1
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Pullenayegum EM, Birken C, Maguire J. Causal inference with longitudinal data subject to irregular assessment times. Stat Med 2023. [PMID: 37054723 DOI: 10.1002/sim.9727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 02/10/2023] [Accepted: 03/18/2023] [Indexed: 04/15/2023]
Abstract
Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse-weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse-weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse-weighting, and apply it to the Liang semi-parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2-9 years and enrolled in the TargetKids! study.
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Affiliation(s)
- Eleanor M Pullenayegum
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Catherine Birken
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Jonathon Maguire
- Department of Paediatrics, St Michael's Hospital, Toronto, Canada
- Departments of Paediatrics & Nutritional Sciences, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Canada
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2
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Lokku A, Birken CS, Maguire JL, Pullenayegum EM. Quantifying the extent of visit irregularity in longitudinal data. Int J Biostat 2022; 18:487-520. [PMID: 34392639 DOI: 10.1515/ijb-2020-0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 08/02/2021] [Indexed: 01/10/2023]
Abstract
The timings of visits in observational longitudinal data may depend on the study outcome, and this can result in bias if ignored. Assessing the extent of visit irregularity is important because it can help determine whether visits can be treated as repeated measures or as irregular data. We propose plotting the mean proportions of individuals with 0 visits per bin against the mean proportions of individuals with >1 visit per bin as bin width is varied and using the area under the curve (AUC) to assess the extent of irregularity. The AUC is a single score which can be used to quantify the extent of irregularity and assess how closely visits resemble repeated measures. Simulation results confirm that the AUC increases with increasing irregularity while being invariant to sample size and the number of scheduled measurement occasions. A demonstration of the AUC was performed on the TARGet Kids! study which enrolls healthy children aged 0-5 years with the aim of investigating the relationship between early life exposures and later health problems. The quality of statistical analyses can be improved by using the AUC as a guide to select the appropriate analytic outcome approach and minimize the potential for biased results.
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Affiliation(s)
- Armend Lokku
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Catherine S Birken
- Division of Pediatric Medicine and the Pediatric Outcomes Research Team (PORT), Hospital for Sick Children, Toronto, ON, Canada.,Sick Kids Research Institute, Toronto, ON, Canada.,Institute of Health Policy, Management, and Evaluation, Toronto, ON, Canada.,Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jonathon L Maguire
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Division of Pediatric Medicine and the Pediatric Outcomes Research Team (PORT), Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Applied Health Research Centre, Li Ka Shing Knowledge Institute, Toronto, ON, Canada.,Department of Pediatrics, Li Ka Shing Knowledge Institute, Toronto, ON, Canada
| | - Eleanor M Pullenayegum
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada
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3
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Weaver C, Xiao L, Lu W. Functional data analysis for longitudinal data with informative observation times. Biometrics 2022. [PMID: 35188270 DOI: 10.1111/biom.13646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 02/10/2022] [Indexed: 11/28/2022]
Abstract
In functional data analysis for longitudinal data, the observation process is typically assumed to be non-informative, which is often violated in real applications. Thus, methods that fail to account for the dependence between observation times and longitudinal outcomes may result in biased estimation. For longitudinal data with informative observation times, we find that under a general class of shared random effect models, a commonly used functional data method may lead to inconsistent model estimation while another functional data method results in consistent and even rate-optimal estimation. Indeed, we show that the mean function can be estimated appropriately via penalized splines and that the covariance function can be estimated appropriately via penalized tensor-product splines, both with specific choices of parameters. For the proposed method, theoretical results are provided, and simulation studies and a real data analysis are conducted to demonstrate its performance. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Caleb Weaver
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina, 27606, USA
| | - Luo Xiao
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina, 27606, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina, 27606, USA
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4
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Sun D, Zhao H, Sun J. Regression analysis of asynchronous longitudinal data with informative observation processes. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2020.107161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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5
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Pullenayegum EM, Birken C, Maguire J. Clustered longitudinal data subject to irregular observation. Stat Methods Med Res 2021; 30:1081-1100. [PMID: 33509042 DOI: 10.1177/0962280220986193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data collected longitudinally as part of usual health care is becoming increasingly available for research, and is often available across several centres. Because the frequency of follow-up is typically determined by the patient's health, the timing of measurements may be related to the outcome of interest. Failure to account for the informative nature of the observation process can result in biased inferences. While methods for accounting for the association between observation frequency and outcome are available, they do not currently account for clustering within centres. We formulate a semi-parametric joint model to include random effects for centres as well as subjects. We also show how inverse-intensity weighted GEEs can be adapted to account for clustering, comparing stratification, frailty models, and covariate adjustment to account for clustering in the observation process. The finite-sample performance of the proposed methods is evaluated through simulation and the methods illustrated using a study of the relationship between outdoor play and air quality in children aged 2-9 living in the Greater Toronto Area.
