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Monthly Mean newsletter, November 2009 (released 2009-12-10)
Welcome to the Monthly Mean newsletter, the newsletter that dares to call itself average. This newsletter was sent out on December 10, 2009. If you are having trouble reading this newsletter in your email system, please go to www.pmean.com/news/2009-11.html. If you are not yet subscribed to this newsletter, you can sign on at www.pmean.com/news. If you no longer wish to receive this newsletter, there is a link to unsubscribe at the bottom of this email. Here's a list of topics.
- Risk adjustment using the case mix index
- Power for a three arm study
- The fourth deadly sin of researchers: lust
- Monthly Mean Article (peer reviewed): Extent of publication bias in different categories of research cohorts: a meta-analysis of empirical studies
- Monthly Mean Article (popular press): Lack of Study Volunteers Hobbles Cancer Fight
- Monthly Mean Book: Where's the Evidence: Debates in Modern Medicine
- Monthly Mean Definition: What is a Kaplan-Meier survival curve?
- Monthly Mean Quote: A few years ago, while doing a research project...
- Monthly Mean Website: The Median Isn't the Message
- Nick News: Nicholas the iceskater
- Very bad joke: There are three types of statisticians...
- Tell me what you think.
- Upcoming statistics webinars
- Why do I do all this for free?
1. Risk adjustment using the case mix index.
In previous newsletters, I have highlighted strategies for risk adjustment:* Analysis of covariance: www.pmean.com/news/2009-05.hml#1, and
* Reweighting: www.pmean.com/news/2009-07.html#2.
Another common strategy for risk adjustment is using the case mix index. Case-mix adjustment uses information at the patient level only to define discrete groups with (hopefully) a high degree of homogeneity within the groups and a high degree of heterogeneity between the groups.
A case-mix index (CMI) is a single number that represents the tendency for a particular group to include patients in groups that have a higher risk (CMI > 1) or lower risk (CMI < 1) of experiencing the large values of the outcome measure.
First, you divide the data into relatively homogenous subgroups. Then you compute the relative weight for each subgroup, which is the average outcome measure for all observations in that subgroup divided by the average outcome measure for all observations across all subgroups. The case mix index is then the sum of the weights times the size of the subgroups within a comparison unit.
I will use the same data set used in previous examples. This data set lists average salaries for 1,184 colleges and universities in the fifty states plus the District of Columbia. The colleges and universities are also categorized in Divisions I, IIA, and IIB. Greater prestige and larger salaries are associated with Division I, more moderate prestige and salaries in Division IIA, and the lowest prestige and salaries in Division IIB. If one state pays lower salaries than another, it may be partly due to the different mix of I, IIA, IIB in that state compared to the rest of the United States.
The average salary across all 1,184 colleges and universities is 43 thousand dollars. The average salaries in I, IIA, and IIB is 55, 45, and 38 thousand dollars respectively. The case mix indexes are:
55 / 43 = 1.28 (I),
45 / 43 = 1.05 (IIA), and
38 / 43 = 0.88 (IIB).
You might think of this along the lines of the famous quote in �Animal Farm� by George Orwell (All animals are equal but some animals are more equal than others). A Type I school is �worth� more (about 28% more) than an average school because that�s how much larger the salaries are (on average). A Type IIB school is worth less (about 12% less) because its salaries are lower (on average).
West Virginia (WV) has lower average salaries, but also a far larger number of IIB colleges and universities. The CMI for WV is
1.28*0.06 = 0.08
1.05*0.06 = 0.06
0.88*0.88 = 0.77
This value is less than one, which indicates that WV has a case mix that tends to produce artificially low salaries. Divide the unadjusted average salary in WV by the CMI to get an adjusted average salary for WV
35 / 0.91 = 38.5.
The salary for WV still lags behind the overall average, but not by as much as the raw data might indicate. Dividing by 0.91 adjusted the estimate upward to compensation for the larger proportion of IIB schools.
