When I met my second husband, the entire contents of his kitchen cupboards was a carton of Marlboro cigarettes, a bottle of Jack Daniels and a loaded Magnum revolver. Despite that, he was extremely healthy. His reasoning was that he was a big, tough guy while viruses and bacteria were little bitty things, so the nicotine and alcohol killed them before they could get around to making him sick.
In my continuing saga of mucking about with open data, I’ve been looking at the Kaiser Permanente data from the study of the oldest old. I’m interested in testing out the claims of a friend of mine who recently turned 65. He says that he may as well keep up drinking 8 or 9 beers a night because it’s too late to quit now. He’s already too old to die young.
The original data set had over 40% of the data missing on this variable but I noticed that people who said they did not drink were not asked this question. So, I added a couple of IF statements that set the number of drinks per day to zero for people who said they did not drink. This left me with four categories.
Little nifty note for if you are using SAS Enterprise Guide, and you want your data to be presented in the table according to the formatted values, rather than the unformatted values, look to the right of the task window for summary tables and you’ll see an option to sort by and in what order.
This was useful for me because the default is to sort by unformatted values and the unformatted values were
- 0 = never,
- 1 = 6 or more,
- 2 = 3-5 drinks per day and
- 3 = < 2 drinks per day.
The results can be seen in the table below. In fact, people who did not drink at all, at least in the past year, had the longest life span, at 85.4 years. People who drank two or fewer drinks per day had a life-expectancy of 84.8, those who had 3-5 alcoholic drinks per day lived an average of 81.9 years and those who drank six or more did have the lowest life span, a still pretty old 81.7 years.
It’s worth noting that almost 20% of respondents had no data for this question and they had the longest lifespan of all at 85.8 years. I don’t know what to make of that other than that possibly those who tell researchers it is none of their damned business how much they drink have less repressed anger and stress and consequently live longer.
Were these differences significant? My next analysis was to do an ANOVA with lifespan as the dependent variable and the four alcohol consumption groups as the independent. I dropped the people with missing data.
There was a significant difference (F = 19.55, p < .0001), however, keep in mind that this is a fairly large sample meaning that even relatively minor differences will be statistically significant. In fact, drinking only explained about 2% of the variance in lifespan, and that’s assuming that there aren’t any confounding factors like women drinking less and also having greater longevity.
Another nifty tip for SAS Enterprise Guide. If you would like to export a graph in a project, say, so that you could post it on your blog, all you need to do is right-click on that graph and select SAVE PICTURE AS
When we look at the box plot produced by the ANOVA, it’s evident that there is very little difference between the last two groups.
We knew that from the table of means above, anyway. Perhaps more interesting is that fact that a Tukey post hoc test showed all groups to be significantly different from one another EXCEPT for the last two groups.
In other words, for my friend who drinks 8-9 beers a night to see any increase in life expectancy he would have to drop his alcohol consumption to less than 25% of his current level. Simply cutting it in half would not really yield him a significant benefit.
Of course, I do suggest that from time to time and his response is:
Look, you’re not my mother. If I have any need of a woman telling me what to do, I already get it fulfilled when I visit my mother three times every week. She’s 88 years old.
I wonder how much she drinks?