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Affiliation(s)
- Eleanor M Pullenayegum
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Catherine Birken
- Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.,Department of Paediatrics, University of Toronto, Toronto, ON, Canada.,Department of Paediatrics, St Michael's Hospital, Toronto, ON, Canada
| | - Jonathon Maguire
- Department of Paediatrics, University of Toronto, Toronto, ON, Canada.,Department of Paediatrics, St Michael's Hospital, Toronto, ON, Canada.,Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada.,Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
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6
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Lavery JA, Callahan MK, Panageas KS. Apples and Oranges? Considerations for EHR-Based Analyses Aggregating Data From Interventional Clinical Trials and Point-of-Care Encounters in Oncology. JCO Clin Cancer Inform 2021; 5:21-23. [PMID: 33411618 DOI: 10.1200/cci.20.00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Jessica A Lavery
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Margaret K Callahan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Katherine S Panageas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
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7
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Jiang H, Su W, Zhao X. Robust estimation for panel count data with informative observation times and censoring times. LIFETIME DATA ANALYSIS 2020; 26:65-84. [PMID: 30542803 DOI: 10.1007/s10985-018-09457-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 12/03/2018] [Indexed: 06/09/2023]
Abstract
We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.
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Affiliation(s)
- Hangjin Jiang
- Center for Data Science, ZheJiang University, Hangzhou, China.
- Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong.
| | - Wen Su
- Haitong International Securities Group, Kowloon, Hong Kong
| | - Xingqiu Zhao
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Mishra A, Buzkova P, Balkus JE, Brown ER. Accounting for Informative Sampling in Estimation of Associations between Sexually Transmitted Infections and Hormonal Contraceptive Methods. ACTA ACUST UNITED AC 2020; 12. [PMID: 34141052 DOI: 10.1515/scid-2019-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The relationship between hormonal contraceptive method use and sexually transmitted infections (STIs) is not well understood. Studies that implement routine screening for STIs among different contraceptive users, such as the ASPIRE HIV-1 prevention trial, can be useful for identifying potential risk factors of STIs. However, the complex nature of non-random data can lead to challenges in estimation of associations for potential risk factors. In particular, if screening for the disease is not random (i.e. it is driven by symptoms or other clinical indicators), estimates of association can suffer from bias, often referred to as informative sampling bias. Time-varying predictors and potential stratification variables can further contribute to difficulty in obtaining unbiased estimates. In this paper, we estimate the association between time-varying contraceptive use and STI acquisition, in the presence of informative sampling, by extending the work Buzkova (2010). We use a two-step procedure to jointly model the non-random screening process and sexually transmitted infection risk. In the first step, inverse intensity rate ratios (IIRR) weights are estimated. In the second step, a weighted proportional rate model is fit to estimate the IIRR weighted hazard ratio. We apply the method to evaluate the relationship between hormonal contraception and risk of sexually transmitted infections among women participating in a biomedical HIV-1 prevention trial. We compare our results using the proposed weighted method to those generated using conventional approaches that do not account for potential informative sampling bias or do not use the full potential of the data. Using the IIRR weighted approach we found DMPA users have a significantly decreased hazard of T. vaginalis acquisition compared to IUD users (HR: 0.44, 95% CI: (0.25, 0.83)), which is consistent with the literature. We did not find significant increased or decreased hazard of other STIs for hormonal contraceptive users compared to non-hormonal IUD users.
