Cancer hates mornings too?
Via the pharmaceutical chemist Derek Lowe, and also various media outlets, there is a new cancer study that randomised patients with lung cancer to get their immunotherapy infusions in the morning or the afternoon/evening. The motivation will have been the various not-very-convincing correlational studies where patients getting morning treatment did better on average. In those studies the differences seen were large, but the studies were small enough that only large differences could have been seen.
The new study also saw a massive difference between morning and afternoon treatment, with the estimated rate of survival without disease progression being 60% lower in the morning group. That difference was 5.5 standard errors away from zero — almost physics levels of statistical surprise.
So, what do we check?
First: dropout. Maybe the healthy patients in the afternoon or the sick patients in the morning dropped out? No, according to the research paper everyone who was randomised was included in the final analysis.
Second: did they report what they said they would report? Up to a point, yes. The clinical trial registry says they started out with overall survival and response rate (tumour shrinkage) as their measurements of success. They changed to progression-free survival as their headline measurement after the trial had been running for a while, which is potentially dodgy. On the other hand, they did report overall survival, and the results are almost as good as progression-free survival. They also reported response rate, which had unimpressive favorable results, but which is a much less important measurement. If things had gone the other way, with good response and bad survival data I would have believed the survival data.
We should now consider whether the results make sense. This is immunology — as Ed Yong described it for the Atlantic, “where intuition goes to die”. Looking at the experts (Derek Lowe and the people quoted in the news stories) it seems they don’t completely believe it, but they are also unwilling to entirely disbelieve it. The drug hangs around in the body for weeks, making a time-of-day effect surprising, but who knows? The result agrees with past correlational research, but that past research is not very convincing. The worst that the experts quoted by Stat (a medical news site) were willing to say is that only half the eligible patients were randomised, which might mean problems in generalising the results. Fortunately, this trial will be relatively easy to replicate, directly in lung cancer, or in the range of other conditions such as melanoma or head and neck cancer where this specific antibody is used, or in the wider world of immune checkpoint inhibitors.
The possibility that’s not mentioned by any of the news stories is fraud: either faking data or faking the tidiness of the randomisation and completeness of the data. Fraud happens; it’s a definite possibility. On the other hand, this doesn’t look like an especially attractive place to try it. Other researchers are bound to redo the experiment, and look into the details, and Big Pharma hasn’t worked out how to manufacture more than one morning per day.
I expect these results to fail to replicate, but I wouldn’t bet large amounts of money on it.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »
I guess their could be some interaction with night-time/day-time chemicals in the blood like melatonin/serotonin.
There’s also less air pollution in the morning – maybe less stress on the lungs.
2 months ago
There’s no problem with there being some difference. What’s weird is the *size* of the difference, which is maybe 3-4 times bigger than I would have thought possible, and ten times bigger than I would have thought likely.
2 months ago
Yeah — this is a massive effect size and would likely completely change how therapy is given should it survive rigorous replication. The effect size approaches what would be a viable treatment, itself, and actually probably increases costs of treatment.
But the real thing is that, if it is real, there has to be a mechanism and figuring that out is likely an important clue in how to just improve the medications, themselves.
But this really is utterly unintuitive to me.
2 months ago
I think it’s utterly unintuitive to everyone, and the division is between “and so it’s wrong” and “but no-one understands this so we might be wrong”.
2 months ago
There are structural reasons…
The more unwell patients could come in later in the day because it takes more time and effort to get to the clinic and it also takes them more time to get through preparatory examinations or to get treatment e.g. find a vein.
If it’s a mix of hospital and home patients, the hospital patients are likely to be later because rounds are generally in the morning.
Younger patients (presumably fitter) might want to come in earlier because they don’t want to split their work day.
1 month ago
The patients don’t get to choose! The comparison is in a group of patients who were willing to get either morning or afternoon treatment, who were randomly allocated to morning or afternoon, and who were compared based on their randomised group.
If it wasn’t randomised no-one would be taking this at all seriously. Patient preference for morning vs afternoon will affect who they can recruit and so can definitely affect generalisability, but it can’t cause this sort of difference in the randomised comparison.
1 month ago
Yeah, it is the randomization that gets me. Even if it isn’t fully generalizable, absent some sort of fraud or randomization failure, it is hard to imagine an easy explanation. Now N=210 so it’s not an absurd sample size but a very odd data distribution. It’s odd enough that I wouldn’t be shocked if a statistician somewhere didn’t ask for the data set to play with how sensitive the analysis is to different parameterizations/assumptions.
1 month ago
As Dorothy Bishop pointed out on Bluesky, the data “sharing” statement is very negative.
1 month ago