Biostatistics

Biostatistics

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Members of the biostatistics faculty lead research that develops novel statistical-methods approaches to meet complex data challenges across many domains. They design and conduct national, multi-center clinical trials and direct clinical-research networks. They contribute as team scientists to biomedical, behavioral, clinical and translational research across many disciplines, in a wide array of studies within the Center for Clinical Epidemiology and Biostatistics, in various schools at Penn, and nationally—interpreting the data and drawing accurate inferences from the findings.

The DBEI brings their work into close proximity and collaboration with cutting-edge research in epidemiology and informatics. We invite you to explore some of their publications here.

Publications

Masking Policies for Covid-19: What Does the Science Say?

February 2022

Masking policies during Covid-19 have inspired plenty of political debate, but scientific evidence about the policies’ effects has been very limited. To measure whether instituting a masking policy reduces case numbers, Jing Huang, PhD, and colleagues emulated a randomized controlled trial, including more than 400 diverse counties.

Jing Huang, Brian T. Fisher, Vicky Tam, Zi Wang, Lihai Song, Jiasheng Shi, Caroline La Rochelle, Xi Wang, Jeffrey S. Morris, Susan E. Coffin, and David M. Rubin

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Turning the Tables on Diseases that Spread Spatially

October 2021

Researchers confront a key, persistent bottleneck as they look to realize the huge translational potential of spatial transcriptomics: They lack the analytical tools to extract knowledge from the information they see about cancer and Alheimer's. Enter the technique that biostatistician Mingyao Li, PhD, has developed.

Jian Hu, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li

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HCC Cells

Informing Decisions in Liver Cancer: Use Biology to Better Characterize the Disease

September 2020

With an estimated five-year survival rate of less than 10 percent, liver cancer is the United States’ fastest growing cause of cancer-related death in men. These dire statistics hail, in part, from the lack of validated biomarkers to characterize the biological features of an individual’s cancer and inform therapeutic decisions. However a novel method, reported recently in the journal Hepatology, stands poised to change that.

 

Jeffrey Morris, Manal M. Hassan, Ye Emma Zohner, Zeya Wang, Lianchun Xiao, Asif Rashid, Abedul Haque, Reham Abdel‐Wahab, Yehia I. Mohamed, Karri L. Ballard, Robert A. Wolff, Bhawana George, Liang Li, Genevera Allen, Michael Weylandt, Donghui Li, Wenyi Wang, Kanwal Raghav, James Yao, Hesham M. Amin, Ahmed Omar Kaseb

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Past, Present and Future of the Pandemic: Social Distancing Is Key

July 2020

A look back at the drivers behind early COVID-19 outbreaks the US attests that social distancing was, and is, a key intervention to reduce disease spread. A CHOP/DBEI team's dynamic model continues to highlight  trends within counties in real time—and has revealed some disturbing new facts.

David Rubin, MD, MSCE; Jing Huang, PhD; Brian T. Fisher, DO, MPH, MSCE; Antonio Gasparrini, PhD, MSc; Vicky Tam, MA; Lihai Song, MS; Xi Wang, PhD; Jason Kaufman, MSt; Kate Fitzpatrick, BS; Arushi Jain, BS; Heather Griffis, PhD, MS; Koby Crammer, PhD; Jeffrey Morris, PhD; Gregory Tasian, MD, MSc, MSCE

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New Bayesian Nonparametric Model Accurately Handles Zero-Inflated Outcomes, Benefiting Researchers Across Many Fields

March 2020

Researchers often seek to predict outcomes, detect distinct subgroups within their data, or estimate causal treatment effects. But in empirical analyses, outcomes with many observed zeros can complicate these tasks. PhD candidate Arman Oganisian recently led development of a method that helps researchers faced with such outcomes.

Authors: Arman Oganisian, Nandita Mitra, Jason Roy

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Combating Biased Estimates in Studies With Unmeasured Confounding

January 2020

Unmeasured confounding almost always exists in observational studies—and it can bias our analysis of the effects we see in patients. These new findings can point researchers in various situations toward the best statistical methods to combat dominant types of unmeasured confounders.

Yun Li, Yoonseok Lee, Friedrich K Port, Bruce M Robinson

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New Method for Mass Spectrometry Proteomics Data Detects Differences Others Miss

October 2019

Given the fundamental role that proteins play in the processes that underlie various diseases, including cancer, it's important to identify relevant proteomic biomarkers—for early detection, new drug target identification and precision medicine strategies. But standard methods fail to detect some useful ones.

Yusha Liu, Meng Li, Jeffrey S. Morris

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Use of “Trend” Statements in Oncology Literature Shows Why We Must Shift Focus to Findings’ Clinical Relevance

December 2018

The importance of a study is often inappropriately defined by the P value, the authors write, and this problem is highlighted by the use of “trend”—a term for which there is no definition—to refer to statistically non-significant results. To make a case in point, they set out to characterize the degree of overreliance on P values in high-impact oncology literature.

Kevin T. Nead,  Mackenzie R. Wehner,  Nandita Mitra

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Quantifying and Comparing Bacterial Growth: Method Charts Path Toward New Treatments

November 2018

A new statistical method identifies bacteria that grow at different rates in disease, such as inflammatory bowel disease, vs. in normal individuals. The potential: develop treatments by inhibiting or promoting bacterial growth.

Yan Gao, Hongzhe Li

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Differences in Activity Patterns May Point to Biomarkers for Bipolar Disorder

August 2017

Each year in the U.S., bipolar disorder affects about six million adults, and about 16 million adults experience at least one major depressive episode,  according to the National Institute of Mental Health. This study is the largest to date that examined motor-activity patterns in people who represent the full range of mood-disorder subgroups--(Bipolar I (BPI), Bipolar II (BPII) and major depression (MDD)--in a non-clinical population sample. Its findings are thus are more likely to apply to the general community and to offer usable clues about biomarkers for mood disorders.

Haochang Shou, L. Cui, I. Hickie, D. Lameira, F. Lamers, J. Zhang, C. Crainiceanu, V. Zipunnikov and K.R. Merikangas

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About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

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