When Heping Zhang first began his appointment at the Yale School of Public Health, he did not even know what “epidemiology,” a concept at the heart of the field of public health, meant. “It was not trivial to even learn how to spell it,” he recalled.
Yet, as Susan Dwight Bliss Professor of Biostatistics, professor in the Child Study Center, and professor of Statistics and Data Science, he has applied his mastery of statistics and methodology toward understanding and solving complex problems in public health, and has made discoveries in the field of infertility that have led to five publications in JAMA and NEJM in the past several years.
“I am a statistician, not a clinician,” said Zhang, who earned his Ph.D. in statistics from Stanford University before coming to Yale in 1992, where he began by researching the epidemiology and genetics of Tourette’s Syndrome and Obsessive Compulsive Disorder in collaboration with colleagues at the Child Study Center. “Statisticians don’t deal with one particular disease, but develop general methods and software that we hope have broader implications. Cancer, genomics, mental health – anything where you are trying to understand the relationship between one thing and another.”
For Zhang, the secret of his success is just that: an understanding and fostering of relationships between, as he said, one thing and another. These relationships, or collaborations, between peers in science, staff members, research centers, nations, and multidisciplinary approaches are key to his research.
Good collaborations, for Zhang, start in his own office. Zhang heads the Collaborative Center for Statistics in Science, also known as C2S2, which was founded through a grant from the NIH that Zhang received in 2005 to study genomics and proteomics in preterm birth, and expanded from there. C2S2 is part of the NIH’s Reproductive Medicine Network, a collaboration between six clinical sites that collaborate on multicenter trials in infertility. There, Zhang is at the helm of six staff members and a host of graduate students that assist in the work.
At C2S2, he works alongside clinicians to develop clinical trials that test important questions that aim to understand infertility, and develop novel treatments for it. To develop these questions for testing, as a statistician, “We engage in serious discussions but I always defer the medical decision to clinicians,” he said. “That’s where the hypotheses for this work come from. I try to understand the questions they want to answer, and then we design the study to collect data to get as clear of an answer to the question that you can. That’s why we work as a team.”
Part of working on a successful team made up of people of complementary expertise, Zhang said, is knowing when to work together, and knowing when to let people get on with work on their own. An important part of collaboration is recognizing your own role, and giving collaborators the freedom to do their own how they see fit. “Here, when you have certain tasks, you are given the freedom and the trust to do what you are supposed to. And if someone tries to interfere with your work, whether here or elsewhere, I’m going to stand up for you.”
Once he and his clinician colleagues establish the question they aim to answer, Zhang works with them to develop the entire spectrum of the clinical trial, as it relates to data. He utilizes his statistical perspective to design the study, develop protocols, develop and manage databases, collect, analyze, and interpret data. All of this work is done to maximize the impact of the available data. Because his work designs and applies statistical methods to disease-specific clinical trials, Zhang’s work stands in each of the distinct worlds of basic and translational science.
“What we do with methods is the domain of basic science,” Zhang said. Applying these basic science methods, which can be abstract, to specific situations shifts the work into the domain of translational research. “Without the method, then you can’t deal with a specific situation. But when you see enough specific situations, you abstract into a more general setting, and develop a new approach.” According to Zhang, there are very few scientists in the field who work on both sides of the research bench, translational and basic science, as he does. “You get to see the entire picture, so you start appreciating the challenges and the rewards. If you only see this through the methods, you don’t see how difficult it is to design the study and collect the data.”
Statisticians don’t deal with one particular disease, but develop general methods and software that we hope have broader implications. Cancer, genomics, mental health – anything where you are trying to understand the relationship between one thing and another.
Heping Zhang
Zhang has helmed five multicenter trials with a significant impact on the field of infertility, three of which conducted in China, with a team of both Chinese and American researchers. In a paper published in The New England Journal of Medicine (NEJM), Zhang and the team found, looking at more than 2,100 women at fertility centers in China, that frozen embryos yielded a higher live-birth rate than fresh embryos among women with polycystic ovary syndrome. Another related study also published in NEJM found similar live birth rates between fresh and frozen embryo transfers in couples with unexplained infertility. With 1,500 women who underwent fresh- or frozen-embryo transfer, a lower risk of ovarian hyper-stimulation syndrome, and a higher risk of preeclampsia were found. Another study conducted in China, and published in JAMA, found that an acupuncture procedure, contrary to what was previously thought, does not have a positive effect on fertility, either with or without treatment with clomiphene, a common drug also known as Clomid, that is used to stimulate ovulation.
The collaboration with Chinese researchers and Chinese fertility centers has been an important one for Zhang, who grew up in rural China, and still has many connections there. Each nation has its strengths and weaknesses in conducting clinical research, he said, but together, they can produce strong results. In China, infertility trials are much more readily funded than in the US, and can be accomplished much more efficiently. Yet in the US, there is deep, longstanding expertise in the field of research, but, because of restrictive privacy laws and population size, there are many fewer potential volunteers for clinical trials than in China. “In the US, we have expertise, but we don’t have enough people. In China, they don’t have enough experience, but they have the patient population. So that’s why it’s ideal: they complement each other.” Zhang also said that while he works with Chinese research centers, he and his senior colleagues work on a daily basis to train a new generation of Chinese researchers. “That’s been a wonderful experience,” he said.
Stateside, Zhang is known for two studies, both published in NEJM, that challenged the primacy of clomiphene, or Clomid, an estrogen-receptor modulator, as a first-line infertility treatment for women. Both studies revealed the potential power of aromatase inhibitors such as letrozole as a treatment for infertility. One study showed that letrozole, which is sold under the brand name Femara, among others, could be effective at increasing live-birth and ovulation rates among women with polycystic ovarian syndrome, and second indicated that letrozole could reduce multiple gestation rates while maintaining live birth rates among women with unexplained infertility.
Zhang has always considered himself an autodidact, something he sees as critical to taking on collaborative work. “In my own experience, to be really successful as a contributor to multidisciplinary research, you have to be very prepared too.” While still a doctoral student at Stanford, “I wrote several single-author or two-author papers,” Zhang said. “I wrote thousands of lines of code. I did everything myself.” In such a way, he is able to contribute significantly to public health research without a formal background in the field. “When you get to the real world, you have to be able to learn by yourself,” Zhang said. “You have to adapt and learn from the work if you are going to be able to excel.”
Looking ahead, Zhang is turning his expertise and methodological point of view toward one of the most enigmatic mysteries in medicine: understanding the epigenetics of brain function. And outside of career in research, Zhang is also a committed autodidact.
“Education is important, but when you get to a certain stage, I more enjoy figuring things out by myself,” Zhang said. He has taught himself tennis, Chinese calligraphy, and is determined to learn to sing via YouTube. “I’m awful,” he acknowledges. But “the process is more important than the outcome, sometimes. For that reason, it probably underlies things I have done, and how I approach things. It may take longer, but to me that’s a different reward.”