This page first discusses Excel, then statistics in general near the bottom of the page.
Help with Excel
This is a simple introduction to some Excel commands which are helpful with the large spreadsheets of medical data on the site. The commands here work in Excel 2010 and Excel 2003.
When you open an excel spreadsheet from the web, it may ask if you want to "enable editing." You'll need to say Yes to make any changes in your copy, or to "find all" entries of a certain type, or filter, or sort.
If the spreadsheet was slow to download, click File/Save As a couple times with new names, so you can get back to the original version without downloading again.
If you need extra help, many people who work in bookkeeping or finance are good with Excel spreadsheets, or you can search the web.
Basics. Most of you already know these:
Undo a Sort or anything: press Ctrl and Z at the same time. You can do this repeatedly to back up to previous versions, sometimes as far as the last time you saved the file
Save Frequently, with new names, so you can go back to previous versions if you need to.
Calculate an Average or Sum: Suppose you want Average of Column T
Help with Statistics
For subscribers, AMA has advice for using statistics with large data files, a checklist, a series discussing medical databases, and an article comparing "odds ratios," probabilities, and "relative risk ratios" (emphasis added):
There are excellent articles on statistical analysis of health data in the British Medical Journal, though it requires a subscription, which you may find at a university or hospital library.
When people compare hospitals and doctors, and "control for the differences among patients," the risk adjustment is pitifully poor.
Since risk adjustment is ineffective, hospitals can improve their results by denying care to the patients with the worst conditions (“We can’t help you…”), giving the hospital a better “success” rate. Attention to outcome measures leads to denial of care to the sickest.
As a very professional and problematic example, Medicare’s adjustment of health condition (HCC) is poor. It explains only 2% to 12% of the total variation actually caused by patient mix (p.65 table 3-22, “r-squared” of version 21).
Medicare’s adjustment of patient mix for readmission penalties is also poor. For example, their equations explain 3% of the variation in readmissions among heart failures (p.30), 5% for heart attacks (p.30) and pneumonia (p.29). These percents date from 2008 and have not been updated.
Medicare now shows c-statistics between 0.61 and 0.66 for readmission penalties,
The c-statistic has a scale of 0.5 to 1, where 0.5 means their equations do no better than chance, and 1 means their equations are perfect. So some equations are little better than chance, and they still rate hospitals with them. “Models are typically considered reasonable when the C-statistic is higher than 0.7 and strong when C exceeds 0.8”
so none of their equations is “reasonable,” and they still charge hospitals hundreds of millions of dollars of penalties with them each year, driving hospitals to reduce admissions among the sickest.
The Society of Thoracic Surgeons (STS) has its own risk adjustment.
They give “c-statistics” ranging from 0.616 to 0.826, so some of their equations are not "reasonable," and are little better than chance, but they use them to compare hospitals.
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hospitals or anyone. It
mostly from Medicare, so
you can decide.
Dates are assigned
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