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Matching with time-dependent treatments: A review and look forward.

ABSTRACT: Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.

SUBMITTER: Thomas LE

PROVIDER: S-EPMC7384144 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Matching with time-dependent treatments: A review and look forward.

Thomas Laine E LE Yang Siyun S Wojdyla Daniel D Schaubel Douglas E DE

Statistics in medicine 20200403 17

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of in ...[more]

PMID: 32242973

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Project description:BackgroundLongitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching.MethodsTo evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models.ResultsIn all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates.ConclusionsWhile the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Project description:IntroductionIn the last two decades, our understanding of the therapeutic utility and medicinal properties of cannabis has greatly changed. This change has been accompanied by widespread cannabis use in various communities and different age groups, especially within the United States. With this increase, we should consider the potential effects of cannabis-hemp on general public health and how they could alter therapeutic outcomes.Material and methodsThe present investigation examined cannabis use for recreational and therapeutic use and a review of pertinent indexed literature was performed. The focused question evaluates "how cannabis or hemp products impact health parameters and do they provide potential therapeutic value in dentistry, and how do they interact with conventional medicines (drugs)." Indexed databases (PubMed/Medline, EMBASE) were searched without any time restrictions but language was restricted to English.ResultsThe review highlights dental concerns of cannabis usage, the need to understand the endocannabinoid system (ECS), cannabinoid receptor system, its endogenous ligands, pharmacology, metabolism, current oral health, and medical dilemma to ascertain the detrimental or beneficial effects of using cannabis-hemp products. The pharmacological effects of pure cannabidiol (CBD) have been studied extensively while cannabis extracts can vary significantly and lack empirical studies. Several metabolic pathways are affected by cannabis use and could pose a potential drug interaction. The chronic use of cannabis is associated with health issues, but the therapeutic potential is multifold since there is a regulatory role of ECS in many pathologies.ConclusionCurrent shortcomings in understanding the benefits of cannabis or hemp products are limited due to pharmacological and clinical effects not being predictable, while marketed products vary greatly in phytocompounds warrant further empirical investigation. Given the healthcare challenges to manage acute and chronic pain, this review highlights both cannabis and CBD-hemp extracts to help identify the therapeutic application for patient populations suffering from anxiety, inflammation, and dental pain.

Project description:While South Africa has had a fairly consistent record of producing national-level strategic plans for human resources for health in the past 25 years, the country continues to face major problems of affordability, availability, distribution and management of its health workforce. There are several factors contributing to the state of health human resources in the country, but problems with governance stand out as one area requiring further research, analysis and critique. This paper presents a retrospective analysis of the historical patterns in national health human resources governance in South Africa, based on a desktop policy review spanning 25 years after democracy. The authors took a multi-pronged, iterative approach, reviewing policy documents alongside grey and published literature. This led to a timeline showing key legislation, relevant health system and human resource policies, interventions, reviews and evaluations from 1994 to 2018. The review identified three distinct periods that help to characterise the terrain of human resources for health governance over the concerned 25 years. Firstly, a foundational period, in which much of the constitutional and legislative groundwork was laid. Secondly, the HIV epidemic period, which presented a major disruption to the development of system wide governance interventions and improvements. Thirdly, the launch of National Health Insurance discussions and policy processes, which signalled a gradual return to a comprehensive systems focus in line with the founding principles of the first period. Using this periodisation, as well as a conceptual framework of health human resources governance functions based on international literature, the authors argue that South Africa has experienced both progress and challenges in human resources for health governance. This has affected the successful implementation of its policy and strategic planning over the past 25 years. Good governance for human resources for health requires capable stewardship, underpinned by an appropriate mix of technical and administrative skills and explicit political support. The review findings suggest that strengthening human resources for health governance roles, including fostering purposeful stewardship by the National Department of Health, may be key to shifting the terrain in the availability and performance of South Africa's health workforce going forwards.

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Matching with time-dependent treatments: A review and look forward.

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Matching with time-dependent treatments: A review and look forward.

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