率分别为97.65%、92.63%、89.16%、91.06%、13.85%、3.46%。不同盟市的报告卡的填写正确率、病例报告及时率、过度报告率和重复报告率的差异均有统计学意义（P均＜0.05）。后天梅毒病例诊断正确率为66.93%,不同地区间差异有统计学意义（χ2=45.963,P=0.000）。不同地区间一期梅毒分期正确率、隐性梅毒分类正确率的差异均有统计学意义（P均＜0.05）。皮肤性病科、非皮肤性病科报告病例正确率分别为74.90%、57.63%,差异有统计学意义（χ2=29.967,P=0.000）。结论　内蒙古自治区梅毒疫情报告质量参差不齐,梅毒诊断正确率、分期正确率低,影响梅毒发病率与发病趋势的分析,须加强梅毒报告、诊断标准的培训,切实提高病例数据的质量。     

基于点云数据的髋臼置钉通道全局优化

针对髋臼前后柱置钉通道多截面调控规划法的不足,该研究提出了基于大密度点云数据、相关理论、软件内置功能及自编程序的规划方法,实现了对置钉通道总长的全局优化,同时进行了基于7截面、11截面调控规划法以及点云数据的前柱最大内接圆柱体虚拟置钉实验,分析了实验结果及形成机理,验证了基于点云数据规划法的全局优化效果,但对多截面调控规划法的调控有效性表示还存有疑问,此外,还对基于点云数据的髋臼置钉通道规划方法应用的相关原理和操作技巧作了简略介绍。     

基于成分等时替代模型的高中生身体活动与焦虑症状相关性分析

目的 了解高中生在居家网课期间身体活动、焦虑症状状况及其相关性,为高中生的身心健康干预提供科学依据。方法 2020年4月,某校高中生以问卷星形式完成在线调查,内容包括:一般资料、生活作息、身体活动、焦虑自评量表。采用成分等时替代模型分析高中生24 h行为活动（包括身体活动、静坐行为和睡眠）与焦虑症状的相关性。结果 共调查1 324人,高中生焦虑症状患病率为23.34%。10 min中高强度身体活动（MVPA）替代久坐行为（SB）、低强度身体活动（LPA）与焦虑得分减少相关有统计学意义（平均变化量分别为 - 0.25（95%CI: - 0.47～ - 0.04）、 - 0.24（95%CI : - 0.46～ - 0.02））。随着 MVPA 替代 LPA、SB和睡眠（SLP）的时间增加,焦虑得分预测值减少量增加。结论 高中生居家网课期间焦虑情绪不容忽视,进行中高强度身体活动能有效预防焦虑症状发生。     

粗糙集在构建骨肿瘤辅助诊断知识库的应用研究

在骨肿瘤辅助诊断专家系统研究中，知识库的建立是一直困扰系统建立的瓶颈问题，知识库的好坏不仅影响到后续推理过程，而且还对诊断的精度有着很大的影响。本文使用基于粗糙集理论的数据挖掘技术，对已确诊病例资料数据库进行数据分析，建立骨肿瘤与患者临床表现之间的推理关系，并结合医生的临床实践，最终形成一个比较完备的辅助诊断知识数据库，为后续的推理、解释程序打下良好的基础。     

一种心电数据压缩算法的研究

利用心电信号的相关性，对ECG进行预处理，再进行差值运算，将得到的差值序列进行DCT变换，从而实现心电数据的压缩。