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Rabideau DJ, Li F, Wang R. Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes. Stat Med 2024; 43:1458-1474. [PMID: 38488532 DOI: 10.1002/sim.10027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 03/19/2024]
Abstract
Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate-conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV-prevention measures can reduce HIV incidence.
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Affiliation(s)
- Dustin J Rabideau
- Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Li
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, Connecticut, USA
| | - Rui Wang
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
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2
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Shen B, Ren H, Shardell M, Falvey J, Chen C. Analyzing risk factors for post-acute recovery in older adults with Alzheimer's disease and related dementia: A new semi-parametric model for large-scale medicare claims. Stat Med 2024; 43:1003-1018. [PMID: 38149345 PMCID: PMC10922471 DOI: 10.1002/sim.9982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 10/11/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023]
Abstract
Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall-related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer's disease and related dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post-operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual-level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital-level unmeasured confounders, informative hospital size, and loss to follow-up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large-scale study. We further develop its asymptotic properties with stabilized inference assisted by unsupervised clustering. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators.
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Affiliation(s)
- Biyi Shen
- Regeneron Pharmaceuticals, New Jersey, U.S.A
| | - Haoyu Ren
- Department of Mathematics and Statistics, University of Maryland, Baltimore County, Maryland, U.S.A
| | - Michelle Shardell
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Maryland, U.S.A
| | - Jason Falvey
- Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Maryland, U.S.A
| | - Chixiang Chen
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Maryland, U.S.A
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3
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Mukherjee K, Gunsoy NB, Kristy RM, Cappelleri JC, Roydhouse J, Stephenson JJ, Vanness DJ, Ramachandran S, Onwudiwe NC, Pentakota SR, Karcher H, Di Tanna GL. Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations. PHARMACOECONOMICS 2023; 41:1589-1601. [PMID: 37490207 PMCID: PMC10635950 DOI: 10.1007/s40273-023-01297-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/15/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Missing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research. OBJECTIVES We aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes. METHODS A systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data. RESULTS From 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data. CONCLUSIONS Several health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.
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Affiliation(s)
- Kumar Mukherjee
- Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA
| | | | | | | | - Jessica Roydhouse
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | | | | | | | - Nneka C Onwudiwe
- Pharmaceutical Economics Consultants of America, Silver Spring, MD, USA
| | | | | | - Gian Luca Di Tanna
- Department of Business Economics, Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Stabile Piazzetta, Via Violino 11, 6928, Manno, Switzerland.
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Cai J, Zhang N, Zhou X, Spiegelman D, Wang M. Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes. Biometrics 2023; 79:3739-3751. [PMID: 37222518 PMCID: PMC11214728 DOI: 10.1111/biom.13877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/28/2023] [Indexed: 05/25/2023]
Abstract
Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long-term exposure toPM 2.5 $\text{PM}_{2.5}$ , in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2-year decline in the standard measure of cognition was 0.018 (95% CI, -0.034 to -0.001) units worse per 10μ g/m 3 $\mu \text{g/m}^3$ increase inPM 2.5 $\text{PM}_{2.5}$ exposure. After correction, the estimated impact ofPM 2.5 $\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, -0.059 to 0.005) units lower per 10μ g/m 3 $\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, -0.047 to -0.040) units per 1 year older after applying our correction method.
