Services

• Advise on study design, data management, and execution for clinical trials and observational studies in national and international settings
• Advise on sample size calculation and power analysis
• Advise on rigorous statistical data analysis and modeling
• Advice on grant proposal development
• Special interest in cluster randomized trial design and methods
• Special interest in data coordinating center (DCC) projects for multi-site studies

Collaborative project examples

Worked on more than 50+ collaborative projects in different areas as a principal Investigator (PI), co-PI, co-Investigator, Statistical Investigator, or Biostatistician.  I used different datasets including individual study data, multi-site and multi-country study data, statewide surveillance data, survey data, and laboratory data.  Following are selected collaborative projects example in HIV/AIDS:

• HIV Viral Load
  • Disparities in Viral Load and CD4 Count Trends among HIV Infected Adults;
  • Individual HIV Viral Load and Community HIV Viral Load Trends among HIV Infected Adults;
  • Rural-urban differences in HIV viral loads and progression to AIDS among new HIV cases
  • HIV Viral Load trends among children;
  • Evaluation of the relationship between the concentration of HIV-1 RNA in blood and semen;
  • Cervical dysplasia in women infected with the human immunodeficiency virus (HIV): A correlation with HIV viral load and CD4+count;
  • The effect of Plasmodium falciparum malaria on HIV-1 RNA blood plasma concentration;
  • Effects of genital tract inflammation on HIV-1 V3 populations in blood and semen;
  • Reduction of concentration of HIV-1, in semen after treatment of urethritis: Implications for prevention of sexual transmission of HIV-1;
• Biostatistics Modeling: 
  • HIV transmission modeling using national and international datasets;
  • Modeling HIV disease burden;
  • Viral burden in genital secretions determines male-to-female sexual transmission of HIV-1: A probabilistic empiric model;
  • Statistical models to estimate male-to-female HIV transmission probabilities;
  • Modeling Right-skewed Heavy-tail Right-censored Data with Application to HIV Viral Load;
  • New Biostatistics methodology development for HIV/AIDS data analysis;
• Other Areas

• HIV/AIDS in the South: Evaluating an interprofessional community stakeholder for social work and public health professionals on HIV/AIDS in the South;
• Determinants of Intimate Partner Violence among HIV Positive and Negative Women;
• Cardiovascular Disease Morbidity and Mortality among HIV/AIDS Patients in South Carolina;
• Comparison of blood collected in ACD and EDTA for use in HIV peripheral blood mononuclear cell cultures;
• Optimizing Partner Notification Programs for Men Who Have Sex with Men: Factorial Survey Results;
• Late Diagnosis of HIV in South Carolina: Prevalence, Cause, and Consequences;

Services

• Advice for study design in population, clinical and molecular studies in small or large groups of participants
• Data management for clinical and observational for domestic and international studies
• General statistical analysis of data generated by these studies
• Extensive experience in guiding students and trainees in all aspects of conducting studies through manuscript preparation

Collaborative project examples

• Epidemiology of co-morbidities and disease course in HIV patients
  • Tackling a neglected HIV-associated infection - A validation study of the IgG and IgM serological responses to talaromycosis (Shanti Narayanasamy, Thuy Lee)
  • Registry in HIV/HCV Co-infected Patients Initiating Ledipasvir/Sofosbuvir (Susanna Naggie)
  • Predictors of mortality in treatment experienced HIV-infected patients in Northern Tanzania (Deng Madut, Nathan Thielman)
  • Proton Pump Inhibitor Usage Reduces SVR Response in HIV/HCV Patients Treated with Ledipasvir/Sofosbuvir (Susanna Naggie)
• Infectious organism host-pathogen responses
  • A blood-based host gene expression assay allows for early detection of respiratory viral infection (Chris Woods, Micah McClain)
  • Associations of pathogen-specific and host-specific characteristics with disease outcome in patients with Staphylococcus aureus bacteremic (SAB) pneumonia (Vance Fowler)
  • Genetic Variation of DNA Methyltransferase-3A Contributes to Protection Against Persistent MRSA Bacteremia in Patients (Vance Fowler)
• Global health and One Health
  • High Risk of Influenza Virus Infection Among Swine Workers: Examining a Dynamic Cohort in China (Greg Gray)
  • Patient factors affecting successful linkage to treatment in a cervical cancer prevention program in Kenya (Megan Huchko)
  • Environmental influences on the skin microbiome of humans and cattle in rural Madegascar (Charlie Nunn)

