#Tabout stata code
The table1_mc code will apply your bizarre, space-less variable name to the output unless you are using labels.
![tabout stata tabout stata](https://i1.wp.com/statadaily.com/wp-content/uploads/2010/10/outreg_sample_result1.jpg)
Pluck out the variables you’ll include as the exposure and outcome.
#Tabout stata install
Type: ssc install table1_mc Step 2: Label your variables Need to reformat for a new target journal? Make minor changes and hit re-run and - ”POOF”’ - out pops an updated and compliant Table 1. It automates the generation of a Table 1 with a few simple codes. It’s a derivation of the original table1 program by Phil Clayton. The Stata program table1_mc was released by Mark Chatfield, a biostatistician at the University of Queensland. Formulating one either requires manually running –sum– commands over and over again or writing custom code to help automate this for you. Well, Stata doesn’t natively pop out Table 1s. Why have Table 1s historically been such a pain in the butt to make in Stata? This creates challenges for authors, who may need to rework Table 1s in the submission (and resubmission) process.
![tabout stata tabout stata](https://i.stack.imgur.com/ae752.png)
The ultimate design of the Table 1 will be dictated by the target journal. They are only occasionally helpful in observational studies.
![tabout stata tabout stata](http://cshin.me/wp-content/uploads/2019/05/ect16oqi.jpg)
A row for the entire population – This always seems overkill to me.There are certain variations that you’ll see in Table 1s:
![tabout stata tabout stata](https://miro.medium.com/max/1400/1*n8UKkPKCgPj-eFTEvuec1Q.jpeg)
Wait, I’m looking at a Table 1 has more than just a column for each exposure! Here, the columns would be smoking and no smoking. Say you are curious about the relationship between smoking and development of breast cancer in a cohort. In observational studies, the column is your exposure of interest. In placebo-controlled RCTs, the columns are drug and placebo. The rows are characteristics of your population that are relevant to your research project. The columns represent the exposure you are studying. Baseline demographic tables (colloquially known as ‘Table 1’ given their common location) are a core feature of nearly all epidemiologic manuscripts.