Stats >> WebLog (Started on February 3, 2004)

This weblog is a collection of materials that I find interesting and useful. I hope you find these entries useful as well. The dates listed on the weblog are the dates when the original entry was written. I will make some minor editorial changes from time to time, but I'll note any major changes with a revision date. Some of these entries are incomplete because I was interrupted while writing them. I try to finish them up within the next day or so, but once in a while a weblog entry stays unfinished for some time. Please be patient, as I can only work on these web pages during quiet times at work.

Archive

Archive arranged by topic

The last ten weblog entries

  1. Stats: A short biography that can be used as an introduction (May 9, 2008). I'm giving a talk today, and I was asked to provide some material that could be used to introduce me.
  2. Stats: Why does a Bayesian approach make sense for monitoring accrual? (May 8, 2008). I'm working with Byron Gajewski to develop some models for monitoring the progress of clinical trials. Too many researchers overpromise and undeliver on the planned sample size and the planned completion date of their research This leads to serious delays in the research and inadequate precision and power when the research is completed. We want to develop some tools that will let researchers plan the pattern of patient accrual in their studies. These tools will also let the researchers carefully monitor the progress of their studies and let them take action quickly if accrual rates are suffering. We've adopted a Bayesian approach for these tools. While a Bayesian approach to Statistics is controversial, we feel that there should be no controversy with regard to using Bayesian models in modeling accrual.
  3. Stats: Slipped deadlines and sample size shortfalls in a random sample of research studies (May 7, 2008). There is a limited amount of data out there that suggests that many researchers overpromise on the planned sample size and completion date and underdeliver. About a year ago, I received a small grant to study the proportion of studies at Children's Mercy Hospital (CMH) that failed to meet the proposed completion deadlines, that failed to recruit the promised number of patients or both. Here is a brief summary of these results.
  4. Stats: Monitoring refusals and exclusions in a clinical trial (May 1, 2008). Someone sent me an email asking about the work that Byron Gajewski and I have done on monitoring accrual patterns in clinical trials. She had been doing something similar at her job and wanted to see if we could collaborate. In her situation, the major issue was the number of patients who made an initial contact but did not keep their first appointment, the number of patients who kept the appointment, but refused to sign the consent form once they realized what the study was about, and the number of patients who did sign the consent form, but who did not meet the inclusion criteria once the initial screening was done.
  5. Stats: Directions to my new office (April 25, 2008). I have moved to a new office. It is a modular building just north of Children's Mercy Hospital. It is between 23rd and 22nd street, just off of Kenwood Avenue (Kenwood is a small north/south street just west of Holmes). If you need to get from your office to mine, here are some directions written by my Administrative Assistant, Judy Champion.
  6. Stats: Nomination for the Kreamer Award for Research Excellence (April 24, 2008). Every year, Children's Mercy Hospital offers the Kreamer Award for Research Excellence. I plan to apply this year. I wanted to outline the requirements for the award and offer an overview of why I would be a good candidate for this award.
  7. Stats: Upcoming topics in Poisson regression (April 24, 2008). I get a lot of questions about Poisson regression. I feel embarrassed when this happens because my pages on this topic are woefully incomplete. Everything on my web pages is incomplete to some extent, of course, but this is an area with the biggest gaps. I have been planning for quite a while to write more about this topic, and here are some of the areas I want to discuss.
  8. Stats: I hate bad research examples (April 23, 2008). Someone wrote in asking if I know of any good examples of research studies that illustrate problems of making false generalizations. I had to mention my book, of course, which has lots of commentary of actual publications, most of which are open source and freely available on the web. For what it’s worth, I do have a pedagogical bone to pick. I believe it is not a good idea to find a “bad” publication and tear it apart.
  9. Stats: Evidence Based Medicine for patients (April 23, 2008). There was an interesting email exchange on the email discussion group EVIDENCE-BASED-HEALTH@JISCMAIL.AC.UK. The first correspondent (TH) described a series of workshops that are intended to help patients access and evaluate health related websites.
  10. Stats: A brief overview of instrumental variables (April 14, 2008). People will often ask me questions that are outside my area of expertise. Yes, I know you're shocked to hear this, but there are lots of areas of statistics where I only have a vague understanding. One of these questions was about instrumental variables. I could only offer a vague explanation, but I hope that is better than no explanation at all.

The last ten interesting articles, interesting books, or interesting websites.

  1. GRADE working group Excerpt: The Grading of Recommendations Assessment, Development and Evaluation (short GRADE) Working Group began in the year 2000 as an informal collaboration of people with an interest in addressing the shortcomings of present grading systems in health care. The working group has developed a common, sensible and transparent approach to grading quality of evidence and strength of recommendations. Many international organizations have provided input into the development of the approach and have started using it.
  2. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Description: This article presents the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system for rating evidence in a systematic overview or a clinical guideline. The system examines the quality of evidence, uncertainty about the balance between desirable and undesirable effects, uncertainty or variability in values and preferences, and uncertainty about whether the intervention represents a wise use of resources.
  3. How do we assess the quality of information? Description: This website provides a checklist of questions that you can use to assess the quality of web pages that provide health information.
  4. Negative Consequences of Dichotomizing Continuous Predictor Variables Description: This Java applet shows graphically how creating a median split for a predictor variable leads to loss of precision and power.
  5. StatLinks: Applied statistics, data analysis, and visualization Description: This website provides links to resources of interest to most practicing statisticians. It uses a social bookmarking system (SlinkSet), which means that any registered user can add links and can vote on links of others that they like.
  6. Instrumental variable Excerpt: In statistics and econometrics, an instrumental variable (IV, or instrument) can be used to produce a consistent estimator of a parameter when the explanatory variables (covariates) are correlated with the error terms. Such correlation can be caused by endogeneity, by omitted covariates, or by measurement errors in the covariates. In this situation, ordinary linear regression produces biased and inconsistent estimates. However, if an instrument is available, consistent estimates may still be obtained. An instrument is a variable that does not itself belong in the explanatory equation, that is correlated with the suspect explanatory variable, and that is uncorrelated with the error term.
  7. Instrumental Variable Estimation Excerpt: One way of identifying models that cannot be estimated by using multiple regression is through the use of instrumental variables. For path analysis, the disturbance must not be correlated with each causal variable. There are three reasons why such a correlation might exist: * Spuriousness (Third Variable Causation): A variable causes both the endogenous variable and one its causal variables and that variable is not included in the model. * Reverse Causation (Feedback Model): The endogenous variable causes, either directly or indirectly, one of its causes. * Measurement Error: There is measurement error in a causal variable.
  8. Blind Prejudice - "Hard" scientists believe they are immune to bias. Description: This article makes the claim that parapsychology is far more rigorous thatn other scientific methods because their research papers use blinding far more often than other disciplines. It includes a quote from Rupert Sheldrake Most hard scientists take it for granted that blind techniques are unnecessary in their own field. Parapsychologists, on the other hand, have been constantly subjected to intense scrutiny by sceptics, and this has made them more rigorous." This claim is overly simplistic in my opinion, because blinding is just one of many dimensions of quality that need to be considered.
  9. Don’t Ask, Don’t Tell? Transfer and Sale of De-Identified Patient Data. Description: This article reviews the privacy regulations associated with research and offers an explanation of de-identifed data. The authors raise some provocative issues about individual property rights to medical data, even data that has been de-identified.
  10. An alternative to null-hypothesis significance tests. Description: This article describes p-rep, a statistic that measures the probability of replication. The article argues that this measure is superior to the p-value and also covers the mathematical details needed for calculation of the statistic.