Services

• Full research-cycle support for collaborators doing High-Throughput Sequence experiments:
  • Collaborative planning of experiments and new experimental methods, mindful of the research questions and ultimate analysis plans;
  • Help in preparing analysis plans for grant proposals;
  • Analysis of resulting data and collaborative writing of manuscripts describing research results.
• Experimental methods that the Microbiome & Genomics Core can provide analytic support for include:
  • Microbiome: Amplicon (e.g. 16S rRNA), shotgun metagenomic data
  • RNA-Seq: bulk and single-cell
  • DNA-Seq: resequencing, de novo genome sequencing, and annotation (e.g. opportunistic pathogens)
  • Antigen receptor (TCR, BCR) sequencing

Collaborative project examples

• RNA-Seq
  • Multidimensional immune profiling in HIV-associated neuroinflammation (David Murdoch)
  • Transcriptional profiling of immune responses to novel adjuvants in a murine model (Herman Staats)
  • Comparative analysis of RNA enrichment methods for preparation of Cryptococcus neoformans RNA-sequencing libraries (Andrew Alspaugh)
• Microbiome
  • A highly conserved gut microbial environment is associated with increased risk of postnatal HIV infection in infants (Sallie Permar and Ria Goswami)
  • Impact of Maternal HIV Infection on the Respiratory Microbiome of Infants (Matthew Kelly)
• Novel Genomic Methods
  • Rapid mapping of insertional mutations to probe cell wall regulation in Cryptococcus neoformans (Andrew Alspaugh)

Services

• Analysis of complex data sets, including:
  • Pipeline development for automated workflows
  • Statistical methods for high-dimensional analysis of assay data
  • Machine learning methods to predict outcomes from complex data sets
• Assays that we have experience with include:
  • scRNA-seq (with Josh Granek)
  • Flow and mass cytometry
  • Antibody binding, kinetic and functional assays
• Development of mathematical models and computer simulations of host-pathogen dynamics. Examples include:
  • Modeling time to rebound after ART interruption
  • Modeling in-utero transmission through placenta

Collaborative project examples

• Analysis of complex immunological data sets
  • Statistical methods for comparative analysis of multi-sample flow cytometry data (Guido Ferrari, Kent Weinhold, Georgia Tomaras)
  • Statistical methods for analysis of high-throughput antibody data (Georgia Tomaras)
  • Statistical methods for analysis of soluble factors (John Sleasman, David Murdoch)
  • Machine learning methods for structural and functional MRI data (Christina Meade)
• Mathematical modeling of host-pathogen dynamics
  • The origin, predictors, and immune correlates of viral rebound in orally SHIV infected infant monkeys (Sallie Permar)
  • Immunologic and virologic determinants of congenital Cytomegalovirus transmission and disease in rhesus monkeys (Sallie Permar)

Support for grant applications by CFAR principal investigators
In clinical trials, power calculation for sample size ensures that we have sufficient sample size for achieving a desired power for detecting a clinically meaningful treatment effect at a pre-specified type I error rate. Power calculation for sample size is critical especially when only limited budget and/or resources are available. The QS Core will assist CFAR PIs in (i) power calculation for sample size estimation and/or justification, (ii) evaluation the merits and disadvantages of alternative designs, and (iii) preparation of statistical section for inclusion in the grant application for statistical/scientific validity of the grant application.

Support for clinical studies sponsored by CFAR principal investigators
The QS Core will provide data management (including data quality and verification), statistical programming, and data analysis support to clinical studies sponsored by CFAR investigators to ensure unbiased (accurate) and fair (reliable) assessment of test treatments under investigation. In addition, the QS Core will work closely with the investigators for interpreting the analysis results and preparing the manuscripts for publication consideration in leading medical journals.

Training on statistical issues commonly encountered in AIDS clinical research
In clinical trials, some critical statistical/scientific issues such as the selection of non-inferiority margin in non-inferiority (active control) trials, appropriate methods for missing data imputation, the establishment of predictive model using genomic markers, and advantages of clinical trial simulation are commonly encountered in AIDS clinical research. QS will identify specific issues/topics that are of particular to CFAR investigators and then provide training such as seminars, tutorials, short courses, or workshops to CFAR investigators at regulatory basis.

Design and analysis of AIDS clinical research, especially on the potential use of adaptive clinical trial designs and statistical design for small scale exploratory studies
In recent years, the use of innovative adaptive methods in clinical trial has become very popular due to its flexibility and efficiency for identifying any signal, pattern, and trend of clinical benefits of test treatments under investigation. However, the quality, validity, and integrity of data collected from clinical studies utilizing adaptive clinical trial designs are of great concern to principal investigators and regulatory agencies. QS Core will provide statistical consultation and support to clinical studies intend to use adaptive designs such as group sequential design, adaptive dose finding design, phase I/II seamless adaptive design, biomarker-adaptive design (target clinical trials), and other innovative adaptive clinical trial designs that are commonly used in AIDS clinical research to make sure the quality, validity and integrity of the intended clinical trials utilizing innovative adaptive clinical trial designs.

