10 posts · 5,723 views
This blog is devoted to a science- and evidence-based approach to public health. The evidence basis is accumulating faster than any one person can synthesize it, but decisions at all levels should be informed by the best possible information.
Ryan
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- January 7, 2010
- 05:40 PM
- 745 views
Achtung, Baby: Hazard Ratios
by Ryan in Evidence-Based Public Health
The hazard ratio is the statistic of choice for nearly all medical research involving time. And by far the most common method of deriving hazard ratios from data is via the Cox Proportional Hazards model. In a great little editorial in this month's Epidemiology, Miguel Hernán lays out what we lose and what we can gain with a more subtle approach.... Read more »
Hernán, M. (2010) The Hazards of Hazard Ratios. Epidemiology, 21(1), 13-15. DOI: 10.1097/EDE.0b013e3181c1ea43
- November 19, 2009
- 09:11 PM
- 710 views
Multilevel (Quantile) Regression - A Question for Gelman
by Ryan in Evidence-Based Public Health
Multilevel (or hierarchical) regression modeling is very popular in the social sciences. So what I want to do is a hierarchical quantile regression of the 75% quantile of time spent in jail. And that was my question for Andrew Gelman.... Read more »
Carmichael SL, Witte JS, & Shaw GM. (2009) Nutrient pathways and neural tube defects: a semi-Bayesian hierarchical analysis. Epidemiology (Cambridge, Mass.), 20(1), 67-73. PMID: 19234400
- November 10, 2009
- 11:33 PM
- 686 views
'Cause I said so... The Sufficient-Component Cause model and what it can tell us about cancer screening: Part I
by Ryan in Evidence-Based Public Health
... Read more »
Flanders WD. (2006) On the relationship of sufficient component cause models with potential outcome (counterfactual) models. European journal of epidemiology, 21(12), 847-53. PMID: 17048084
- January 9, 2010
- 02:55 PM
- 598 views
Crime and Public Health: Plumbum Causa
by Ryan in Evidence-Based Public Health
There is a distressingly myopic tendency among our existing social programs. Health departments ignore crime, and miss a valuable opportunity to improve social well-being.... Read more »
Wright, J., Dietrich, K., Ris, M., Hornung, R., Wessel, S., Lanphear, B., Ho, M., & Rae, M. (2008) Association of Prenatal and Childhood Blood Lead Concentrations with Criminal Arrests in Early Adulthood. PLoS Medicine, 5(5). DOI: 10.1371/journal.pmed.0050101
- February 8, 2010
- 01:49 PM
- 598 views
Methods Monday: Maximum Likelihood in SAS using PROC NLP
by Ryan in Evidence-Based Public Health
I've been working on fitting some excess relative risk (ERR) models to case-control data on occupational exposures lately. ERR models are of the form:RR=1+β*XIn SAS, unfortunately, we don't have unlimited freedom in defining the form of the model we want to fit, but a recent paper by Langholz and Richardson [behind firewall] describes a way that we can solve for parameters once we specify the likelihood function. (For those interested, the likelihood function can be thought of as the function that would be most likely to give rise to the data. We define it with some variables, and then try to solve for the variable(s) that maximize the likelihood function. This falls into the class of methods called maximum likelihood estimation.)The general conditional logistic likelihood is pretty simple (phi represents the odds or rate ratio function) :The best way to conceptualize this equation is as: divide the data you observed by all possible permutations of the data.This function is then maximized with respect to beta (for the mat-inclined, an iterative process minimizes the derivative of the log of the function to look for the global maximum).The method described by Langholz and Richardson makes use of a nifty little SAS procedure called PROC NLP (the NLP stands for non-linear programming). It basically does exactly what I just described: you can specify a function and host of parameters, and it will iteratively search for a maximum value of the function, and spit out the parameters that yield the maximum.A cool extension of this is that you can define complex "mixture models" that contain two distinct