ArticleBiometrics2019
Causal comparative effectiveness analysis of dynamic continuous-time treatment initiation rules with sparsely measured outcomes and death.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimating the causal effects of multiple intermittent treatments with application to COVID-19.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Health care costs of cardiovascular disease in China: a machine learning-based cross-sectional study.Frontiers in public health · 2023Article
- Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.International journal of environmental research and public health · 2022Review
- A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.International journal of environmental research and public health · 2022Article
- A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.The annals of applied statistics · 2022Article
- Determinants of Total End-of-Life Health Care Costs of Medicare Beneficiaries: A Quantile Regression Forests Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.BMC medical research methodology · 2022Article
- Immunotherapy Outcomes in Individuals With Non-Small Cell Lung Cancer and Poor Performance Status.JNCI cancer spectrum · 2022Article
- Estimating the optimal timing of surgery by imputing potential outcomes.Statistics in medicine · 2021Article
- Variable selection with missing data in both covariates and outcomes: Imputation and machine learning.Statistical methods in medical research · 2021Article
- Estimating heterogeneous survival treatment effects of lung cancer screening approaches: A causal machine learning analysis.Annals of epidemiology · 2021Article
- Estimating heterogeneous survival treatment effect in observational data using machine learning.Statistics in medicine · 2021Article
- Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence.Journal of urban health : bulletin of the New York Academy of Medicine · 2021Article
- A scoping review of studies using observational data to optimise dynamic treatment regimens.BMC medical research methodology · 2021Article
- Machine learning to identify and understand key factors for provider-patient discussions about smoking.Preventive medicine reports · 2020Article
- Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach.BMC health services research · 2020Article
- Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.Journal of the American Heart Association · 2020Article
- Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach.BMC public health · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Evidence supporting the current World Health Organization recommendations of early antiretroviral therapy (ART) initiation for adolescents is inconclusive. We leverage a large observational data and compare, in terms of mortality and CD4 cell count, the dynamic treatment initiation rules for human immunodeficiency virus-infected adolescents. Our approaches extend the marginal structural model for estimating outcome distributions under dynamic treatment regimes, developed in Robins et al. (2008), to allow the causal comparisons of both specific regimes and regimes along a continuum. Furthermore, we propose strategies to address three challenges posed by the complex data set: continuous-time measurement of the treatment initiation process; sparse measurement of longitudinal outcomes of interest, leading to incomplete data; and censoring due to dropout and death. We derive a weighting strategy for continuous-time treatment initiation, use imputation to deal with missingness caused by sparse measurements and dropout, and define a composite outcome that incorporates both death and CD4 count as a basis for comparing treatment regimes. Our analysis suggests that immediate ART initiation leads to lower mortality and higher median values of the composite outcome, relative to other initiation rules.
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