Statistics article analysis

The articles below are by the witty Dr. Andrew Vickers. Source: taken from Medscape.com Read the three short articles and answer/respond to the two points listed below. This assignment is worth a maximum of 6 points that will be added to either your midterm or final exam score, whichever gives you the highest percentage. The grading will be 0, 3 or 6 points—nothing in between. Your responses must be thoughtful, yet to the point. Your responses must be typed with standard Times New Roman, 12-point font. Your total response cannot exceed one page. 1. What is the main point that Dr. Vickers is trying to make? (3 points) 2. Pick one of the three articles and comment specifically on how the article relates to Dr. Vickers main point. (3 points) ----------------------------------------------------------------------- Shoot First and Ask Questions Later: How to Approach Statistics Like a Real Clinician Posted 07/26/2006, Andrew J. Vickers, PhD It was just before an early morning meeting, and I was really trying to get to the bagels, but I couldn't help overhearing a conversation between one of my statistical colleagues and a surgeon. Statistician: "Oh, so you have already calculated the P-value?" Surgeon: "Yes, I used multinomial logistic regression." Statistician: "Really? How did you come up with that?" Surgeon: "Well, I tried each analysis on the SPSS drop-down menus, and that was the one that gave the smallest p-value". That comment deserves top marks for honesty -- which is more than I can say for many of the presentations I saw at a recent conference. In a typical study, a clinical investigator loaded data on a group of cancer patients into SPSS (a basic software package used for statistical analysis) and ran what is known as a multivariable Cox proportional hazards model. The investigator then read down a list of variables and concluded that each either did (p < .05) or did not (p > .05) predict survival. Calculating a Cox model involves some very complicated mathematics and is impractical without a computer. But computers are exactly the problem. In my favorite picture of R.A. Fisher, one of the founders of modern statistics, he is seated at a desk operating a mechanical counting device. Conducting a complex statistical analysis on such a machine is extremely time-consuming: Anyone who, like Fisher, had to depend on mechanical calculators would have had to think extremely hard about the analysis he or she wanted to conduct before starting. 2 With modern computing, it is possible to conduct an analysis with a minimum of time or brain power -- you just select something from a drop-down menu. The inevitable result of this ease of calculation is a proliferation of analyses that have not been sufficiently thought through. Here is a simple example: In one of the studies presented at the conference, a surgeon had created a multivariable regression model to predict tumor recurrence in terms of stage, grade, a tumor biomarker, and obesity. The p-value for obesity was less than .05, and the presenter concluded that "obesity may have some effect on survival." This isn't particularly illuminating. How I would have approached the problem is as follows: The biomarker is measured in ng/mL, that is, a weight divided by a volume. Two patients with similar tumors, 1 obese and 1 nonobese, will have a similar weight of the biomarker, but it seems likely that the biomarker is distributed in a greater volume of body tissue in the obese patient. Accordingly, the biomarker value in ng/mL will be lower. So if our 2 patients have similar levels of the biomarker, it is reasonable to suppose that the obese patient has a larger tumor. Now, this is pretty much what the multivariable regression model asks: If 2 patients are identical in all other respects, what is the impact of obesity on outcome? To test whether the apparent increased risk of obesity merely results from "dilution" of the biomarker in a larger volume of body tissue, I would add what is known as an "interaction term." If the p-value for obesity was significant in a model including the interaction term, we could conclude that there is something about the biology or behavior of obese individuals that increases the risk of recurrence; if the interaction term was significant we would conclude that the implications of having a certain level of a biomarker level differ between obese and nonobese patients. My approach here was to think about biology, turn it into math, and then think how to apply the results of the math back to biology again. It is, of course, easier just to shove everything into SPSS and interpret the resulting p-values as "yes" and "no." And if this is how you want to approach statistics, you'll have plenty of company. But, please, keep it to yourself, and don't block the bagels. Michael Jordan Won't Accept the Null Hypothesis: Notes on Interpreting High P-values Posted 05/15/2006, Andrew J. Vickers, PhD Finally, after many hours of packing and loading, the bags are in the car, the children in their booster seats and the snack bag in easy reach; everyone is buckled in and my hand is on the ignition key. At which point my wife asks: "Where is the camera?" Being a statistician, I instantly convert this question into 2 hypotheses: "The camera is in the car" and "The camera is still in the house." Given that it is easier to pop back inside the house than to unload the car, I decide to test the second hypothesis. A few minutes later, I tell my wife that I have looked inside the house in all the places where we normally keep the camera and couldn't find it. We conclude that "it must be in the car somewhere" and head off on our road trip. There is something a little odd about this story: we 3 concluded one thing (that the camera was in the car) because we couldn't prove something else (the camera was in the house). But as it happens, this is exactly what we do in statistics. First, we establish what is known as the "null hypothesis": roughly speaking, we establish that nothing interesting is going on. We then run our analyses, obtain our p-value and, if p is less than .05 (statistical significance), we reject the null and conclude that we have an interesting phenomenon on our hands. Drug trials are a simple example: here, our null hypothesis is that drug and placebo are equivalent, so if p is less than .05, we say that the drug and placebo differ. What we do if p is greater than .05 is a little more complicated. The other day I shot baskets with Michael Jordan (remember that I am a statistician and never make things up). He shot 7 straight free