Archive for February, 2008

Rethinking interruptions

Monday, February 4th, 2008

If you read a few personal productivity articles you’ll run into this advice: Interruptions are bad, so eliminate interruptions. That’s OK as far as it goes. But are interruptions necessarily bad? And when you are interrupted, what can you do to recover faster?

Not all interruptions are created equal. Paul Graham talks about this in his essay Holding a Program in One’s Head.

The danger of a distraction depends not on how long it is, but on how much it scrambles your brain. A programmer can leave the office and go and get a sandwich without losing the code in his head. But the wrong kind of interruption can wipe your brain in 30 seconds.

In her interview with John Udell, Mary Czerwinski points out that while interruptions are detrimental to the productivity of the person being interrupted, maybe 75% of the time interruptions are beneficial for the organization as a whole. If one person is stuck and other person can get them unstuck by answering a question, the productivity of the person asking the question may go up more than the productivity of the person being asked the question goes down.

Given that interruptions are good, or at least inevitable, how can you manage them? Czerwinski uses the phrase context reacquisition to describe getting back to your previous state of mind following an interruption. Czerwinski and others at Microsoft Research are looking at software for context acquisition. For example, one of the ideas they are trying out is software that takes snapshots of your desktop. If you could see what your desktop looked like before the phone rang, it could help you get back into the frame of mind you had before you started helping the person on the other end of the line.

Have you discovered a tool or habit that helps with context reacquisition? If so, please leave a comment.

Task switching

Saturday, February 2nd, 2008

If you’re working on three projects, you’re probably spending 40% of your time task switching. Task switching is the dark matter of life: there’s a lot of it, but we’re hardly aware of it.

I’m not talking about multitasking, such as replying to email while you’re on the phone. People are starting to realize that multitasking isn’t as productive as it seems. I’m talking about having multiple assignments at work.

John Maeda posted a note about multiple projects in which he gives a link to a PowerPoint slide graphing percentage of productive time as a function the number of concurrent assignments. According to the graph, the optimal number of projects is two. With two projects, you can do something else when one project is stuck waiting for input or when you need variety. But any more than that and productivity tanks.

Johanna Rothman has an interview on the Pragmatic Programmer podcast where she discusses, among other things, having multiple concurrent projects. She thought it was absurd when she was asked to work 50% on one project, 30% on another, and 20% on another. Research environments are worse. Because of grant funding, people are sometimes allocated 37% to this project, 5% to that project, etc.

We’re not nearly as good at task switching as we think we are. I hear people talking about how it may take 15 or 30 minutes to get back into the flow after an interruption. Maybe that’s true if you were interrupted from something simple. A colleague who works on complex statistical problems says it takes her about two or three days to recover from switching projects. In his article Good and Bad Procrastination, Paul Graham says “You probably only have to interrupt someone a couple times a day before they’re unable to work on hard problems at all.”

Population drift

Friday, February 1st, 2008

The goal of a clinical trial is to determine what treatment will be most effective in a given population. What if the population changes while you’re conducting your trial? Say you’re treating patients with Drug X and Drug Y, and initially more patients were responding to X, but later more responded to Y. Maybe you’re just seeing random fluctuation, but maybe things really are changing and the rug is being pulled out from under your feet.

Advances in disease detection could cause a trial to enroll more patients with early stage disease as the trial proceeds. Changes in the standard of care could also make a difference. Patients often enroll in a clinical trial because standard treatments have been ineffective. If the standard of care changes during a trial, the early patients might be resistant to one therapy while later patients are resistant to another therapy. Often population drift is slow compared to the duration of a trial and doesn’t affect your conclusions, but that is not always the case.

My interest in population drift comes from adaptive randomization. In an adaptive randomized trial, the probability of assigning patients to a treatment goes up as evidence accumulates in favor of that treatment. The goal of such a trial design is to assign more patients to the more effective treatments. But what if patient response changes over time? Could your efforts to assign the better treatments more often backfire? A trial could assign more patients to what was the better treatment rather than what is now the better treatment.

On average, adaptively randomized trials do treat more patients effectively than do equally randomized trials. The report Power and bias in adaptive randomized clinical trials shows this is the case in a wide variety of circumstances, but it assumes constant response rates, i.e. it does not address population drift.

I did some simulations to see whether adaptive randomization could do more harm than good. I looked at more extreme population drift than one is likely to see in practice in order to exaggerate any negative effect. I looked at gradual changes and sudden changes. In all my simulations, the adaptive randomization design treated more patients effectively on average than the comparable equal randomization design. I wrote up my results in The Effect of Population Drift on Adaptively Randomized Trials.