Many of my posts and efforts actively work to get more diversity, particularly women and girls, into STEM (Science, Technology, Engineering, and Mathematics). I’d like to take a moment in this post, however, to comment on a recent article in Scientific American that I find not only appalling as a scientist, but as a human being.
A senior research scientist writing at Scientific American chose to tackle, rather adamantly oppose, the inclusion of females in NIH studies. To date, many experiments use males exclusively in research on drug efficacy, and this author proclaims that the inclusion on females is, for “statistical reasons” (a dubious point I will address later) too “expensive” and a “waste of resources” to conduct. You can read more here: http://www.scientificamerican.com/article/testing-males-and-females-in-every-medical-experiment-is-a-bad-idea/?WT.mc_id=SA_Facebook
Let’s start with some basic science and non-threatening niceties (That’s what I’m supposed to be doing, right? Sugar and spice…). Women are the majority of humans on this Earth, and although the author ignores this point (and perhaps he should, as majority and minority need recognition), I do agree with the opening sentences of his article: “Sex differences lie at the core of biology. They are the driving force of evolution, and in many cases they are fundamental in health and medicine. The study of sex differences is important work, and more of it should be done.”
However, the basis of his article advocates that broadly incorporating the use of males and females in health studies “costs money and requires a duplication of time and effort”. Besides the ethical concerns I have about someone who is reluctant to represent equality due to concerns of effort and money, I’m going to take apart his argument to display not only the apparent faulty scientific reasoning, but also the weak political agenda he attempts to support.
If these are drugs that could have effects in one sex and not another, by not testing both sexes, you *miss* benefits (or potential serious risks) that occur in one sex which will receive the drug. When you treat women– as has been done historically– as small men, you ignore the genetic and hormonal differences that can change drug function. For example, in mice, the typical trial animal, progesterone expression modulates inhibitory effects on dendritic cells, and ovarian hormones modulate both the brain and behavior in response to cannabis. Just earlier this year, it was discovered that male and female *humans* react differently to Ambien. The most popular sleep drug on the market was found to be metabolized differently between males and females WHILE ALREADY OUT IN THE MARKET— FOR *20* years. Effectively, because women metabolize Ambien more slowly, the drug remained in their system longer and contributed to about twice the dosage they actually needed (aka, they were overdosing on a sleep drug by doctor’s orders), and the side effects of impaired driving were so large in women that it prompted FDA investigation. When the FDA pulled the original trial actually conducted *gasp* on both sexes, the original discovery of a 45% (!!) higher level of the drug found in females was “rationalized away” (read more about it and other drugs in detail here). Ambien, unfortunately, is currently the only prescription drug in the country with a different suggested dose for men and women, but it’s far from the only drug with sex-specific effects. Likewise, this not just true for drug trials. A recent study showed just 3% of trials to consider surgical effects in both sexes, leaving a whopping 80% to study only the effects in males.
The next issue I have is purely a numbers one. Specifically, I am a girl who can do math, and his faulty statistics just don’t add up. Although the author attempts to use statistics to support his argument, there is an ignorance that I can’t get behind: basically, he asserts that adding females makes the variance so large that the combined, larger variance of having males and females together will wash out the drugs effectiveness for males and females. If the drug is intended to be used on both sexes blanketly, if it is not shown to have an affect across both sexes, if there is an effect per sex, it will show up when you have a model that includes sex as a factor; you accept in the model that the underlying variances are different between the males and females in both control and experimental groups, and that variances are smaller within groups than between groups. For example, the general height of males will vary more closely around the mean height of males (i.e. give a smaller variance) rather than the mean height of males AND females. If you include sex as a factor and choose the correct statistical model which compares within and between sexes (multiple comparisons), you can disentangle these effects AND find whether the drug overall is effective. Yes, the sample sizes (N) will need to increase to account for the degrees of freedom lost by this type of analysis, but not to the earth-shattering, economically-wasteful level the author purports. Here, the factor of treatment being added to sex uses only 2 additional degrees of freedom (when df= N – K – 1), where N = # subjects and K = the number of independent variables. Here, K = 3 in this case because you would add the second factor (sex) as well as the variable for the interaction effect of sex * treatment (i.e., this lets you test if the effects are experienced in different directions by sex, say, if it’s good for males and bad for females, for example). Thus, your statistics can give your more with only slightly more animals in a single study, vs. repeating the study in females (again requiring as large an N as the first study) to test for similar effects. The author’s claim of doubled input simply doesn’t match statistical reality, and his suggestion of later investigating effects in females may actually cost *more* to conduct.