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Affiliation(s)
- Anu Mishra
- Department of Biostatistics, University of Washington
| | - Petra Buzkova
- Department of Biostatistics, University of Washington
| | - Jennifer E Balkus
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
- Department of Epidemiology, University of Washington
| | - Elizabeth R Brown
- Department of Biostatistics, University of Washington
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
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9
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Shen W, Liu S, Chen Y, Ning J. Regression analysis of longitudinal data with outcome-dependent sampling and informative censoring. Scand Stat Theory Appl 2019; 46:831-847. [PMID: 32066989 PMCID: PMC7025472 DOI: 10.1111/sjos.12373] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 11/03/2018] [Indexed: 11/28/2022]
Abstract
We consider regression analysis of longitudinal data in the presence of outcome-dependent observation times and informative censoring. Existing approaches commonly require correct specification of the joint distribution of the longitudinal measurements, observation time process and informative censoring time under the joint modeling framework, and can be computationally cumbersome due to the complex form of the likelihood function. In view of these issues, we propose a semi-parametric joint regression model and construct a composite likelihood function based on a conditional order statistics argument. As a major feature of our proposed methods, the aforementioned joint distribution is not required to be specified and the random effect in the proposed joint model is treated as a nuisance parameter. Consequently, the derived composite likelihood bypasses the need to integrate over the random effect and offers the advantage of easy computation. We show that the resulting estimators are consistent and asymptotically normal. We use simulation studies to evaluate the finite-sample performance of the proposed method, and apply it to a study of weight loss data that motivated our investigation.
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Affiliation(s)
- Weining Shen
- Department of Statistics, University of California, Irvine
| | - Suyu Liu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Yong Chen
- Department of Biostatistics and Epidemiology, The University of Pennsylvania
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
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10
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Shortreed SM, Cook AJ, Coley RY, Bobb JF, Nelson JC. Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health. Am J Epidemiol 2019; 188:851-861. [PMID: 30877288 DOI: 10.1093/aje/kwy292] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 12/20/2018] [Indexed: 12/14/2022] Open
Abstract
Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.
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Affiliation(s)
- Susan M Shortreed
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Andrea J Cook
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - R Yates Coley
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Jennifer F Bobb
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
| | - Jennifer C Nelson
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington
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11
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Qu L, Sun L, Song X. A Joint Modeling Approach for Longitudinal Data with Informative Observation Times and a Terminal Event. STATISTICS IN BIOSCIENCES 2018. [DOI: 10.1007/s12561-018-9221-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Su W, Jiang H. Semiparametric analysis of longitudinal data with informative observation times and censoring times. J Appl Stat 2017. [DOI: 10.1080/02664763.2017.1403574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Wen Su
- China Merchants Securities (HK) Co., Limited, Hong Kong
| | - Hangjin Jiang
- Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong
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13
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Farzanfar D, Abumuamar A, Kim J, Sirotich E, Wang Y, Pullenayegum E. Longitudinal studies that use data collected as part of usual care risk reporting biased results: a systematic review. BMC Med Res Methodol 2017; 17:133. [PMID: 28877680 PMCID: PMC5588621 DOI: 10.1186/s12874-017-0418-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 08/31/2017] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Longitudinal studies using data collected as part of usual care risk providing biased results if visit times are related to the outcome of interest. Statistical methods for mitigating this bias are available but rarely used. This lack of use could be attributed to a lack of need or to a lack of awareness of the issue. METHODS We performed a systematic review of longitudinal studies that used data collected as part of patients' usual care and were published in MEDLINE or EMBASE databases between January 2005 through May 13th 2015. We asked whether the extent of and reasons for variability in visit times were reported on, and in cases where there was a need to account for informativeness of visit times, whether an appropriate method was used. RESULTS Of 44 eligible articles, 57% (n = 25) reported on the total follow-up time, 7% (n = 3) on the gaps between visits, and 57% (n = 25) on the number of visits per patient; 78% (n = 34) reported on at least one of these. Two studies assessed predictors of visit times, and 86% of studies did not report enough information to assess whether there was a need to account for informative follow-up. Only one study used a method designed to account for informative visit times. CONCLUSIONS The low proportion of studies reporting on whether there were important predictors of visit times suggests that researchers are unaware of the potential for bias when data is collected as part of usual care and visit times are irregular. Guidance on the potential for bias and on the reporting of longitudinal studies subject to irregular follow-up is needed.
