IMG_20211113_141042359_edited.jpg

Statistical consulting services spanning trial design, biostatistics, machine learning, and engineering.

Work with me

Thank you for your message!

Haaland Consulting

IMG_20220116_122849417.jpg

About

Haaland Consulting is led by Ben Haaland, PhD. Ben earned his PhD in Statistics from the University of Wisconsin-Madison in 2010. He has served as an Assistant Professor at Duke-NUS Graduate Medical School, Singapore and the Department of Industrial and Systems Engineering at the Georgia Institute of Technology. Since 2017, Ben has been an Associate Professor in the Department of Population Health Sciences at the University of Utah and the Cancer Biostatistics Shared Resource at Huntsman Cancer Institute. 

When Ben is not designing trials and doing statistics, he enjoys spending time with his family, exercising, and hiking in the desert and mountains.

 
 
IMG_20211126_095303323_edited.jpg

Clinical Trial Design

  • Design and analysis of phase I, II, and III clinical trials

  • Preparation of study concepts for discussions with industry

  • Sample size determination achieving target power subject to type I error constraints, accrual rates, length of follow-up

  • Dose-escalation studies (e.g., 3+3, BOIN)

  • Single arm (e.g., Simon's two-stage phase II)

  • Randomized phase II/III with interim monitoring for safety, futility, and efficacy

  • Bayesian trial design and monitoring

  • Service as Co-Director of the Cancer Biostatistics Shared Resource at Huntsman Cancer Institute 2018-2022

  • Member and statistician for Protocol Review and Monitoring Committee, Independent Protocol Development Committee, and Center for Investigational Therapeutics Committee at Huntsman Cancer Institute, 2017-present

  • Member and statistician for National Cancer Institute (NCI) Gynecologic Cancer Steering Committee (GCSC) Uterine Task Force, 2020-present

Selected references:

  1. Brown, S.M., Peltan, I., Kumar, N., Leither, L., Webb, B.J., Starr, N., Grissom, C.K., Buckel, W.R., Srivastava, R., Butler, A.M., Groat, D., Haaland, B., Ying, J., Harris, E., Johnson, S., Paine, R., and Greene, T. 2020. Hydroxychloroquine vs. Azithromycin for Hospitalized Patients with COVID-19 (HAHPS): Results of a Randomized, Active Comparator Trial. Annals of the American Thoracic Society, (ja). 

  2. Agarwal, N., Nussenzveig, R.H., Hahn, A.W., Hoffman, J.M., Morton, K., Gupta, S., Batten, J., Thorley, J., Hawks, J.L., Santos, V.S. and Nachaegari, G., Wang, X., Boucher, K., Haaland, B., Maughan, B.L. (2020). Prospective Evaluation of Bone Metabolic Markers as Surrogate Markers of Response to Radium-223 Therapy in Metastatic Castration Resistant Prostate Cancer. Clinical Cancer Research, 26(9), pp.2104-2110.

  3. Finkelstein, E.A., Haaland, B., Bilger, M., Sahasranaman, A., Sloan, R.A., Nang, E.E.K. and Evenson, K.R., 2016. Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial. The Lancet Diabetes & Endocrinology, 4(12), pp.983-995. PMID:27717766. 

Machine Learning and Predictive Modeling

  • Development of predictive models based on spectrum of machine learning tools (e.g., neural networks, boosting, decision tree, random forest, support vector machine, Gaussian process, SuperLearner)

  • Generalizability assessment via cross-validation and bootstrapping

  • Dimension-reduction for high-dimensional analyses (e.g., regularization, variable selection, clustering, principal components)

  • Ensemble learning for improving performance and modularity

  • Online retail tailoring consultant at Home Depot, 2016-2017

  • Developed and taught PhD courses in machine learning in Division of Biostatistics, Department of Population Health Sciences at University of Utah and Industrial and Systems Engineering at Georgia Institute of Technology

Selected references:

  1. Brintz, B.J., Haaland, B., Howard, J., Chao, D.L., Proctor, J.L., Khan, A.I., Ahmed, S.M., Keegan, L.T., Greene, T., Keita, A.M. and Kotloff, K.L., 2021. A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea. Elife, 10, p.e63009. 