The CMI for DC is
1.28*0.56 = 0.72
1.05*0.22 = 0.23
0.88*0.22 = 0.19
The CMI is greater than one, which indicates that DC has a case mix that tends to produce artificially high salaries. Divide by CMI to get an adjusted salary for DC
50 / 1.14 = 43.9.
What are the strengths and weaknesses of case mix adjustment? Case mix adjustment is very simple and can be done with a spreadsheet program if you desire. It cannot incorporate adjustments for continuous covariates. This method also can only adjust for variables that are imbalanced across the groups.
There are still other approaches to risk adjustment, such as propensity scores, that I will discuss in future newsletters. This work was part of a project I am helping with: the adjustment of outcome measures in the National Database of Nursing Quality Indicators. The work described in this article was supported in part through a grant of the American Nursing Association.
2. Power for a three-arm study
Someone asked for advice on how to calculate power for a three arm study. The outcome measure in this experiment is binary. For a two-arm study, the power calculation is easy. Just use the formula for power for a test comparing two proportions.
You could do this here as well, and demonstrate that the power for the three possible comparisons (1 vs 2, 1 vs 3, 2 vs 3) among the proportions is at least 80%. But a more formal calculation would use the Chi-squared test statistic.
For a chisquare test, you need to estimate the non-centrality parameter by creating some fake data that corresponds to the proportions of yes/no in each group that you think represents a clinically relevant shift. For example, you might hypothesize that if arms 1, 2, and 3, have proportions 0.2, 0.3, and 0.4, that you would have to have to look at a chisquare statistic for a table that looks something like
The expected counts here would be
and the chisquare statistic for this artificial setting would be
10^2/30+10^2/70+0^2/30+0^2/70+10^2/30+10^2/70 = 9.52
That's your noncentrality parameter, assuming 100 patients per group, 300 patients total.
Here's how you would enter the data into a Java applet for power written by Dr. Russ Lenth.
In this dialog box, Chi2* represents the noncentrality parameter using an idealized data set and n* is the total sample size in your idealized data set. In this situation, the power for a total sample size of 300 is almost 80%. If the sample size were decreased to 200, the power would decrease to 61%
and if the total sample size were increased to 400, the power would increase to 90%.
You can find Dr. Lenth's free Java power applets at
Additional discussion of this example, including screenshots from another free power calculation program, G*Power, is available at
3. The fourth deadly sin of researchers: lust
The fourth deadly sin of researchers is lust. Many researchers desire fame and recognition, and while this is often a positive motivating factor, sometimes this desire turns to a lust for recognition that can distort and harm the research process.
Duplicate publication. In order to let each author get his/her place in the sun, researchers will publish the same data twice, with different lead authors, and no hint in the publications that the data were published elsewhere. This is a serious problem for meta-analysis, as it can lead to double counting of some research results.
Aversion to multi-center trials. In a belief that being a big fish in a small pond is better than being a small fish in a big pond, researchers would prefer being first author on a small single center study than fifth author on a large multi-center study. Since each single center study uses a slightly different protocol, the meta-analysts have to cope with a greater degree of heterogeneity in the research results.
Use of the least publishable unit. In an attempt to pad the number of publications to meet a quota for tenure or promotion, researchers may try to break their research into the smallest pieces possible and submit each piece to a separate journal. This leads to a fragmentary perspective on a research topic. There are some arguments in favor of publishing small articles, especially for younger faculty, but this is often taken too far.
Outright fraud. While many cases of research fraud involve money, others involve non-monetary rewards, such as fame and recognition. I'll admit that I get a big thrill when I see my name in print, or when I get an invitation to talk at a research conference. This desire for fame has caused some people to make up data. It helps if you make-up the right type of data, data that supports hypotheses that scientists suspect, but where getting actual data on these hypotheses is hard.
In previous newsletters, I have highlighted some of the other deadly sins of researchers:* gluttony, www.pmean.com/news/2009-09.html#2,
* sloth, www.pmean.com/news/2009-07.html#3, and
* pride www.pmean.com/news/2000-05.html#2.
