PMean » Randomization

Recommended: An overview of randomization techniques

pmean — Wed, 06 Feb 2019 04:05:48 +0000

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Recommended: Randomized Controlled Trials in Health Insurance Systems

pmean — Thu, 21 Sep 2017 00:41:52 +0000

While researchers often use data from health insurance systems to conduct observational studies, the authors of this research paper point out that you can also conduct randomized trials as well. You can randomly assign different levels of insurance coverage and then get claims data to evaluate how much difference there is, if any, in the levels of coverage. This approach is attractive because you do not need a lot of resources, and you can very quickly get a very large sample size. Since insurance data is collected for administrative needs rather than research needs, you have to contend with inaccurate or incomplete data, potentially causing loss of statistical efficiency or producing biased results. The authors offer some interesting examples of actual studies, propose new potential studies, and offer general guidance on how to conduct a randomized trial from health insurance systems.

Choudhry NK. Randomized, Controlled Trials in Health Insurance Systems. N Engl J Med 2017 (Sept. 7); 377: 957-964. Available at http://www.nejm.org/doi/full/10.1056/NEJMra1510058.

Recommended: The Coin Flip: A Fundamentally Unfair Proposition

pmean — Mon, 25 Jan 2016 20:01:14 +0000

A flip of a coin does not result in an exact 50-50 chance of heads or tails. It depends a lot on how the coin is flipped, of course, but there is a bias. This article explains when, why, and how much bias there is.

Doug Steigerwald. The Coin Flip: A Fundamentally Unfair Proposition. Available at http://econ.ucsb.edu/~doug/240a/Coin%20Flip.htm.

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Recommended: The Empirical Evidence of Bias in Trials Measuring Treatment Differences

pmean — Mon, 25 Jan 2016 18:29:12 +0000

When I wrote a book about Evidence Based Medicine back in 2006, I talked about empirical evidence to support the use of certain research methodologies like blinding and allocation concealment. Since that time, many more studies have appeared, more than you or I could easily keep track of. Thankfully, the folks at the Agency for Healthcare Research and Quality commissioned a report to look at studies that empirically evaluate the bias reduction of several popular approaches used in randomized trials. These include

selection bias through randomization (sequence generation and allocation concealment); confounding through design or analysis; performance bias through fidelity to the protocol, avoidance of unintended interventions, patient or caregiver blinding and clinician or provider blinding; detection bias through outcome assessor and data analyst blinding and appropriate statistical methods; detection/performance bias through double blinding; attrition bias through intention-to-treat analysis or other approaches to accounting for dropouts; and reporting bias through complete reporting of all prespecified outcomes.

The general finding was that failure to use these bias reduction approaches tended to exaggerate treatment effects, but the magnitude and precision of these exaggerated effects was inconsistent.

Nancy D Berkman, P Lina Santaguida, Meera Viswanathan, and Sally C Morton. The Empirical Evidence of Bias in Trials Measuring Treatment Differences. Available at http://www.ncbi.nlm.nih.gov/books/NBK253181/.

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Recommended: Clinical trials: discerning hype from substance

pmean — Thu, 29 Oct 2015 15:40:32 +0000

I was attending a short course taught by Susanne May on clinical trials (very good, by the way) and she mentioned an interesting publication. It talks about the “random high” bias.

Fleming TR. Clinical trials: discerning hype from substance. Ann Intern Med. 2010 Sep 21;153(6):400-6. doi: 10.7326/0003-4819-153-6-201009210-00008.

Recommended: In search of justification for the unpredictability paradox

pmean — Thu, 11 Dec 2014 17:02:21 +0000

This is a commentary on a 2011 Cochrane Review that found substantial differences between studies that were adequately randomized and those that were not adequately randomized. The direction of the difference was not predictable, however, meaning that there was not a consistent bias on average towards overstating the treatment effect or a consistent bias on average towards understating the treatment effect. This leads the authors of the Cochrane review to conclude that “the unpredictability of random allocation is the best protection against the unpredictability of the extent to which non-randomised studies may be biased.” The authors of the commentary provide a critique of this conclusion on several grounds.

Howick J, Mebius A. In search of justification for the unpredictability paradox. Trials. 2014;15(1):480. doi:10.1186/1745-6215-15-480. Available at http://www.trialsjournal.com/content/15/1/480/abstract.

Recommended: The random risks of randomised trials

pmean — Mon, 28 Apr 2014 12:30:25 +0000

This is an overview of some of the ethical controversies associated with randomization. It includes an interesting story of an early trial by Archie Cochrane that raised a lot of fuss at the time because it was attacking one of the “sacred cows” of medicine.

Harford T. The random risks of randomised trials. Financial Times. http://www.ft.com/intl/cms/s/2/59bb202c-ca7b-11e3-8a31-00144feabdc0.html#axzz30BOFBYb7. Accessed April 28, 2014.

Recommended: Large randomized controlled trials are ready for retirement

pmean — Fri, 17 Jan 2014 20:09:08 +0000

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