# The Petty/Bone RCT

**URL:** <https://discourse.datamethods.org/t/the-petty-bone-rct/22077>\
**Category:** data analysis\
**Tags:** rct\
**Created:** [October 25, 2024, 12:23am UTC](https://discourse.datamethods.org/t/the-petty-bone-rct/22077 "2024-10-25T00:23:42Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [October 25, 2024, 2:34pm UTC](https://discourse.datamethods.org/t/the-petty-bone-rct/22077/3 "2024-10-25T14:34:53Z")

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> [@llynn](#):
>
> **Petty’s idea of lumped set of different diseases by a set of consensus thresholds of nonspecific lab and vitals values.** The consensus group thereby established a method of generating a single disease equivalent for RCT which they called a “heterogenous syndrome”. RCT testing a treatment for these syndromes were the most common embodiments of this novel Petty/Bone RCT.

The following is just one of many papers that condemns the use of dichotomization of continuous variables. Nothing good can come from it, as it loses large amounts of information in the best case, to ability to manipulate results at worst.

> **[Why Quantitative Variables Should Not Be Recoded as Categorical](https://www.scirp.org/journal/paperinformation?paperid=93794)**
>
> Discover the drawbacks of recoding quantitative variables into categories. Learn why this method can lead to biased estimates and inefficiency in data analysis. Stay informed and avoid this poor methodological strategy.

> Dichotomization leads us to overlook the true nature of the relationship between X and Y. According to [[6](https://www.scirp.org/journal/paperinformation?paperid=93794#ref6)] , “simply dichotomizing continuous variables without previously referring to the original distributions by plotting them and checking consequences of dichotomization is a bad idea and should be discouraged” (p. 3). These two examples show how dichotomization can lead scholars to wrong inferences.

🆕 The Data Methods thread on [Categoizing Continuous Variables](https://discourse.datamethods.org/t/categorizing-continuous-variables/3402) is also worth study.

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