Dummy Tables Essay Sample

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  • Pages: 2
  • Word count: 374
  • Rewriting Possibility: 99% (excellent)
  • Category: reality

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Introduction of TOPIC

“Dummy Tables” are not labelled “dummy” in the way that Books for Dummies are. They are NOT tables for dummies! They are tables that are virtual, not real. And that term has nothing to do with the Internet and Virtual Reality. Statisticians have always used “dummy tables” to help them visualize their data in relationship to their theory, their data gathering methods, and the hypotheses they hope to support. “Dummy tables” are real world ways to help you visualize. You should never undertake a research project without exploring “dummy tables” on the project. This the statistician’s way of planning out his/her work.

It is important to consider what information you will need to collect before finalising the data collection tools, and generation of Dummy tables will inform this process. Dummy tables outline how the results of the trial will be reported. In practice, the structure of the ‘tables’ will often follow that suggested by the CONSORT statement:

• Flow diagram showing the flow of participants through each stage of the trial (enrolment, randomization, treatment, follow up, analysis)

• Table of baseline data by randomized group

• Tables of actual management received

• Table(s) of outcomes with effect sizes and 95% confidence intervals

• Table(s) of other analyses (for example, subgroup analyses)

• Table(s) of adverse events

The dummy tables should be appended to the end of the protocol and the form of any interim analyses or final analyses should adhere to them. Successful completion of dummy tables allows cross-checking against questionnaires to ensure they are collecting all the correct data. It also gets collaborators to agree on the important data items during the design stage, and it allows advance planning of interim and final analyses.ld adhere to them. Successful completion of dummy tables allows cross-checking against questionnaires to ensure they are collecting all the correct data. It also gets collaborators to agree on the important data items during the design stage, and it allows advance planning of interim and final analyses.

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