STRengthening analytical thinking for observational studies: The STRATOS initiative Academic Article uri icon

Overview

MeSH Major

  • Biostatistics
  • Epidemiology
  • Observational Studies as Topic
  • Research Design
  • Statistics as Topic

abstract

  • The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even 'standard' analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests. In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved.

authors

publication date

  • December 30, 2014

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4320765

Digital Object Identifier (DOI)

  • 10.1002/sim.6265

PubMed ID

  • 25074480

Additional Document Info

start page

  • 5413

end page

  • 32

volume

  • 33

number

  • 30