Advancement of Statistical Theory and Methods for Survival Analysis, Longitudinal Data Analysis, and Missing Data Problems in United Kingdom
DOI:
https://doi.org/10.47604/jsar.2305Keywords:
Statistical Theory, Survival Analysis, Longitudinal Data Analysis, Missing Data ProblemsAbstract
Purpose: The aim of the study was to investigate advancement of statistical theory and methods for survival analysis, longitudinal data analysis, and missing data problems
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: In the United Kingdom, statistical advancements in survival analysis, longitudinal data analysis, and missing data solutions have improved predictions, enhanced the understanding of individual-level changes over time, and increased research reliability, particularly in healthcare and epidemiology. These developments have global significance, benefiting evidence-based decision-making and policy formulation.
Unique Contribution to Theory, Practice and Policy: Cox proportional-hazards model, mixed-effects models, multiple imputation may be used to anchor future studies on advancement of statistical theory and methods for survival analysis, longitudinal data analysis, and missing data problems. Create user-friendly software tools and packages for implementing advanced survival models, making them accessible to researchers and practitioners. By identifying trends and disparities over time, policymakers can design interventions and allocate resources more effectively.
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