  2. Nongpiur, M.E., Haaland, B., Friedman, D.S., Perera, S.A., He, M., Foo, L.L., Baskaran, M., Sakata, L.M., Wong, T.Y. and Aung, T., 2013. Classification algorithms based on anterior segment optical coherence tomography measurements for detection of angle closure. Ophthalmology, 120(1), pp.48-54. PMID: 23009888.  

  3. Ong, M.E.H., Goh, K., Fook-Chong, S., Haaland, B., Wai, K.L., Koh, Z.X., Shahidah, N. and Lin, Z., 2013. Heart rate variability risk score for prediction of acute cardiac complications in ED patients with chest pain. The American journal of emergency medicine, 31(8), pp.1201-1207. PMID: 23763936. 

Comparative Effectiveness

​​

  • Construction of comparison cohorts, including entry criteria and index dates

  • Balancing or adjusting for potential confounders (e.g., matching, inverse propensity weighting, multivariable analyses)

  • Feature construction based on administrative or electronic health record (EHR) data (see below)

  • Meta-analyses of multiple study results (see below)

  • Analyses of cost and utility data (e.g., quality-adjusted life years)

Selected references:

​​

  1. Aguiar, P.N., Haaland, B., Park, W., San Tan, P., Del Giglio, A. and de Lima Lopes, G., 2018. Cost-effectiveness of osimertinib in the first-line treatment of patients with EGFR-mutated advanced non–small cell lung cancer. JAMA oncology, 4(8), pp.1080-1084.

  2. Mooney, K., Titchener, K., Haaland, B., Coombs, L.A., O'Neil, B., Nelson, R., McPherson, J.P., Kirchhoff, A.C., Beck, A.C. and Ward, J.H., 2021. Evaluation of Oncology Hospital at Home: Unplanned Health Care Utilization and Costs in the Huntsman at Home Real-World Trial. Journal of Clinical Oncology, pp.JCO-20. 

  3. Madaras-Kelly, K., Hostler, C., Townsend, M., Potter, E.M., Spivak, E.S., Hall, S.K., Goetz, M.B., Nevers, M., Ying, J., Haaland, B. and Rovelsky, S.A., 2021. Impact of implementation of the core elements of outpatient antibiotic stewardship within veterans health administration emergency departments and primary care clinics on antibiotic prescribing and patient outcomes. Clinical Infectious Diseases, 73(5), pp.e1126-e1134.

Analysis of Electronic Health Record (EHR) Data

​​

  • Construction of analytic datasets based on EHR data (e.g., data cleaning, linkage, establishing common time scale)

  • Construction of comparison cohorts, including entry criteria and index dates

  • Balancing or adjusting for potential confounders (e.g., matching, inverse propensity weighting, multivariable analyses)

  • Feature construction

  • Extensive experience using Flatiron Health medical oncology databases

Selected references:

​​

  1. Kerrigan, K., Jo, Y., Chipman, J., Haaland, B., Puri, S., Akerley, W. and Patel, S., 2022. A Real-World Analysis of the use of Systemic Therapy in Malignant Pleural Mesothelioma and the Differential Impacts on Overall Survival by Practice Pattern. JTO Clinical and Research Reports, p.100280.

  2. Swami, U., Haaland, B., Kessel, A., Nussenzveig, R., Maughan, B.L., Esther, J., Sirohi, D., Pal, S.K., Grivas, P. and Agarwal, N., 2021. Comparative Effectiveness of Immune Checkpoint Inhibitors in Patients with Platinum Refractory Advanced Urothelial Carcinoma. The Journal of Urology, 205(3), pp.709-717.

  3. Kerrigan, K., Patel, S.B., Haaland, B., Ose, D., Weinberg Chalmers, A., Haydell, T., Meropol, N.J. and Akerley, W., 2020. Prognostic significance of patient-reported outcomes in cancer. JCO oncology practice, 16(4), pp.e313-e323.

