• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

与纵向干预依从性相关的基线患者特征和死亡率。

Baseline patient characteristics and mortality associated with longitudinal intervention compliance.

作者信息

Lin Julia Y, Ten Have Thomas R, Bogner Hillary R, Elliott Michael R

机构信息

Center for Multicultural Mental Health Research, Somerville, MA 02143, USA.

出版信息

Stat Med. 2007 Dec 10;26(28):5100-15. doi: 10.1002/sim.2909.

DOI:10.1002/sim.2909
PMID:17477334
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2810145/
Abstract

Lin et al. (http://www.biostatsresearch.com/upennbiostat/papers/, 2006) proposed a nested Markov compliance class model in the Imbens and Rubin compliance class model framework to account for time-varying subject noncompliance in longitudinal randomized intervention studies. We use superclasses, or latent compliance class principal strata, to describe longitudinal compliance patterns, and time-varying compliance classes are assumed to depend on the history of compliance. In this paper, we search for good subject-level baseline predictors of these superclasses and also examine the relationship between these superclasses and all-cause mortality. Since the superclasses are completely latent in all subjects, we utilize multiple imputation techniques to draw inferences. We apply this approach to a randomized intervention study for elderly primary care patients with depression.

摘要

林等人(http://www.biostatsresearch.com/upennbiostat/papers/,2006年)在因本斯和鲁宾依从性类别模型框架内提出了一种嵌套马尔可夫依从性类别模型,以解释纵向随机干预研究中随时间变化的受试者不依从情况。我们使用超类,即潜在依从性类别主层,来描述纵向依从模式,并且假定随时间变化的依从性类别取决于依从历史。在本文中,我们寻找这些超类良好的受试者水平基线预测因子,并研究这些超类与全因死亡率之间的关系。由于超类在所有受试者中完全是潜在的,我们利用多重填补技术进行推断。我们将此方法应用于一项针对老年抑郁症初级护理患者的随机干预研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2b9/2810145/96e8b2c20597/nihms166102f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2b9/2810145/f6617f0692d3/nihms166102f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2b9/2810145/96e8b2c20597/nihms166102f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2b9/2810145/f6617f0692d3/nihms166102f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2b9/2810145/96e8b2c20597/nihms166102f2.jpg

相似文献

1
Baseline patient characteristics and mortality associated with longitudinal intervention compliance.与纵向干预依从性相关的基线患者特征和死亡率。
Stat Med. 2007 Dec 10;26(28):5100-15. doi: 10.1002/sim.2909.
2
Nested Markov compliance class model in the presence of time-varying noncompliance.存在随时间变化的不依从性时的嵌套马尔可夫依从性类别模型
Biometrics. 2009 Jun;65(2):505-13. doi: 10.1111/j.1541-0420.2008.01113.x.
3
Joint modeling compliance and outcome for causal analysis in longitudinal studies.纵向研究中用于因果分析的联合建模依从性与结果
Stat Med. 2014 Sep 10;33(20):3453-65. doi: 10.1002/sim.5811. Epub 2013 Apr 9.
4
Α Markov model for longitudinal studies with incomplete dichotomous outcomes.用于具有不完全二分结果的纵向研究的马尔可夫模型。
Pharm Stat. 2017 Mar;16(2):122-132. doi: 10.1002/pst.1794. Epub 2016 Dec 5.
5
Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.有非随机缺失时纵向二分类和有序结局敏感性分析的受控模式插补。
Stat Med. 2018 Apr 30;37(9):1467-1481. doi: 10.1002/sim.7583. Epub 2018 Jan 15.
6
A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout.具有信息性缺失的纵向数据的共享参数连续时间隐马尔可夫和生存模型。
Stat Med. 2019 Mar 15;38(6):1056-1073. doi: 10.1002/sim.7994. Epub 2018 Oct 15.
7
Comprehensive implementations of multiple imputation using retrieved dropouts for continuous endpoints.使用检索到的失访数据对连续终点进行多重填补的综合实施方法。
BMC Med Res Methodol. 2025 Feb 21;25(1):47. doi: 10.1186/s12874-025-02494-5.
8
Finite Mixtures of Hidden Markov Models for Longitudinal Responses Subject to Drop out.用于存在缺失数据的纵向响应的隐马尔可夫模型的有限混合
Multivariate Behav Res. 2020 Sep-Oct;55(5):647-663. doi: 10.1080/00273171.2019.1660606. Epub 2019 Sep 27.
9
Pattern mixture models and latent class models for the analysis of multivariate longitudinal data with informative dropouts.用于分析具有信息性缺失的多变量纵向数据的模式混合模型和潜在类别模型。
Int J Biostat. 2008;4(1):Article 14. doi: 10.2202/1557-4679.1088.
10
Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values.用于处理具有不可忽略缺失值的临床试验纵向数据的基于插补的策略。
Stat Med. 2008 Jul 10;27(15):2826-49. doi: 10.1002/sim.3111.