The remaining three sins of researchers (wrath, greed, and envy) will be described in future issues of this newsletter.
4. Monthly Mean Article (peer reviewed): Extent of publication bias in different categories of research cohorts: a meta-analysis of empirical studies
Fujian Song, Sheetal Parekh-Bhurke, Lee Hooper, et al. Extent of publication bias in different categories of research cohorts: a meta-analysis of empirical studies. BMC Medical Research Methodology. 2009;9(1):79.Abstract: "BACKGROUND: The validity of research synthesis is threatened if published studies comprise a biased selection of all studies that have been conducted. We conducted a meta-analysis to ascertain the strength and consistency of the association between study results and formal publication. METHODS: The Cochrane Methodology Register Database, MEDLINE and other electronic bibliographic databases were searched (to May 2009) to identify empirical studies that tracked a cohort of studies and reported the odds of formal publication by study results. Reference lists of retrieved articles were also examined for relevant studies. Odds ratios were used to measure the association between formal publication and significant or positive results. Included studies were separated into subgroups according to starting time of follow-up, and results from individual cohort studies within the subgroups were quantitatively pooled. RESULTS: We identified 12 cohort studies that followed up research from inception, four that included trials submitted to a regulatory authority, 28 that assessed the fate of studies presented as conference abstracts, and four cohort studies that followed manuscripts submitted to journals. The pooled odds ratio of publication of studies with positive results, compared to those without positive results (publication bias) was 2.78 (95% CI: 2.10 to 3.69) in cohorts that followed from inception, 5.00 (95% CI: 2.01 to 12.45) in trials submitted to regulatory authority, 1.70 (95% CI: 1.44 to 2.02) in abstract cohorts, and 1.06 (95% CI: 0.80 to 1.39) in cohorts of manuscripts. CONCLUSIONS: Dissemination of research findings is likely to be a biased process. Publication bias appears to occur early, mainly before the presentation of findings at conferences or submission of manuscripts to journals." [Accessed November 29, 2009]. Available at: www.biomedcentral.com/1471-2288/9/79.
5. Monthly Mean Article (popular press): Lack of Study Volunteers Hobbles Cancer Fight
Gina Kolata. Lack of Study Volunteers Hobbles Cancer Fight. The New York Times. 2009. Excerpt: "There are more than 6,500 cancer clinical trials seeking adult patients, according to clinicaltrials.gov, a trials registry. But many will be abandoned along the way. More than one trial in five sponsored by the National Cancer Institute failed to enroll a single subject, and only half reached the minimum needed for a meaningful result, Dr. Ramsey and his colleague John Scoggins reported in an editorial in the September 2008 issue of The Oncologist." [Accessed August 29, 2009]. Available at: www.nytimes.com/2009/08/03/health/research/03trials.html.
6. Monthly Mean Book: Where's the evidence? Controversies in modern medicine
William A Silverman. Where's the evidence? Controversies in modern medicine. New York: Oxford University Press Excerpt: "Medicine is moving away from reliance on the proclamations of authorities to the use of numerical methods to estimate the size of effects of its interventions. But a rumbling note of uneasiness underlines present-day medical progress: the more we know, The more questions we encounter about what to do with the hard-won information. The essays in Where's the Evidence examine the dilemmas that have arisen as the result of medicine's unprecedented increase in technical powers. How do doctors draw the line between "knowing" (the acquisition of new medical information) and doing" (the application of that new knowledge)? What are the long-term consequences of responding to the demand that physicians always do everything that can be done? Is medicine's primary aim to increase the length of life? Or is it to reduce the amount of pain and suffering? And who is empowered to choose when these ends are mutually exclusive? This engaging collection of essays will be of interest to professionals interested in the evidence-based medicine debate, including epidemiologists, neonatologists, those involved in clinical trials and health policy, medical ethicists, medical students, and trainees." Available at: lccn.loc.gov/97052058.