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Affiliation(s)
- Delaram Farzanfar
- University Health Network, University of Toronto, Toronto, M5T 2S8, Canada.
| | - Asmaa Abumuamar
- Institute of Medical Science, University of Toronto, City, ON, M5S 1A8, Canada
| | - Jayoon Kim
- Faculty of Arts & Science, University of Toronto, City, ON, M5S 3G3, Canada
| | - Emily Sirotich
- Faculty of Arts & Science, University of Toronto, City, ON, M5S 3G3, Canada
| | - Yue Wang
- Faculty of Arts & Science, University of Toronto, City, ON, M5S 3G3, Canada
| | - Eleanor Pullenayegum
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada.,Dalla Lana School of Public Health, University of Toronto, City, ON, M5T 3M7, Canada
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14
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Du T, Ding J, Sun L. Joint modeling and estimation for longitudinal data with informative observation and terminal event times. COMMUN STAT-THEOR M 2016. [DOI: 10.1080/03610926.2014.960589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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15
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Pullenayegum EM, Lim LSH. Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design. Stat Methods Med Res 2016; 25:2992-3014. [DOI: 10.1177/0962280214536537] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
When data are collected longitudinally, measurement times often vary among patients. This is of particular concern in clinic-based studies, for example retrospective chart reviews. Here, typically no two patients will share the same set of measurement times and moreover, it is likely that the timing of the measurements is associated with disease course; for example, patients may visit more often when unwell. While there are statistical methods that can help overcome the resulting bias, these make assumptions about the nature of the dependence between visit times and outcome processes, and the assumptions differ across methods. The purpose of this paper is to review the methods available with a particular focus on how the assumptions made line up with visit processes encountered in practice. Through this we show that no one method can handle all plausible visit scenarios and suggest that careful analysis of the visit process should inform the choice of analytic method for the outcomes. Moreover, there are some commonly encountered visit scenarios that are not handled well by any method, and we make recommendations with regard to study design that would minimize the chances of these problematic visit scenarios arising.
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Affiliation(s)
- Eleanor M Pullenayegum
- Child Health Evaluative Sciences, Hospital for Sick Children, Dalla Lana School of Public Health, University of Toronto
| | - Lily SH Lim
- Division of Rheumatology, Department of Paediatrics, Hospital for Sick Children, Toronto, Canada
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16
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Joint analysis of longitudinal data with additive mixed effect model for informative observation times. J Stat Plan Inference 2016. [DOI: 10.1016/j.jspi.2015.08.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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Pullenayegum EM. Multiple outputation for the analysis of longitudinal data subject to irregular observation. Stat Med 2015; 35:1800-18. [DOI: 10.1002/sim.6829] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 09/29/2015] [Accepted: 11/02/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Eleanor M. Pullenayegum
- Child Health Evaluative Sciences; Hospital for Sick Children; Toronto ON Canada
- Dalla Lana School of Public Health; University of Toronto; Toronto ON Canada
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18
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Liu KY, Zhao X. Robust estimation for longitudinal data with informative observation times. CAN J STAT 2015. [DOI: 10.1002/cjs.11269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Kin-yat Liu
- Department of Applied Mathematics; The Hong Kong Polytechnic University; Hung Hom Hong Kong
| | - Xingqiu Zhao
- Department of Applied Mathematics; The Hong Kong Polytechnic University; Hung Hom Hong Kong
- The Hong Kong Polytechnic University Shenzhen Research Institute; Shenzhen China
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