Blinding and randomization: the basics

Before I start talking too much about if it's possible to effectively unblind a study without knowing treatment codes, it will be helpful to establish why blinding is important. (Also called masking.) In a nutshell, these techniques, when applied correctly, correct our unconscious tendencies to bias results in favor of an experimental treatment.

Randomization is the deliberate introduction of chance into the process of assigning subjects to treatments. This technique not only establishes a statistical foundation for analysis methods used to draw conclusions from the data in the trial, but also corrects the well-known tendency for doctors to assign sicker patients to one treatment group (the experimental treatment if placebo-controlled, or active control if the active control is well-established, e.g.).

Blinding is the keeping secret the treatment assignment from either the patient, the doctor, or both. (Statisticians and other study personnel are kept blinded as well.) Single-blind studies maintain treatment assignment from the subject. Double-blind studies maintain treatment assignment from both the subject and doctor. I have run across a case before where the doctor was blinded to treatment assignment but not the subject, but those are rare.

For some examples of kinds of bias handled by these techniques, see here.

If a particular patient experiences problems with treatment in such a way that the treatment assignment has to be known, we have ways of exposing just the treatment assignment of one patient without having to expose everybody's treatment assignment. If all goes well, this is a relatively rare event. That's a big "if."

At the end of the study, ideally we produce the statistical analysis with dummy randomization codes in order to get a "shell" of what the statistical analysis will look like. This analysis is conducted according to some prespecified plan that is documented in the study protocol and a statistical analysis plan. In many cases, we will draw up in Microsoft Word or other editing software a shell of what the tables will look like. (I've heard about some efforts at using OASIS table model for both shells and analysis.) When we are satisfied with the results, we drop in the true randomization codes (seen for the first time) and hope nothing strange happens. (Usually, very little goes awry unless there was a problem in specifying the data structure of the true randomization codes.)

Any analysis that occurs afterward might be used to generate hypotheses, but isn't used to support an efficacy or safety claim. If something interesting does come up, it has to be confirmed in a later study.

Ideally.

What happens when things aren't so ideal? Stay tuned.

Ban abbrs!

So, maybe this is a little bit radical, but I think we should stop using abbreviations. We have the technology to automatically expand abbreviations in writing, so that advantage is pretty much lost. For anyone who is a nonlinear reader and writer, the rules create a huge waste of time -- you have to track the first use of an abbreviation in order to expand it and put it in a table of abbreviations. For a nonlinear writer, who might write the introduction after the body, this can get cumbersome. For a nonlinear reader, such as someone who uses SQ3R or similar method, it creates an irritation to have to thumb back to a table of abbreviations (and another huge waste of time).

So, how about it, style guide writers? Time to move grammar out of the IBM Selectric age?

Can the blind really see?

That's Sen. Grassley's concern, stated here. (A thorough and well-done blog with some eye candy, though I don't agree with a lot of opinions expressed there.)

I've wondered about this question even before the ENHANCE trial came to light, but, since I'm procrastinating on getting out a deliverable (at 11:30pm!) I'm going to just say that I plan to write about this soon.

Critical thinking about vaccines

I encourage people to think critically about vaccines (just like any other topic). However, pseudoskepticism about vaccines (just like any other topic) is harmful because discouraging others from vaccinating leads to a rise in, for example, whooping cough. Orac has also written about the rise in measles in the wake of decreasing vaccination.

Remember, the decisions you make about vaccination affect others as well. Some critical thinking about vaccines is good -- for example knowing when to go ahead or delay a shot due to illness (or knowing what conditions may lead to a life-changing reaction). However, a blind rejection is as bad as blind acceptance.

Well, why not?

Since I'm posting, I might as well point toward Derek Lowe's post about the failure of the Singulair/Claritin idea. Too bad for Merck, though one has to wonder how long this drug combination strategy among big pharma is going to play out. After all, wouldn't it be about as cheap to take two pills (since one is over-the-counter) as it would be to ask your insurance to fork it over for a prescription version of a combination? Heck, a lot of people take the combination separately now, anyway.

