Biostatistics at Hackensack Meridian Health   

Biostatistics at Hackensack Meridian Health

At Hackensack Meridian Health, investigators, students, and staff interested in research are encouraged to work with the Biostatistics Team from the inception of research. Our biostatistics team provides support in multiple areas including protocol development, statistical analysis plan, data analysis, abstract and poster preparation, and more. Our team has expertise in clinical research, public health, behavioral research, and basic science.

How can you request help?

To request a biostatistician to work with you on your project, please fill out this form. Please provide all relevant information including any existing protocol drafts, study design, and manuscripts (if applicable). After receipt of your completed form, an initial consultation will be scheduled with the principal investigator and other involved personnel. During this consultation, the project needs and a proposed timeline of completion will be discussed.

To request protocol development assistance (e.g. study design), please fill out this form.


Meet your HMH biostatistics team!

Dr. Yen-Hong Kuo is the Manager of the Biostatistics Core here at HMH. Dr. Kuo joined the network in 1997. He also serves as an Assistant Professor in the Department of Medical Sciences at the Hackensack Meridian School of Medicine. He received his Master of Science degree in Agriculture from National Taiwan University and his Master of Science in Biostatistics from Johns Hopkins University. His Ph.D. is in Biostatistics from Rutgers University. Dr. Kuo is an experienced scientist in the areas of public health and clinical research. He has authored and co-authored more than 240 peer-reviewed journal articles and conference abstracts. Dr. Kuo specializes in the real world evidence, design and analysis of clinical trials and epidemiologic studies, meta-analysis, methodologies and quality in biostatistics, healthcare outcomes and human subject protections. HMH publications: Yen-Hong Kuo, Ph.D.

Dr. Themba Nyirenda is a Research Biostatistician here at HMH. Since joining the network in 2009, he has successfully supported over 13 federal-funded (NIH/DoD) investigations and published over 50 articles in peer-reviewed journals. He is an Assistant Professor in the Department of Medical Sciences at the Hackensack Meridian School of Medicine. He is a member of the Biostatistics Core of the LCCC/JTCC Cancer Consortium, providing biostatistical support for Georgetown University and HMH. He earned his Ph.D. from Western Michigan University in Statistics with a concentration in rank estimation for non-linear models. Dr. Nyirenda specializes in the design and conduct of studies in Oncology (Phase 1- Phase 3), Neurology (Parkinson’s Disease), innervation of laryngeal, pharyngeal and oral cavity (tongue) muscles and paralyzed limb muscles. HMH publications: Themba Nyirenda, Ph.D.

Dr. Simon Gelman is a Research Biostatistician here at HMH. He joined the network in 2022. Prior to that, he worked in the pharmaceutical realm, having spent 10 years with Psychogenics, a contract research organization (CRO). Before joining the CRO, he was a postdoctoral fellow in the Neuroscience department at the Albert Einstein College of Medicine. His Ph.D. is in Biology from the University of Maryland and his M.S. is in Statistics from Montclair State University. HMH publications: Simon Gelman, Ph.D., MS

Dr. Lora Kasselman joined HMH as a Research Biostatistician in January 2023. Before that, she worked as a Research Scientist and Assistant Professor at NYU Winthrop Hospital and NYU Long Island School of Medicine. Her Ph.D. is in Psychology with a focus in Behavioral Neuroscience. She completed her postdoctoral training at Harvard Medical School and Penn State College of Medicine. Several years ago her research interests led her to get her M.P.H. in Biostatistics and Epidemiology at the CUNY School of Public Health. Dr. Kasselman specializes in the analysis of publicly available population-representative datasets as well as social-behavioral studies. HMH publications: Lora Kasselman, Ph.D., MPH

Policies and Procedures/Guideline Documents

Biostatistics Core Service Policy and Standard Operating Procedures are in place to help describe the process by which Biostatistics support is requested for research projects and how these projects are then developed, executed, and delivered to the respective Principal Investigator or designee.