引用本文的文献

1
Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Methodological Papers.统计方法在调整时间事件结局和卫生技术评估中患者不依从性对治疗效果估计的应用:方法学论文的系统评价。
Med Decis Making. 2019 Nov;39(8):910-925. doi: 10.1177/0272989X19881654. Epub 2019 Oct 24.
2
A causal model for longitudinal randomised trials with time-dependent non-compliance.具有时间依赖性不依从性的纵向随机试验的因果模型。
Stat Med. 2015 May 30;34(12):2019-34. doi: 10.1002/sim.6468. Epub 2015 Mar 16.
3
Neighborhood social environment and patterns of adherence to oral hypoglycemic agents among patients with type 2 diabetes mellitus.2型糖尿病患者的邻里社会环境与口服降糖药依从模式
Fam Community Health. 2015 Apr-Jun;38(2):169-79. doi: 10.1097/FCH.0000000000000069.
4
A brief adherence intervention that improved glycemic control: mediation by patterns of adherence.一种改善血糖控制的简短依从性干预措施:通过依从模式进行介导。
J Behav Med. 2015 Feb;38(1):39-47. doi: 10.1007/s10865-014-9576-3. Epub 2014 Jun 10.
5
Collaborative care for depression and anxiety problems.抑郁症和焦虑症的协作护理。
Cochrane Database Syst Rev. 2012 Oct 17;10(10):CD006525. doi: 10.1002/14651858.CD006525.pub2.

本文引用的文献

1
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.随机松弛,吉布斯分布,以及贝叶斯图像恢复。
IEEE Trans Pattern Anal Mach Intell. 1984 Jun;6(6):721-41. doi: 10.1109/tpami.1984.4767596.
2
Nested Markov compliance class model in the presence of time-varying noncompliance.存在随时间变化的不依从性时的嵌套马尔可夫依从性类别模型
Biometrics. 2009 Jun;65(2):505-13. doi: 10.1111/j.1541-0420.2008.01113.x.
3
Patterns of early adherence to the antidepressant citalopram among older primary care patients: the prospect study.老年初级保健患者中早期服用抗抑郁药西酞普兰的模式:前瞻性研究。
Int J Psychiatry Med. 2006;36(1):103-19. doi: 10.2190/DJH3-Y4R0-R3KG-JYCC.
4
A meta-analysis of the association between adherence to drug therapy and mortality.药物治疗依从性与死亡率之间关联的荟萃分析。
BMJ. 2006 Jul 1;333(7557):15. doi: 10.1136/bmj.38875.675486.55. Epub 2006 Jun 21.
5
Depression, cardiovascular disease, diabetes, and two-year mortality among older, primary-care patients.老年初级保健患者中的抑郁症、心血管疾病、糖尿病及两年死亡率
Am J Geriatr Psychiatry. 2005 Sep;13(9):748-55. doi: 10.1176/appi.ajgp.13.9.748.
6
An extended general location model for causal inferences from data subject to noncompliance and missing values.一种用于对存在不依从性和缺失值的数据进行因果推断的扩展一般位置模型。
Biometrics. 2004 Sep;60(3):598-607. doi: 10.1111/j.0006-341X.2004.00208.x.
7
Reducing suicidal ideation and depressive symptoms in depressed older primary care patients: a randomized controlled trial.降低老年初级保健抑郁症患者的自杀意念和抑郁症状:一项随机对照试验。
JAMA. 2004 Mar 3;291(9):1081-91. doi: 10.1001/jama.291.9.1081.
8
Special intervention reduces CVD mortality for adherent participants in the multiple risk factor intervention trial.在多危险因素干预试验中,特殊干预措施可降低依从性参与者的心血管疾病死亡率。
Ann Behav Med. 2003 Aug;26(1):61-8. doi: 10.1207/S15324796ABM2601_08.
9
Predictors of noncompliance in patients with schizophrenia.精神分裂症患者不依从性的预测因素
J Clin Psychiatry. 2002 Dec;63(12):1121-8. doi: 10.4088/jcp.v63n1206.
10
Depression as a risk factor for non-suicide mortality in the elderly.抑郁症作为老年人非自杀性死亡的一个风险因素。
Biol Psychiatry. 2002 Aug 1;52(3):205-25. doi: 10.1016/s0006-3223(02)01423-3.