Perhaps a more pressing problem for typical CFAR investigators is how to design effective small-scale exploratory studies on a shoestring budget. Good statistical planning is critical for such studies, since small poorly designed studies may provide no useful information at all. We are interested in combining exploratory HIV/AIDS studies with clinical trial simulations in order to provide insight into the optimal design and strategies for using these small scale studies to launch randomized clinical trials.

Software and algorithms for analysis of immunological assays
Sometimes, vendor-provided software for the analysis of data from immunological assays perform poorly on specific data sets or are simply not available (for newly developed assays). We are happy to work with CFAR investigators to help find or develop the appropriate algorithms or software packages to analyze and summarize immunological assay data. For example, we have previously developed software and algorithms for the analysis of iTopia, Luminex and polychromatic flow cytometry assay data in collaboration with CFAR investigators.

Mathematical and statistical modeling of host-pathogen dynamics
We are happy to work with CFAR investigators who wish to develop and calibrate mathematical models of some aspect of host, pathogen or host-pathogen dynamics. Such models are typically expressed as systems of nonlinear difference equations or ordinary differential equations, and can provide insight into biological mechanisms typically not available with standard statistical approaches. For example, the effect of combination antiviral therapy was initially evaluated with the help of such models for viral dynamics.

Workshops on practical computing for research scientists
The QS core runs annual workshops on practical computing for CFAR research scientists. The objective of these workshops is to increase the productivity of researchers by the use of software tools such as the Unix shell, text editors, databases, Python and R to facilitate data management, manipulation, analysis and reporting. These workshops are intended to convert novices into competent (not expert) users of powerful computational tools.
Workshop Information

International Science and Statistics Skills Workshop – Kilimanjaro Christian Medical University College (KCMC)
Title: HIV/AIDS Research Design and Analysis Workshop
Instructor:  Hrishikesh Chakraborty
Date: October 21-25
Abstract: This workshop covers the main concepts and methods in the design and execution of a research study. The course covers several important topics that commonly face by research scientists conducting observational and clinical research. The course is designed for research scientists working in the health research organizations. It is equally beneficial to scientists working in institutions that deliver health care, government branches that conduct health-care related research and academics interested in learning how to plan a research project, how to properly design a research study with emphasis on sample size and power calculation fundamentals; and how to conduct and disseminate research findings. The attendees are required to have a basic knowledge of research studies. Technical aspects will be minimal, although familiarity with the basics of research studies and clinical trial design is strongly recommended.
This is in collaboration with the Clinical Core.

Help with statistical plan for CFAR small grant applications
The BCB core helps with the experimental design and analysis plan for many CFAR small grant applicants. This is a free service, and the Core also provides support for data analysis upon request if the grant application is successful. Some examples of small grants where a critical statistical review was provided are:

• Susanna Naggie: The role of antiretroviral-induced dyslipidemia in lipid-HCV interactions in HIV/HCV co-infected patients: An assessment of the effect on HCV viral integration

• Wanda Lakey: From wasting to obesity - Antiretroviral therapy and weight gain in HIV-infected patients

• Giny Fouda: Specificity and function of maternally-acquired and vaccine-induced antibody responses in HIV-exposed infacnts

• Christy Kaiser: Comparison of screening rates for coronary artery disease in HIV-infected and HIV-uninfected patients

• Maria Biasi: The ability of integrase-defective lentiviral vector (IDLVs) to elicit durable antibody responses in systemic and mucosal compartments

• Dorothy Dow: Establishing mental health needs in HIV-positive adolescents in Tanzania

Data analysis for prospective cohort studies of fungal, bacterial and viral infections in sub-Saharan Africa
The BCB core performed data analysis for a series of prospective cohort studies of infectious diseases in Moshi, Tanzania in collaboration with John Crump and John Bartlett. These studies have been described in the following publications.

• Crump, J. A., Ramadhani, H. O., Morrissey, A. B., Saganda, W., Mwako, M. S., Yang, L.Y., Chow, S. C., Reyburn, H., Njau, B. N., Shaw, A.V., Bartlett, J. A., and Maro, V. P. (2012). Bacteremic disseminated tuberculosis in sub-Saharan Africa: a prospective cohort study. Clinical Infectious Diseases: 55:242–250. [SCI]

• Crump, J.A., Ramadhani, H.O., Morrissey, A.B., Saganda, W., Mwako, M.S., Yang, L.Y., Chow, S.C., Njau, B.N., Mushi, G.S., Maro, V.P., Reller, L.B., Bartlett, J.A. (2012). Bacteremic disseminated tuberculosis in sub-Saharan Africa: a prospective cohort study. Clinical Infectious Diseases, 55, 242–250.