Meta-analysis

​​

  • Fixed or random effects, Bayesian or frequentist meta-analysis

  • Network meta-analysis, meta-regression, complex meta-analysis incorporating interactions and nested effects

  • Meta-analysis of multiple endpoints (e.g., overall survival, progression-free survival, and overall response rate) simultaneously

  • Non-traditional meta-analyses (e.g., bi-normal random effects network meta-analysis to compare diagnostic modalities)

  • Bayesian missing data techniques

Selected references:

​​

  1. Hahn, A.W., Klaassen, Z., Agarwal, N., Haaland, B., Esther, J., Xiang, Y.Y., Wang, X., Pal, S.K. and Wallis, C.J., 2019. First-line treatment of metastatic renal cell carcinoma: a systematic review and network meta-analysis. European urology oncology, 2(6), pp.708-715.

  2. Haaland, B., San Tan, P., de Castro Jr, G. and Lopes, G., 2014. Meta-analysis of first-line therapies in advanced non–small-cell lung cancer harboring EGFR-activating mutations. Journal of Thoracic Oncology, 9(6), pp.805-811.

  3. da Silveira Nogueira Lima, J.P., Georgieva, M., Haaland, B. and de Lima Lopes, G., 2017. A systematic review and network meta‐analysis of immunotherapy and targeted therapy for advanced melanoma. Cancer medicine, 6(6), pp.1143-1153.

Bayesian Modeling and Analysis

​​

  • Construction of Bayesian models in Markov Chain Monte Carlo software (e.g., Stan, JAGS)

  • Hierarchical modeling

  • Bayesian missing data techniques

  • Prior information expert solicitation

  • Analyses of cost and utility data (e.g., quality-adjusted life years)

Selected references:

​​

  1. Georgieva, M., Lima, J.P.D.S.N., Aguiar Jr, P., de Lima Lopes Jr, G. and Haaland, B., 2018. Cost-effectiveness of pembrolizumab as first-line therapy for advanced non-small cell lung cancer. Lung Cancer, 124, pp.248-254.

  2. Tan, P.S., Lopes, G., Acharyya, S., Bilger, M. and Haaland, B., 2015. Bayesian network meta-comparison of maintenance treatments for stage IIIb/IV non-small-cell lung cancer (NSCLC) patients with good performance status not progressing after first-line induction chemotherapy: results by performance status, EGFR mutation, histology and response to previous induction. European Journal of Cancer, 51(16), pp.2330-2344.

  3. Haaland, B. and Chiang, A.Y., 2014. Bayesian semiparametric predictive modeling with applications in dose-response prediction. Journal of biopharmaceutical statistics, 24(2), pp.294-309.

Design and Analysis of Computer Experiments

​​

  • Gaussian process, local Gaussian process, multi-resolution functional ANOVA modeling

  • Space-filling designs (e.g., orthogonal array-based Latin hypercube, MaxPro, (t,s)-nets)

Selected references:

​​

  1. Sung, C.L., Wang, W., Plumlee, M. and Haaland, B., 2019. Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments. Journal of the American Statistical Association, pp.1-23. 

  2. Haaland, B. and Qian, P.Z., 2011. Accurate emulators for large-scale computer experiments. The Annals of Statistics, 39(6), pp.2974-3002. 

  3. Xu, X., Haaland, B. and Qian, P.Z., 2011. Sudoku-based space-filling designs. Biometrika, 98(3), pp.711-720. 

Evaluation of surrogate endpoints for clinical trials

​​

  • Member of CKD-EPI analytic team 2018-present

  • Trial-level meta-analyses evaluating quality of candidate surrogate endpoints

  • Evaluation of subgroup differences in surrogates

 
 
 
 
 
 
 
IMG_20211114_112439580.jpg

Selected prior projects

National Kidney Foundation

  • ​Evaluation of surrogate endpoints in chronic kidney disease with CKD-EPI analytics team (2018-present)

Value Analytics Labs

  • ​Bayesian network meta-analysis comparing surveillance modalities for hepatocellular carcinoma (2020-present)

Prometics Life Sciences

  • ​Guidance and simulations supporting a registration trial for a rare disease indication (2019)

Home Depot

  • ​Machine learning approaches to online retail optimization and tailoring (2016-2017)