Tag Archives: Observational study

New blood thinners: the French studies in real life.

14 Jul

One more time CNAMTS boys have crunched the numbers from the reimbursement data bases.
They previously had done this exercise in real life for the mediator and they had found cardiac side effects. This time they investigated a new category of blood thinner the NACOs (Nouveaux Anti Coagulants). Unlike the mediator they have concluded that in the short term (3 months) no evidence of any adverse side effects such as bleeding or thrombosis could be found.

The mediator study:
http://www.ncbi.nlm.nih.gov/pubmed/20945504#

The NACOS study of the risk associated with the initiation of treatment with the new blood thinner in anticoagulant treatment naive patients (3 months of follow up):
http://ansm.sante.fr/var/ansm_site/storage/original/application/6372793e0dfaf927308665a647ed0444.pdf

The NACOS study of the risk associated with the change in treatment consisting in replacing Warfarin by the new blood thinner in anticoagulant treatment experienced patients (4 months of follow up):
http://ansm.sante.fr/var/ansm_site/storage/original/application/5504a80da7d6ec6eab26798eebf64fb3.pdf

Historical cohorts: strengths and weaknesses

8 Jul

Historic cohort study, generally means to take a look back at events that already have taken place.

database

database (Photo credit: Sean MacEntee)

With the huge data bases containing Millions of lines of historic of several years of reimbursements of health care and health conditions now at the disposal of nation wide health care insurances like CNAMTS or RSI in France or Kaiser Permanente or Veteran Affairs in the USA , historical cohorts such as the one that is described in the article referenced below are very easy to implement provided that researchers have access to the data base and use the appropriate software to extract accurately the information to transform rough data in a relevant medical information. Personally I am a fan of SAS enterprise guide (no funding by SAS to disclose for this Blog).
But nothing being perfect in this world the weakness of such historical cohorts retrospectively rebuilt is that they can only put in evidence associations without absolutely no hint but the possible causation process involved in the association. Their force is of course the number of subjects analyzed (usually huge) and the provenance of the subjects (community and real life subjects as opposed as the carefully selected subjects of the controlled randomized trials).
But at the end of the day, to conclude like the study referenced below does, that high doses of ACE treatment causes a lowering of the mortality rate and the readmission rate is obviously going beyond the proper results of the study. Indeed no observational historic cohort, whatever the size of the analysed sample is, has the power to demonstrate a causality link. One possible explanation of the association unveiled by the study is that prescribers could be more reluctant to give high doses of ACE to the more fragile groups and comorbidity incurring groups of the studied population.

More content and referenced study:

ARCH INTERN MED PUBLISHED ONLINE JULY 2, 2012 WWW.ARCHINTERNMED.COM

Improved Outcomes in Heart Failure Treated With High-Dose ACE Inhibitors and ARBs: A Population-Based Study: full text research letter

Evaluation of Scientific Publications

29 Oct
Coverage Probability of Clopper-Pearson confid...

Image via Wikipedia

In his blog named “OH-world” John Cherrie from Edinburgh, United Kingdom, signaled us an interesting series of seventeen articles freely available in full text on PubMedCentral. The first of the series is entitled Critical Appraisal of Scientific Articles; Part 1 of a Series on Evaluation of Scientific Publications.

The title of the following ones are listed below:

1. Critical Appraisal of Scientific Articles

2. Study Design in Medical Research

3. Types of Study in Medical Research

4. Confidence Interval or P-Value?

5. Requirements and Assessment of Laboratory Tests: Inpatient Admission Screening

6. Systematic Literature Reviews and Meta-Analyses

7. The Specification of Statistical Measures and Their Presentation in Tables and Graphs

8. Avoiding Bias in Observational Studies

9. Interpreting Results in 2×2 Tables

10. Judging a Plethora of p-Values: How to Contend With the Problem of Multiple Testing

11. Data Analysis of Epidemiological Studies

12. Choosing statistical tests

13. Sample size calculation in clinical trials

14. Linear regression analysis

15. Survival analysis

16. Concordance analysis

17. Randomized controlled trials

An other way to be able to evaluate a scientific article in medicine is to read the fourteen articles constituting the Clinical Chemistry Guide to Scientific Writing. The first article is entitled The Title Says It All.
The following articles are listed below:
Part 1. The Title Says It All

Part 2. The Abstract and the Elevator Talk: A Tale of Two Summaries

Part 3. “It was a cold and rainy night”: Set the Scene with a Good Introduction

Part 4. Who, What, When, Where, How, and Why: The Ingredients in the Recipe for a Successful Methods Section

Part 5. Show Your Cards: The Results Section and the Poker Game

Part 6. If an IRDAM Journal Is What You Choose, Then Sequential Results Are What You Use

Part 7. Put Your Best Figure Forward: Line Graphs and Scattergrams

Part 8. Bars and Pies Make Better Desserts than Figures

Part 9. Bring Your Best to the Table

Part 10. The Discussion Section: Your Closing Argument

Part 11. Giving Credit: Citations and References

Part 12. How to Write a Rave Review

Part 13. Top 10 Tips for Responding to Reviewer and Editor Comments

Part 14. Passing the Paternité Test

We thank Hervé Maisonneuve for having signaled this Guide in his blog.

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