Reading Between the Lines, and Learning from an Epidemiologist
Early on in Between the Lines, a breezy new book on medical statistics by Dr. Marya Zilberberg, the author encourages her readers to “write, underline, highlight, dog-ear and leave sticky notes.” I did just that. Well, with one exception; I didn’t use a highlighter. That’s partially due to my fear of chemicals, but mainly because we had none in my home.
I enjoyed reading this book, perhaps more than I’d anticipated. Maybe that’s because I find the subject of analyzing quantitative data, in itself, dull. But this proves an easy read: it’s short and not boring. The author avoids minutia. Although I’m wary of simplified approaches – because as she points out, the devil is often in the details of any study – this tact serves the reader who might otherwise drop off this topic. Her style is informal. The examples she chooses to illustrate points on medical studies are relevant to what you might find in a current journal or newspaper this morning.
Over the past year or two, I have gotten to know Dr. Zilberberg, just a bit, as a blogging colleague and on-line associate. This book gave me the chance to understand her perspective. Now, I can better “see” where she’s coming from.
There’s a lot anyone with an early high school math background, or a much higher level of education, might take away from this work. For doctors who’ve attended four-year med schools and, of course, know their stats well (I’m joking, TBC*), this book provides an eminently readable review of basic concepts – sensitivity, specificity, types of evidence, types of trials, Type II errors, etc. For those, perhaps pharmacy student, journalists and others, looking for an accessible source of information on terms like “accuracy” or HTE (heterogeneous treatment effect), Between the Lines will fill you in.
The work reads as a skinny, statistical guidebook with commentary. It includes a few handy tables – on false positives and false negatives (Chapter 3), types of medical studies (Chapter 14), and relative risk (Chapter 19). There’s considered discussion of bias, sources of bias, hypothesis testing and observational studies. In the third chapter the author uses lung cancer screening scenarios to effectively explain terms like accuracy, sensitivity and specificity in diagnostic testing, and the concept of positive predictive value.
Though short, this is a thoughtful, non-trivial work with insights. In a segment on hierarchies of evidence, for example, the author admits “affection for observational data.” This runs counter to some epidemiologists’ views. But Zilberberg defends it, based on the value of observational data in describing some disease frequencies, exposures, and long-term studies of populations. In the same chapter, she emphasizes knowing – and stating – the limits of knowledge (p. 37): “…I do think we (and the press) do a disservice to patients, and to ourselves, and to the science if we are not upfront about just how uncertain much of what we think we know is…”
Mammography is, not surprisingly, one of few areas about which I’d take issue with some of the author’s statements. For purposes of this post and mini-review, I’ll leave it at that, because I think this is a helpful book overall and in many particulars.
Dr. Zilberberg cites a range of other sources on statistics, medical studies and epistemology. One of my favorite quotes appears early on, from the author directly. She considers the current, “layered” system of disseminating medical information through translators, who would be mainly physicians, to patients, and journalists, to the public. She writes: “I believe that every educated person must at the very least understand how these interpreters of medical knowledge examine, or should examine, it to arrive at the conclusions.”
This book sets the stage for richer, future discussions of clinical trials, cancer screening, evidence-based medicine, informed consent and more. It’s a contribution that can help move these dialogues forward. I look ahead to a continued, lasting and valuable conversation.
*TBC = to be clear
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