Matching with time-dependent treatments: A review and look forward.
Laine E ThomasSiyun YangDaniel WojdylaDouglas E SchaubelPublished in: Statistics in medicine (2020)
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.
Keyphrases
- big data
- cross sectional
- end stage renal disease
- artificial intelligence
- phase iii
- newly diagnosed
- machine learning
- study protocol
- ejection fraction
- chronic kidney disease
- cardiovascular disease
- clinical trial
- prognostic factors
- peritoneal dialysis
- double blind
- randomized controlled trial
- high fat diet
- type diabetes
- electronic health record
- patient reported outcomes
- current status
- deep learning
- insulin resistance
- smoking cessation