## Archive for the ‘meta-analysis’ Category

### Where has evidence based medicine taken us in 20 years?

January 10, 2014

This one of the best appraisals of evidence based medicine(EBM):

http://www.kevinmd.com/blog/2013/06/evidence-based-medicine-20-years.html

It highlights one important pitfall of EBM, ie its failure to integrate scientific(biological) plausibility in the pyramid of evidence.

I think the best way to effect a synthesis between evidence from clinical data and biological plausibility is through a Bayesian argument:

– Decide on a standard EBM approach to synthesize the clinical data regarding a hypothesis from multiple sources eg a meta-analysis
– Encode the non-clinical, background knowledge into a prior. This prior encodes the scientific plausibility of the hypothesis under question

A Bayesian analysis of the clinical data under a scientific plausibility prior provides a transparent way to leverage the pyramid evidence of EBM while providing a basic science/disease understanding context for the interpretation of clinical data.

### Extracting standard errors and treatment effects from medical journal tables (powered by R)

November 10, 2013

I decided to start blogging the R code used for some of my statistical posts, so I will start with the meta-analysis posts and move on to more difficult stuff.

As stated previously (here and here) the problem is to convert the reported relative risks(RR, $t$), 95% confidence interval ($t_L, t_U$) and p-value ($p_v$) into estimates for the log-relative risk ratio and its associated standard error for down-stream use (usually meta-analysis). Medical journals are in the bad habit of exponentiating (and rounding) the output of statistical software so that one needs to manipulate the reported estimates in order to recover the output of the statistical software. (more…)

### Meta-analytic Death Match : dumb algebra guy v.s. the Bayesian King Kong

May 2, 2013

Maybe I overdid it with the title, but I think the data speak for themselves in the (?much) anticipated,  pre-announced comparison between a naive, algebra based solution and the Bayesian, Monte Carlo based one.

To carry out this comparison, I assumed: (more…)

### An algebra guy’s take on the meta-analysis posts

April 29, 2013

As I was reading through the meta-analysis posts in order to correct various typos, the forgotten non-probability me woke up and raised the following question:

What if one were to treat the reported RR ($t$), 95% confidence interval ($t_L, t_U$) and p-value ($p_v$) as the true values of the non-reported quantities, in essence ignoring the round-off error?

Could this lead to a (?simpler) solution bypassing the need for Monte Carlo? What this solution would look like and how it differs (implementationally) from the Bayesian one ? More importantly how does it hold up against the Bayesian solution?

### Extracting standard errors and effect estimates for meta-analysis: paging Rev Bayes

April 14, 2013

After a very long leave of absence I return to the issue of extracting the effect estimate ($T$) and standard error ($se$) from reported and (rounded to a fixed number of decimal points) relative risk ($t$), limits of 95% confidence intervals ($t_L$ and $t_U$) and p-value ($p_v$) figures found in scientific publications. This solution is a Bayesian one, requiring nothing more than a straightforward application of the Bayes theorem for the posterior distribution of the A straightforward application of Bayes theorem for the quantities $T, se$ given the $t, t_L, t_U, p_v$:

$P(T,se|t, t_L, t_U, p_v) \propto P(t|T,se,t_L, t_U, p_v) \times P(T,se|t_L, t_U, p_v)$

### Extracting standard errors from 95%CI for meta-analysis (or weird uses for measurement error models)

December 29, 2012

I was recently confronted with the task of running a meta-analysis of a subject in which the various studies had reported (adjusted) measures of treatment efficacy on various continuous outcomes. This is one of the areas in which the data for meta-analysis comes not in the usual form of #events and #patients(N) , but as treatment effects and their associated standard errors. Not too uncommon examples include the effects of a given intervention on Blood Pressure, Cholesterol levels, Psychometric scales, Cox regression Hazard Ratios or Logistic Regression Odds Ratios etc. And then it hit me: for almost all of the studies I wanted to pool, I did have access at all to the actual data that I had to process!! For sure, there were treatment effects (actually hazard ratios,HRs, for my project) in the papers but the standard errors were not reported; furthermore, the information that was actually contained in the manuscripts (HRs, 95%CI and the p-value) was not the “real thing” but its approximation, rounded down to one (and sometimes two) significant digits.

I’m sure that others have run into this issue previously, but I have never seen a formal, discussion for handling this missing data problem. (more…)