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Affiliation(s)
- Jiachen Cai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Ning Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Xin Zhou
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Donna Spiegelman
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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Chen C, Wang M, Chen S. An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis. Biometrics 2023; 79:2947-2960. [PMID: 36960726 DOI: 10.1111/biom.13858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/06/2023] [Indexed: 03/25/2023]
Abstract
Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called multiple information borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Shuo Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
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Chen C, Yu T, Shen B, Wang M. Synthesizing secondary data into survival analysis to improve estimation efficiency. Biom J 2023; 65:e2100326. [PMID: 36192158 DOI: 10.1002/bimj.202100326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 07/19/2022] [Accepted: 08/14/2022] [Indexed: 11/09/2022]
Abstract
The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Tonghui Yu
- School of Mathematics, Hefei University of Technology, Anhui, China
| | - Biyi Shen
- Bristol Myers Squibb, Lawrence Township, New Jersey, U.S.A
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve university, Cleveland, Ohio, USA
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Malatesta S, Weir IR, Weber SE, Bouton TC, Carney T, Theron D, Myers B, Horsburgh CR, Warren RM, Jacobson KR, White LF. Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation. BMC Med Res Methodol 2022; 22:297. [PMID: 36402979 PMCID: PMC9675206 DOI: 10.1186/s12874-022-01782-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/02/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The occurrence and timing of mycobacterial culture conversion is used as a proxy for tuberculosis treatment response. When researchers serially sample sputum during tuberculosis studies, contamination or missed visits leads to missing data points. Traditionally, this is managed by ignoring missing data or simple carry-forward techniques. Statistically advanced multiple imputation methods potentially decrease bias and retain sample size and statistical power. METHODS We analyzed data from 261 participants who provided weekly sputa for the first 12 weeks of tuberculosis treatment. We compared methods for handling missing data points in a longitudinal study with a time-to-event outcome. Our primary outcome was time to culture conversion, defined as two consecutive weeks with no Mycobacterium tuberculosis growth. Methods used to address missing data included: 1) available case analysis, 2) last observation carried forward, and 3) multiple imputation by fully conditional specification. For each method, we calculated the proportion culture converted and used survival analysis to estimate Kaplan-Meier curves, hazard ratios, and restricted mean survival times. We compared methods based on point estimates, confidence intervals, and conclusions to specific research questions. RESULTS The three missing data methods lead to differences in the number of participants achieving conversion; 78 (32.8%) participants converted with available case analysis, 154 (64.7%) converted with last observation carried forward, and 184 (77.1%) converted with multiple imputation. Multiple imputation resulted in smaller point estimates than simple approaches with narrower confidence intervals. The adjusted hazard ratio for smear negative participants was 3.4 (95% CI 2.3, 5.1) using multiple imputation compared to 5.2 (95% CI 3.1, 8.7) using last observation carried forward and 5.0 (95% CI 2.4, 10.6) using available case analysis. CONCLUSION We showed that accounting for missing sputum data through multiple imputation, a statistically valid approach under certain conditions, can lead to different conclusions than naïve methods. Careful consideration for how to handle missing data must be taken and be pre-specified prior to analysis. We used data from a TB study to demonstrate these concepts, however, the methods we described are broadly applicable to longitudinal missing data. We provide valuable statistical guidance and code for researchers to appropriately handle missing data in longitudinal studies.
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Affiliation(s)
- Samantha Malatesta
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, 3rd Floor, Boston, MA, 02119, USA.
| | - Isabelle R Weir
- Center for Biostatistics in AIDS Research in the Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sarah E Weber
- Section of Infectious Diseases, Boston Medical Center, Boston, MA, USA
| | - Tara C Bouton
- Section of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Tara Carney
- Alcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, Tygerberg, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, South Africa
| | | | - Bronwyn Myers
- Alcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, Tygerberg, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Groote Schuur Hospital, Observatory, Cape Town, South Africa
- Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - C Robert Horsburgh
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, 3rd Floor, Boston, MA, 02119, USA
- Section of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Departments of Epidemiology and Global Health, Boston University School of Public Health, Boston, MA, USA
| | - Robin M Warren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research and South African Medical Research Council Centre for Tuberculosis Research, Cape Town, South Africa
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Karen R Jacobson
- Section of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Laura F White
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, 3rd Floor, Boston, MA, 02119, USA
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Missing data were poorly reported and handled in randomized controlled trials with repeatedly measured continuous outcomes: a cross-sectional survey. J Clin Epidemiol 2022; 148:27-38. [DOI: 10.1016/j.jclinepi.2022.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/15/2022] [Accepted: 04/13/2022] [Indexed: 11/18/2022]
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Chen C, Han P, He F. Improving main analysis by borrowing information from auxiliary data. Stat Med 2021; 41:567-579. [PMID: 34796519 DOI: 10.1002/sim.9252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 07/22/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Peisong Han
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Fan He
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA
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