throws; I hit 3 and missed 4 and then (being a statistician) rushed to the sideline, grabbed my laptop and calculated a p-value of .07 by Fisher's exact test. Now, you wouldn't take this p-value to suggest that there is no difference between my basketball skills and those of Michael Jordan, you'd say that our experiment hadn't proved a difference. Yet, a good number of researchers, physicians, and commentators come to exactly the opposite conclusion when interpreting the results of medical research. Take the recent Women's Health Initiative trial evaluating a low-fat diet for breast cancer. This study, published in the February 8, 2006 issue of JAMA, reported a ~10% reduction in breast cancer risk in women eating a diet low in fat compared with controls [1]. If this is indeed the true difference, low-fat diets could reduce the incidence of breast cancer by many tens of thousands of women each year, an astonishing health benefit for an inexpensive and nontoxic intervention. The p-value for the difference in cancer rates was .07, and, here is the key point: This was widely interpreted as indicating that low-fat diets don't work. For example, The New York Times editorial page trumpeted that "low fat diets flub a test" and claimed that the study provided "strong evidence that the war against all fats was mostly in vain." [2] However, failure to prove that a treatment is effective is not the same as proving it ineffective. This is what statisticians call "accepting the null hypothesis" and, unless you accept that a British-born statistician got game with Michael Jordan, it is something you'll want to avoid. My own view on the low-fat diet trial is that the results were very encouraging, but weren't quite good enough to prove a difference. A prediction: as years go by and more data come in, the difference between groups in the breast cancer trial will reach statistical significance. At this point, there will be a general outcry of "wait a minute, they just said it didn't work" with a consequent increase in cynicism about medical research and nihilism about diet. This is to say, The New York Times should stick to hoops. References 1. Prentice RL, Caan B, Chlebowski RT, et al. Low-fat dietary pattern and risk of invasive breast cancer: the Women's Health Initiative Randomized Controlled Dietary Modification Trial. JAMA. 2006; 295:629-642. 2. Low-fat diets flub a test. New York Times. February 9, 2006 4 To P or Not to P: Why Use a P-value, Anyway? Posted 03/02/2006, Andrew J. Vickers, PhD My nonstatistical colleagues seem to imagine that I spend my off-hours calculating the mean time it takes to grill a piece of fish, or an exact binomial confidence interval for the proportion of Saturdays that I get to lie in. In truth, of course, I spend my off-hours trying not to think of statistics at all. But let's indulge the fantasy of the 24/7 statistician. Going home each night, I have a choice between cycling down a busy road or winding through the beautiful backstreets of Brooklyn. Being statistically obsessed, I have recorded how long each route takes me on a number of occasions and have calculated means and standard deviations. Imagine that, one day, I had to get home as soon as possible for an appointment. To choose a route, I conduct a statistical analysis of my travel time data: it turns out that the travel time for the busy road is shorter, but the difference between routes is not statistically significant (p = 0.4). Nonetheless, it would still seem sensible to take what is likely to be the quicker route home, even though I haven't proved that it will get me there fastest. Now let's imagine that this incident got me fired up, and I spend 2 years randomly selecting a route home and recording times. When I finally analyze the data, I find strong evidence that going home via the busy road is faster (p = .0001), but not by much (it saves me 57.3 seconds on average). So I decide that, unless I am in a real rush, I'll wind along the backstreets, simply because it is a more pleasant journey and well worth the extra minute. We tend to think that p-values should determine our actions; in the case of a drug clinical trial, for example, we say: "p < .05: use the drug; p >.05: don't use the drug." Yet, the bicycle example shows the opposite: I chose the busy road when p was .4 but not when p was .0001. This suggests we need to think a little harder about what p-values are and how we should use them. The most important thing to remember about p-values is that they are used to test hypotheses. This sounds obvious, but it is all too easily forgotten. A good example is the widespread practice of citing p-values for baseline differences between groups in a randomized trial. The hypothesis being tested here is whether there are real differences between groups. Yet we know that groups were randomly selected, so any differences in characteristics such as age or sex must be due to chance alone. Science is often said to be about testing hypotheses, but in many cases this is not what we want to do at all. When I had to get home in a rush, I wasn't interested in proving which was the quickest way home, I just needed to work out what route was likely to get me to my appointment on time. Moreover, even when we do want to test hypotheses, our conclusion is a necessary but not sufficient guide to action. I eventually proved that using the busy road was quickest but decided to choose a different route on the basis of considerations -- pleasure and quality of life -- that formed no part of the hypothesis test. An even more difficult problem is when our p-value is > .05, that is, when we have failed to prove our (alternative) hypothesis. This is often interpreted as proof that our hypothesis is false. Such an interpretation is not only incorrect, but it can also be dangerous; I'll discuss this in a future column.

The articles below are by the witty Dr. Andrew Vickers. Source: taken from Medscape.com

Read the three short articles and answer/respond to the two points listed below. This assignment is worth a maximum of 6 points that will be added to either your midterm or final exam score, whichever gives you the highest percentage. The grading will be 0, 3 or 6 points—nothing in between. Your responses must be thoughtful, yet to the point. Your responses must be typed with standard Times New Roman, 12-point font. Your total response cannot exceed one page.

1. What is the main point that Dr. Vickers is trying to make? (3 points)

2. Pick one of the three articles and comment specifically on how the article relates to Dr. Vickers main point. (3 points)