So at any rate, Derek deduces that the problem lies in efficacy. Is it possible to support a marketing claim that the combination is more than the sum of its parts? Merck apparently thinks so, but the FDA does not. Unless there's an advisory committee meeting on this, or the drug eventually gets approved, or efforts to get results of all clinical trials posted publically succeed, we won't know for sure. But what I do know is that for one of these combinations to gain marketing approval, at the very least there has to be a statistically significant synergistic effect. That means that the treatment effect has to be greater than the sum of the treatment effects of the drugs alone. Studies that demonstrate this effect tend to have a lot of patients, especially if there are multiple dose levels involved. It isn't easy, and I've known more than one combination development program to fizzle out.

Update: but see this serious safety concern for Singulair reported by Pharmalot.

It's easy to make silly claims when you take numbers out of contexts

I often respect Mark Schauss, but when he shows his hatred of the pharmaceutical and healthcare industries his logic tends to go out the window.

Take for example his latest silly claim "stay out of hospitals to live longer." Ok, I guess one could make the argument that behaviors or genetic predispositions that lead one to a hospital stay would probably tend to shorten life. Fair enough. But rather than taking that fairly obvious argument, we are treated to a naked number: 99,000 deaths from nosocomial (hospital-related) infections per year. Rather than delve into that number, Mark simply calls it "unacceptable."

Granted, we all want to reduce that number. But let's take a closer look by reviewing the report on which Mark bases his post. (Link is a pdf.)

The infection rate per 1,000 patient-days was highest in ICUs (13.0), followed by high-risk nurseries (6.9), and well-baby nurseries (2.6).

Now, let's think about the claim that people are better off out of the hospital than in the hospital. The highest infection rates are in ICUs and high risk nurseries. Well-baby nurseries registered as well. Sounds to me like if someone needs to be in one of these places, they have some pretty serious problems, and infections considered, in the hospital is better than outside the hospital. I doubt that bolting from the ICU to avoid infection is going to, in the long run, lead to a longer life.

99,000 is a number we all want to go down to zero, and I suspect that more judicious use of antibiotics, solving the problems with overtired and overworked healthcare practitioners, and avoiding drug dispensing and therapeutic errors will all be part of the solution. But before we go making any silly conclusions based on this number, let's see what the problems really are and solve them rather than cut off our noses to spite our faces.

This is why I keep my internal emails clean

Every once in a while I will express frustration about a client in an email, but I don't use profanity in emails and chats (these things are logged, too). Why?

Well, while my emails are unlikely to get plastered all over the interwebs, I do realize they can reach a wider audience than their intended audience. Don't forget, the things are discoverable, too. That means the lawyers can get 'em.

Getting the hands dirty

I don't very often write the SAS code to produce tables and listings anymore. There are other people who do it a lot better than I, and I do a lot of other things better than write SAS code. But I've had the occasion to do it recently, and, I have to admit, it's a reminder of why I didn't go down the SAS programmer path (and a reminder of the fact that things really do take a long time to produce, at least correctly).

The buzz

Every once in a while I look through the keyword referrals and do an informal assessment of how people find this blog -- one that's geared for a rather narrow audience. Here are the most popular keywords:

• O'Brien-Fleming (especially doing this kind of design in SAS)
• Bayesian statistics in R
• noninferiority
• NNT (number needed to treat)
• confidence intervals in SAS

On occasion I will get hits from more clinical or scientific searches, such as TGN1412 (the monoclonal antibody that caused a cytokine storm in healthy volunteers, leading to gangrene, multiple organ failure, and for those lucky/unlucky enough to survive, cancer), Avandia, or CETP inhibitors.

At 2000 hits in a year, this is clearly a narrowly-targeted blog. :D

When you consult, be sure you know what your labels mean

In a recent project, I fit a varying slopes and intercepts model to data that was organized into replicated experiments, with several replications at each timepoint for several timepoints. Turns out, the only relationship between units labeled "Run 1" was the label. Nothing else. In this case, it turned out a simple linear regression was the more appropriate model.