Library of documents

  • Analyzable datasets (this can be used to help you create an analyzable dataset for delivery to your project biostatistician)
  • Variables table (this can be used to help you clearly define variables for inclusion in the IRB protocol or the statistical analysis plan)

Frequently Asked Questions (FAQs)

The PICO(T) mnemonic is helpful for developing your research question:

  • P: POPULATION - who do you want to recruit/study?
  • I: INTERVENTION - what is the treatment in your study (if none, then you are likely conducting an observational study which still requires a good research question)?
  • C: COMPARISON - who is your reference population/group to which you will compare your treatment group (AKA control group; if none, then you are likely reporting descriptive statistics on your population)?
  • O: OUTCOME - what is your primary variable of interest that you are measuring? It is best to use validated and reliable tools to measure this, if available.
  • (T): TIME - how long will your study last with respect to data collection?
  • At a minimum, PICO should be encompassed within your research question(s). For example, a PICO(T)-based research question might look like the following: “Does one probiotic drink per day (kombucha, 20 oz) reduce the risk of heart disease in adults diagnosed with major depressive disorder (MDD) compared to adults with MDD who do not drink kombucha?” In this case, P = adults with MDD, I = kombucha (20 oz, once per day), C = adults without depression, and O = heart disease.
  • see this article for more information.
  • Variables are essential components of research studies.
  • Variables help operationalize concepts for data collection, e.g. to measure “depression” one may use a depression scale such as the Beck Depression Inventory as the primary outcome variable.
  • There are different types of variables:
    • Quantitative variables represent a (typically numeric) measurable quantity.
      • These can be continuous (take on any value, e.g. height) or discrete (cannot take on any value, usually whole numbers, e.g. number of skin lesions).
    • Qualitative variables represent (typically descriptive, categorical, or word-based) names, labels, categories, etc.
      • These can be nominal/categorical (names or categories, e.g. gender, diabetes type) or ordinal (similar to categorical but they have an inherent order, e.g. college year).
  • It is important to ensure that your variable type(s) are adequate to address your research question.
  • It is also important to understand what type of variable(s) you are using since this will determine, in part, what type of statistical analyses are appropriate.
  • See this article for more information.
  • It is important to choose the correct research design based on your research question.
    • Is your research question descriptive?
      • descriptive studies:
        • case reports
        • case series
        • cross-sectional studies to determine frequencies and/or distributions of disease
    • Is your research question analytical?
      • observational studies:
        • cohort
        • case-control
        • cross-sectional studies to compare different groups or exposures
      • experimental studies
        • randomized controlled trials
    • See this link for more information on study designs.
  • Your study design will then inform what type of statistical analysis is appropriate.
    • See this link for more information on types of statistical analyses.
  • Data organization is important for analysis as well as interpretation of your results.
  • Spreadsheets can help keep your data organized.
    • some best practices for inputting data into spreadsheets include:
      • be consistent (e.g. always using “female” and not using “female” as well as “F”, “Female”, or “f”)
      • indicate missing data (e.g. with “999” or “NA”)
      • choose meaningful and careful variable names (e.g. “glucose_30min_fasting” versus “30 min glu fst”, the latter of which contains spaces and unclear abbreviations)
      • make sure each cell contains one piece of information (e.g. a cell containing the value “1” which corresponds to the Likert scale value of “very likely” versus putting “1 very likely” in the cell together. The latter data point cannot be analyzed as is
      • create a data dictionary which would be a separate tab or document containing information about variable names, ranges, detailed information, measurement units, etc.
      • keep a clean “raw” version of your data that is not touched; when doing minor calculations or creating new variables always create a working copy so that you don’t risk data loss in the original file
    • for more detailed information on data organization see this link.
  • See this link for recorded biostatistics videos.

Publications Co-authored by Our Biostatisticians

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