7. Monthly Mean Definition: What is a Kaplan-Meier survival curve?
The Kaplan-Meier (KM) survival curve is a graphical display of the estimated probability that an event will occur. This explanation will focus on mortality, but there is nothing about a KM curve that requires the event to be an actual death. Other events, such as relapse or re-hospitalization, can also be displayed using KM. The KM curve is not restricted to events occurring in people but can also assess things like the failure of medical devices. Finally, the KM curve does not always have to summarize gloomy events. Time to positive events, such as pregnancy, can also be evaluated using a KM curve.
The KM curve can handle dropouts, patients who provide information during part of the study but who leave prior to experiencing the event. It's kind of like that joke about the couple who were both in their nineties who go to a lawyer asking for help getting a divorce. The lawyer looks surprised, and asks how long they've been married. Sixty years, the husband replied, and we've been miserable for all sixty years. The wife nodded in agreement. The lawyer asked, if you've been miserable all this time, why have you waited so long to get a divorce. The wife sighed and said, we wanted to wait until all of the children were dead.
Researchers can't wait until all of the patients in a study have died before they publish results of a mortality study. It would take too long. There are also problems with patients who leave town and don't provide a forwarding address. The term censoring is used to describe patients who are known to have survived for at least a certain amount of time, but the actual date of their death is unknown other than being beyond the time they were evaluated.
Consider a hypothetical experiment involving mortality in a sample of 25 fruit flies. The researcher diligently records the day of death for each fruit fly. The first fly dies on day 37, the second on day 42, and so forth. The researcher gets the number of days of life for 15 of the 25 flies, but one day, the experimenter leaves the cover off of the container. The ten remaining fruit flies buzz off and are never seen again. You might think that the experiment is ruined, but you can still salvage some of the data. You have the days of life for the first fifteen flies, and the 13th provides information about the median survival time.
The information about censored observations needs to be incorporated carefully in the analysis, and you have to assume that the fact that a patient was lost to follow-up is independent of their prognosis. If a patient drops out because they are flying to Mexico for laetrile treatments is going to be problematic. No one with a good prognosis flies to Mexico for laetrile.
The KM curve is frequently used to compare survival in two or more groups. We can look for gaps in these curves in a horizontal or vertical direction. A vertical gap means that at a specific time point, one group had a greater fraction of subjects surviving. A horizontal gap means that it took longer for one group to experience a certain fraction of deaths. Here's a nice example of a KM curve published in an open source journal (Bachmann et al. BMC Cancer 2009 9:255 doi:10.1186/1471-2407-9-255, www.biomedcentral.com/1471-2407/9/255/figure/F2).
Here's an example of a KM curve comparing survival of patients with cachexia to patients without cachexia.
Notice a pronounced gap in the horizontal direction. The median survival time is much larger (about 200 days larger) in the patients without cachexia.
There is also a pronounced gap in the vertical direction. At 500 days, the probability of survival is about 45% in the patients without cachexia and only 25% in the patients with cachexia.
I wrote a nice description of the Kaplan-Meier curve in Chapter 6 of my book, Statistical Evidence. I'm working on some updates of this description as well as updates on material from my old website.
8. Monthly Mean Quote: A few years ago, while doing a research project...
A few years ago, while doing a research project, I measured several different hormones in about 30 subjects. One subject's growth hormone levels came back about 100 times higher than everyone else's. I assumed this was a transcription error, so I moved the decimal point two places to the left. Some weeks later, I met the technician who had analysed the specimens and he asked, "Whatever happened to that chap with acromegaly?" Trish Greenhalgh, www.bmj.com/cgi/content/full/315/7104/364.
