Biostatistics: The near future

Scott Zeger, Peter Diggle, Kung Yee Liang

Research output: Book/ReportBook

Abstract

This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.

Original languageEnglish (US)
PublisherOxford University Press
ISBN (Print)9780191718038, 9780198566540
DOIs
Publication statusPublished - Sep 1 2007

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Keywords

  • Bioinformatics
  • Causal inference
  • Conditional inference
  • Genomic data
  • Model complexity
  • Multi-level models
  • Nuisance parameters
  • Statistical computing
  • Statistical efficiency

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Zeger, S., Diggle, P., & Liang, K. Y. (2007). Biostatistics: The near future. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198566540.003.0009