Competing risks and multivariate outcomes in epidemiological and clinical trial research.
Ross L PrenticePublished in: Lifetime data analysis (2024)
Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described.
Keyphrases
- data analysis
- clinical trial
- human health
- healthcare
- public health
- study protocol
- mental health
- air pollution
- randomized controlled trial
- type diabetes
- molecular dynamics
- polycystic ovary syndrome
- double blind
- metabolic syndrome
- machine learning
- big data
- adipose tissue
- electronic health record
- deep learning
- pregnant women
- phase iii
- glycemic control
- health promotion