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Creating summary tables using the sumtable command

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  • Lauren Scott

    (Clinical Trials and Evaluation Unit, Bristol)

  • Chris Rogers

    (Clinical Trials and Evaluation Unit, Bristol)

Abstract

In many fields of statistics, summary tables are used to describe characteristics within a study population. Moreover, such tables are often used to compare characteristics of two or more groups, for example, treatment groups in a clinical trial or different cohorts in an observational study. This talk introduces the sumtable command, a user-written command that can be used to produce such summary tables, allowing for different summary measures within one table. Summary measures available include means and standard deviations, medians and interquartile ranges, and numbers and percentages. The command removes any manual aspect of creating these tables (for example, copying and pasting from the Stata output window) and therefore eliminates transposition errors. It also makes creating a summary table quick and easy and is especially useful if data are updated and tables subsequently need to change. The end result is an Excel spreadsheet that can be easily manipulated for reports or other documents. Although this command was written in the context of medical statistics, it would be equally useful in many other settings.

Suggested Citation

  • Lauren Scott & Chris Rogers, 2016. "Creating summary tables using the sumtable command," United Kingdom Stata Users' Group Meetings 2016 05, Stata Users Group.
  • Handle: RePEc:boc:usug16:05
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    References listed on IDEAS

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    1. Sally R. Hinchliffe & David A. Scott & Paul C. Lambert, 2013. "Flexible parametric illness-death models," Stata Journal, StataCorp LP, vol. 13(4), pages 759-775, December.
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. Asaria, Miqdad & Walker, Simon & Palmer, Stephen & Gale, Chris P & Shah, Anoop D & Abrams, Keith R & Crowther, Michael & Manca, Andrea & Timmis, Adam & Hemingway, Harry & Sculpher, Mark, 2016. "Using electronic health records to predict costs and outcomes in stable coronary artery disease," LSE Research Online Documents on Economics 101257, London School of Economics and Political Science, LSE Library.
    4. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
    5. Andrew C. Titman, 2011. "Flexible Nonhomogeneous Markov Models for Panel Observed Data," Biometrics, The International Biometric Society, vol. 67(3), pages 780-787, September.
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