Keep in mind, N values in mice can be kept quite low (often arguably too low) in medical studies regardless, and therefore the power could be called into question whether or not you consider females. Yes, you would need a few more animals (not double the number) to conduct the research if you include both sexes, but if you do your statistics correctly, if no effect or trend of sex is found, it is perfectly valid to report no effect of sex and then combine experimental groups and test for an overall effect. The shoddy work based on low samples sizes is atrocious as-is, so a better understanding of statistics would not only bolster investing in better, more inclusive science, but also boost the validity of findings regardless. Being cheap about science undermines what we can discover, and the power of what our discoveries broadly mean.
If both females and males are the targets of these drugs, specifically avoiding potential benefits or risks to one sex is costly in itself– it ignores that over half of the population exists and has different health outcomes under certain drug regimes. Aside from the implications of administering drugs for which the effects in the majority of the population are unknown, and the clear errors in statistical thinking, I am also disturbed that this effort by the NIH to address sex-differences in research is pitched as political for 2 reasons:
1. The author hasn’t done their homework. Work to include females in critical trials has been on the agenda of the NSF and NIH as a part of the annual Gender Summit for *years*. Specifically, in Nov. 2013 at the last summit held in DC (of which Janine Clayton, mentioned in the article is *listed as an Organizing Convener*), specific sessions included the application, or lack thereof, of gender in studies within STEM and STEM education. Specifically, an invited panel included experts in public health (Sabra Klein), education and policy (Brian Nosek) calling for representation in studies and with years of DATA to support that the inclusion of males and females has positive benefits within both research and policy. So, no, this didn’t start this January of this year, and it’s *data-driven*, not political (although the repeated lowercase use of nih vs. NIH in the article, is).
2. The author, ostensibly a scientist who uses data to make decisions, could make an argument with economics as the driving force behind his objection, but he doesn’t. He also could argue factors like how males and females are housed, among other things, could have confounding effects unless more rigorously controlled; I’d accept that this is a concern, but rigor should be expected in science, and so we’d reach agreement there– if only he made the point. But, again, it is not about rigor, as we already saw in the stats example. He simply says it costs more, while lacking data or even rough estimates based on data on the specific costs and scope to the research, and the projected (by his estimates, lesser) costs to society for not supporting sex-balanced research. In addition, his argument also ignores the cost and societal implications of minimally 2 years delay and non-repeat funding for projects which we would, by his suggestion, later like to investigate the implications of sex. By this same token, knowing that there are species-specific differences in drug responses (part of the reason that so many potential drug fail before reaching the clinical test phase), it is not valid, by his suggestion, to include females only as an afterthought to an experiment conducted on males. The inclusion of females at all levels will help us better understand effects, risks, and applicability of treatments. Furthermore, inclusion from the start, versus an add-in-later attitude will speed the drugs to market that are actually shown to have a positive effect.
Overall, I recognize there may be costs to incorporating females, but including females and males should have been done from the beginning. To say it is costly now to include them is akin to saying the right for women to vote is bad, for it will to clog our ballot boxes, waste paper, and eat up time at the polls. Hiding behind the convenience of male subjects and weak arguments of thriftiness to justify scientifically biased, unethical treatment of others is not ok. I am embarassed by this senior scientist, and I am not, nor am I ever, inclined to support a viewpoint that is not based on current data, shows a poor grasp of the issue and its history, and does not begin to support itself with any type of data. Frankly, it appears lazy, and that’s not what I’ve come to expect from people supposedly qualified to comment on an issue.