• Crump, J.A., Ramadhani, H.O., Morrissey, A.B., Saganda, W., Mwako, M.S., Yang, L.Y., Chow, S.C., Morpeth, S.C., Reyburn, H., Njau, D.N., Shaw, A.V., Bartlett, J.A., and Maro, V.P. (2011). Invasive bacterial and fungal infections among hospitalized HIV-infected and HIV-uninfected adults and adolescents in northern Tanzania. Clinical Infectious Diseases, 52, 341–348.

• Crump, J.A., Ramadhani, H.O., Morrissey, A.B., Msuya, L.J., Yang, L.Y., Chow, S.C., Morpeth, S.C., Reyburn, H., Njau, B.N., Shaw, A.V., Diefenthal, H.C., Bartlett, J.A., Shao, J.F., Schimana, W., Cunningham, C.K., and Kinabo, G.D.(2011). Invasive bacterial and fungal infections among hospitalized HIV-infected and HIV-uninfected children and infants in northern Tanzania. Trop Med Int Health, 16, 830–837.

RCT Of An Integrated Treatment Of Persons With Co-Occurring HCV And Alcohol Abuse
Alcohol abstinence greatly improves outcomes for HCV-nfected patients. A 6-month treatment model for alcohol abstinence in these patients was proposed by Rae Jean Proeschold-Bell, and the BCB core helped design the statistical analysis plan for this randomized control trial which was recently awarded an R01 by NIH on resubmission.

Methodological studies on adaptive trial design
In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. Some recent publications from the BCB core on adaptive trial design include:

• Chow SC. Adaptive clinical trial design. Annu Rev Med. 2014 Jan 14;65:405–15. doi: 10.1146/annurev-med–092012–112310. PMID: 24422576

• Chow SC and Chiu ST (2013). Sample size and data monitoring for clinical trials with extremely low incidence rate. Therapeutic Innovation and Regulatory Science, 47:438–446.

• Chow SC, Chiu ST (2013) A Note on Design and Analysis of Clinical Trials. Drug Des 2:102. doi: 10.4172/2169–0138.1000102

Statistical mixture modeling for cell subtype identification in flow cytometry
Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data, and may reduce the subjectivity inherent in manual gating procedures. The BCB core, working with the Flow Cytometry Core, published the first paper on the use of multivariate Gaussian mixture models for cell subset identification from flow cytometry data in 2008, and this continues to be an active area of research. Some relevant publications are:

• Chan C, Feng F, Ottinger J, Foster D, West M and Kepler TB, Statistical mixture modeling for cell subtype identification in flow cytometry, Cytometry A, (2008), 73A:693–701

• Frelinger, J, Ottinger J, Chan C, Modeling flow cytometry data for cancer vaccine immune monitoring, Cancer Immunology Immunotherapy, (2010), 59:1435–41.

• Chan C, Lin L, Frelinger J, Hérbert V, Gagnon D, Landry C, Sékaly RP, Enzor J, Staats J, Weinhold KJ, Jaimes M, West M. Optimization of a highly standardized carboxyfluores- cein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation, Cytometry A, (2010), 77:1126–36.

• Cron A, Gouttefangeas C, Frelinger J, Lin L, Singh SK, Britten CM, Welters MJP, van der Burg SH, West M, Chan C,. (2013) Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples. PLoS Computational Biology 9(7): e1003130. doi:10.1371/journal.pcbi.100313.

Leadership:
Cliburn Chan MBBS Ph.D. (Core director/Computational Biology)
Immune response models, assay analysis and immune profiling
cliburn.chan@duke.edu
Dr. Chan has a background in medicine, immunology, mathematics and statistics, and is interested in the application of quantitative methods and models for understanding immune responses. Current research interests include the statistical characterization of single cells from cytometry, scRNA-seq and imaging assays, immune and neurological correlates of disease outcomes including HIV-associated cognitive decline, and the modeling of host-pathogen dynamics. Dr. Chan also leads a research education program in Quantitative Methods for HIV/AIDS research, which includes organizing 18 workshops a year on data science, statistical thinking and assay analysis, as well as annual summer internships for a dozen graduate student interns from quantitative sciences to work on HIV/AIDS research challenges.

Hrishikesh Chakraborty, DrPH
Biostatistics
hrishikesh.chakraborty@duke.edu

QS Core group email: cfar-bcb-core@duke.edu