Before I started working for a cancer center, I was not aware of the tension between science and medicine. Popular perception is that the two go together hand and glove, but that’s not always true.
Physicians are trained to use their subjective judgment and to be decisive. And for good reason: making a fairly good decision quickly is often better than making the best decision eventually. But scientists must be tentative, withhold judgment, and follow protocols.
Sometimes physician-scientists can reconcile their two roles, but sometimes they have to choose to wear one hat or the other at different times.
The physician-scientist tension is just one facet of the constant tension between treating each patient effectively and learning how to treat future patients more effectively. Sometimes the interests of current patients and future patients coincide completely, but not always.
This ethical tension is part of what makes biostatistics a separate field of statistics. In manufacturing, for example, you don’t need to balance the interests of current light bulbs and future light bulbs. If you need to destroy 1,000 light bulbs to find out how to make better bulbs in the future, no big deal. But different rules apply when experimenting on people. Clinical trials will often use statistical designs that sacrifice some statistical power in order to protect the people participating in the trial. Ethical constraints make biostatistics interesting.
2 thoughts on “Science versus medicine”
Every few years some news articles and op eds crop up explaining Phase I trials and questioning their ethical foundation, often producing minor controversy. I think this is good — the ethics are complicated and our general cultural attitude towards the ethics of using human participants has recently changed and therefore probably will change again. At least one prominent physician-scientist I know of thinks Phase I trials are completely unethical and compares them literally with Nazi medical experimentation. Some of the ethical considerations involved in Phase I trials are identical IMO with ethical considerations regarding “alternative” medicine, and even patient refusal of treatment.
I think your point about statistical power is very important and deserves amplification. I’m no expert in experimental design, but I think it is pretty clear that efficient designs from a statistical standpoint often involve extreme conditions. For example, if you want to experimentally determine the relationship between altitude and air pressure, but could only make a limited number of observations, you will almost certainly want to sample near the ground and at the highest spot you could reach, to start with. The best elevations for additional observations depend on your assumptions, but if you assume a linear relationship and just want to determine the slope, I think it would be best to put half of your observations on the ground and half at the highest spot available.
But for example when testing how much of a drug people can tolerate, one of the analagous extreme conditions would be giving someone a dose which he is very unlikely to tolerate. Clearly, if you could do an experiment which is almost as good but only involves doses which are much more likely to be tolerated, this would be a more ethical choice, even if it is not as good from a statistical standpoint.
Anyway, you sure said a mouthful when you said that ethical constraints make biostatistics interesting! Especially since the ethical considerations often involve extremely deep questions.
Well said JC!
There is a constant pressure to provide statistical designs that make best use of limited money, time, and patient recruitment resources. Balancing the needs of the researcher with the well being of the patients is a constant challenge. Ten years ago when I started my job I had a lot of people coming to me with their data and asking (in so many words) “What do I do now?” Though many of these were student and resident researchers, this is no longer the case. IRB’s have become much stricter about requiring sound statistical plans even for student research, and journals are much tougher in their statistical reviews. The result is better use of resources, better use of the available patients for study, and better science leading to improved patient care.
@ J. Venier: You are absolutely correct that the most efficient design for a linear relationship is to sample at the two farthest extremes, but this design cannot detect any non-linear relationships. If you don’t know what so relationship to expect, the you may need to sample in between as well.