9. Monthly Mean Website: The Median Isn't the Message
The Median Isn't the Message by Stephen Jay Gould and Prefatory Note by Steve Dunn. Description: This is a classic treatise that anyone researching their own illness should read. Dr. Dunn's comments are spot on. "As far as I'm concerned, Gould's The Median Isn't the Message is the wisest, most humane thing ever written about cancer and statistics. It is the antidote both to those who say that, "the statistics don't matter," and to those who have the unfortunate habit of pronouncing death sentences on patients who face a difficult prognosis. Anyone who researches the medical literature will confront the statistics for their disease. Anyone who reads this will be armed with reason and with hope." [Accessed November 19, 2009]. Available at: cancerguide.org/median_not_msg.html
10. Nick News: Nicholas the iceskater
One of the local shopping centers has an ice rink and they just opened for the season. Nicholas, who had only skated on ice once before, stepped bravely out and started skating like a pro.
You can find additional pictures at
Also note a picture of Nicholas smile with several big gaps in it at
11. Very bad joke: There are three types of statisticians...
There are three types of statisticians in the world, those who can count and those who can't.
This is a classic joke told with economists, accountants, etc. in place of statisticians. There are so many citations like this on google, that I can't pick out one. A cute variation on this for the programming geeks among you is
There are 10 types of programmers in the world, those who understand binary and those who don't.
12. Tell me what you think.
Thank you for taking the time to provide feedback for the November 2009 issue of the Monthly Mean. Your responses will be kept anonymous. I have three short open ended questions at
You can also provide feedback by responding to this email. My three questions are:
- What was the most important thing that you learned in this newsletter?
- What was the one thing that you found confusing or difficult to follow?
- What other topics would you like to see covered in a future newsletter?
Three people provided feedback to the last newsletter. I got praise for the article on rounding and re-ordering and the article on early stopping of a study and general praise for the newsletter. The only concern was about the basic formatting. I don't know what this means and I'd appreciate additional information from the person who left this comment or anyone else. Topics suggested for future newsletters include longitudinal analysis and robust analysis. I have been intending to write up a series of webpages about longitudinal analyses, but it's a complicated area and takes a lot of time to develop. I do hope to have something soon about this.
13. Upcoming statistics webinars
Free to all! What do all these numbers mean? Odds ratios, relative risks, and number needed to treat, Thursday, December 17, 2009, 11am-noon, CST. Abstract: This one hour training class will teach you some of the numbers used in studies where the outcome only has two possible values (e.g., dead/alive). The odds ratio and the relative risk are both measures of risk used for binary outcomes, but sometimes they can differ markedly from one another. The relative risk offers a more natural interpretation, but certain research designs preclude its computation. Another measure of risk, the number needed to treat, provides comparisons on an absolute rather than relative scale and allow you to assess the trade-offs between effects and harms. No statistical experience is necessary.
To register, send an email to with the words "December 17 webinar" in the subject line. A draft handout is available below. The link to the official handout will appear here 24 hours prior to the webinar.
Free to all! The first three steps in a descriptive data analysis, with examples in PASW/SPSS, Thursday, January 21, 2010, 11am-noon, CST. Abstract: This one hour training class will give you a general introduction to descriptive data analysis using PASW (formerly known as SPSS) software. The first three steps in a descriptive data analysis are: (1) know how much data you have and how much data is missing; (2) compute ranges or frequencies for individual variables; and (3) compute crosstabs, boxplots, or scatterplots to examine relationships among pairs of variables. This class is useful for anyone who needs to analyze research data. No statistical experience or knowledge about PASW/SPSS is necessary.
To register, send an email to with the words "January 21 webinar" in the subject line. A draft handout is available below. The link to the official handout will appear here 24 hours prior to the webinar.
14. Why do I do all this for free?
I write this newsletter and offer free statistics webinars partly for fun and partly to build up goodwill for my consulting business, www.pmean.com/consult.html. Unfortunately, I have not had much success in publicizing these efforts; after one year, I only have 148 subscribers. If you like what I write, please forward this email to others you know who might also find my work interesting. There's a link at the bottom of this newsletter that makes forwarding easy. My goal is to have 1,500 subscribers by the end of 2010, and I can only meet that goal with your help.
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