models that are each exponentiated: one to alpha, one to 1-alpha. You then multiply the two exponentiated models together. If you then maximize the likelihood, including the parameter alpha, you get a neat little value that tells you the relative importance of each of the two models in the full mixture model. For example:RR=[(βX)^α]*[(exp(βX))^(1-α)]PROC NLP lets you specify this model form and get an estimate of which model (linear or exponential) fits better, depending on whether alpha is closer to zero or one.Langholz, B., & Richardson, D. (2009). Fitting General Relative Risk Models for Survival Time and Matched Case-Control Analysis American Journal of Epidemiology, 171 (3), 377-383 DOI: 10.1093/aje/kwp403... Read more »
Langholz, B., & Richardson, D. (2009) Fitting General Relative Risk Models for Survival Time and Matched Case-Control Analysis. American Journal of Epidemiology, 171(3), 377-383. DOI: 10.1093/aje/kwp403
- November 13, 2009
- 09:50 PM
- 530 views
Methods Blogging: The Prostate Cancer Prevention Trial
by Ryan in Evidence-Based Public Health
It's somewhat defeating to acknowledge, but a large part of the strength (and beauty) of this study lies in its simplicity. Placebo-controlled, double-blinded, randomized; these are the things biostatisticians dream of. Luckily, I'm not one; but I can still appreciate it.... Read more »
Thompson, I. (2003) The Influence of Finasteride on the Development of Prostate Cancer. New England Journal of Medicine, 349(3), 215-224. DOI: 10.1056/NEJMoa030660
Gerald L. Andriole, M.D., E. David Crawford, M.D., Robert L. Grubb, III, M.D., Saundra S. Buys, M.D., David Chia, Ph.D., Timothy R. Church, Ph.D., Mona N. Fouad, M.D., Edward P. Gelmann, M.D., Paul A. Kvale, M.D., Douglas J. Reding, M.D., Joel L. Weissfel. (2009) Mortality Results from a Randomized Prostate-Cancer Screening Trial. New England Journal of Medicine, 360(17), 1797-1797. DOI: 10.1056/NEJMx090012
- November 16, 2009
- 10:50 PM
- 492 views
Prevalence in Place of Plausibility: NCCAM Call for Comments
by Ryan in Evidence-Based Public Health
NCCAM has funded, to the tune of half a million dollars, of study of magnets and carpal tunnel syndrome.... Read more »
Colbert, A., Wahbeh, H., Harling, N., Connelly, E., Schiffke, H., Forsten, C., Gregory, W., Markov, M., Souder, J., Elmer, P.... (2007) Static Magnetic Field Therapy: A Critical Review of Treatment Parameters. Evidence-based Complementary and Alternative Medicine, 6(2), 133-139. DOI: 10.1093/ecam/nem131
- January 6, 2010
- 03:30 PM
- 491 views
Innovation in Health: Socialism and Innovation
by Ryan in Evidence-Based Public Health
What's the motivation for innovation in healthcare, and does any degree of socialization at any level have an impact?... Read more »
Conrad, D., & Perry, L. (2009) Quality-Based Financial Incentives in Health Care: Can We Improve Quality by Paying for It?. Annual Review of Public Health, 30(1), 357-371. DOI: 10.1146/annurev.publhealth.031308.100243
- November 12, 2009
- 02:58 PM
- 472 views
Cell Phones and Cancer?
by Ryan in Evidence-Based Public Health
CNN has a story about the link between cell phone usage and tumors. Unfortunately, the article tends towards the sensational, and doesn't cite the many recent studies that have failed to find a link between cell phone use and cancer.... Read more »
Ahlbom A, Feychting M, Green A, Kheifets L, Savitz DA, Swerdlow AJ, & ICNIRP (International Commission for Non-Ionizing Radiation Protection) Standing Committee on Epidemiology. (2009) Epidemiologic evidence on mobile phones and tumor risk: a review. Epidemiology (Cambridge, Mass.), 20(5), 639-52. PMID: 19593153
- February 2, 2010
- 09:41 PM
- 401 views
More on abstinence
by Ryan in Evidence-Based Public Health
A recent paper describes a significant benefit from abstinence-only education. The story is more complicated...... Read more »
Jemmott, J., Jemmott, L., & Fong, G. (2010) Efficacy of a Theory-Based Abstinence-Only Intervention Over 24 Months: A Randomized Controlled Trial With Young Adolescents. Archives of Pediatrics and Adolescent Medicine, 164(2), 152-159. DOI: 10.1001/archpediatrics.2009.267
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