Methodological Research

Many new and complex issues are confronted in the conduct of infectious disease studies creating a need for novel statistical methods. CBAR’s location within the Department of Biostatistics provides a unique environment for novel methodological research.  The Department of Biostatistics at HSPH is consistently rated the top Biostatistics Department in the world by US News and World Report. This provides the opportunity for methodologic development collaborations among many other statisticians with expertise in statistical methodology areas relevant to infectious studies. The Department’s extensive seminar series and working groups provide opportunities for CBAR statisticians to learn about recent advances in applications and methods.

Current foci include the design and analyses of studies with patient-focused outcome measures that integrate efficacy and safety outcomes, competing risks, personalizing treatment, and comparison of diagnostic technologies (e.g., point of care versus gold standard technologies).

CBAR researchers also focus on the development and application of sophisticated epidemiological models for evaluating treatment effects accounting for confounding by indication in observational studies. These include the use of inverse probability of treatment weighting (IPTW), marginal structural models, mediation analysis, and other causal inference approaches.

Methods papers 

Chernofsky A, Bosch RJ, Lok JJ. Causal mediation analysis with mediator values below an assay limit. Stat Med. 2024 May 30;43(12):2299-2313. doi: 10.1002/sim.10065. Epub 2024 Mar 31. PMID: 38556761. https://pubmed.ncbi.nlm.nih.gov/38556761/

Weir IR, Harrison LJ. An evaluation of confidence intervals for a cumulative proportion to enable decisions at interim reviews of single-arm trials. Contemp Clin Trials. 2024 Mar;138:107453. doi: 10.1016/j.cct.2024.107453. Epub 2024 Jan 20. PMID: 38253253; PMCID: PMC10922879. https://pubmed.ncbi.nlm.nih.gov/38253253/

Montepiedra G, Svensson EM, Wong WK, Hooker AC. Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV. CPT Pharmacometrics Syst Pharmacol. 2024 Feb;13(2):270-280. doi: 10.1002/psp4.13077. Epub 2023 Nov 17. PMID: 37946698; PMCID: PMC10864936. https://pubmed.ncbi.nlm.nih.gov/37946698/

Xiang Q, Bosch RJ, Lok JJ. The survival-incorporated median vs the median in the survivors or in the always-survivors: What are we measuring? and Why? Stat Med. 2023 Dec 20;42(29):5479-5490. doi: 10.1002/sim.9922. Epub 2023 Oct 12. PMID: 37827518; PMCID: PMC11104567. https://pubmed.ncbi.nlm.nih.gov/37827518/

Harrison LJ, Wang R. Sample size calculation for randomized trials via inverse probability of response weighting when outcome data are missing at random. Stat Med. 2023 May 20;42(11):1802-1821. doi: 10.1002/sim.9700. Epub 2023 Mar 6. PMID: 36880120; PMCID: PMC10368173. https://pubmed.ncbi.nlm.nih.gov/36880120/

Bather JR, Horton NJ, Coull BA, Williams PL. The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows. Stat Med. 2023 Apr 15;42(8):1171-1187. doi: 10.1002/sim.9664. Epub 2023 Jan 16. PMCID: PMC10023485 https://pubmed.ncbi.nlm.nih.gov/36647625/

Kang M, Kendall MA, Ribaudo H, Tierney C, Zheng L, Smeaton L, Lindsey JC. Incorporating estimands into clinical trial statistical analysis plans. Clinical trials (London, England). 2022 June;19(3):285-291. PubMed PMID: 35257600; PubMed Central PMCID: PMC9232859; DOI:10.1177/17407745221080463. https://pubmed.ncbi.nlm.nih.gov/35257600/

Nelson BS, Liu L, Mehta C. A simulation-based comparison of estimation methods for adaptive and classical group sequential clinical trials. Pharm Stat. 2022 May;21(3):599-611. doi: 10.1002/pst.2188. Epub 2021 Dec 26. PMID: 34957677. https://pubmed.ncbi.nlm.nih.gov/34957677/

Weir IR, Rider JR, Trinquart L. Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials. Pharm Stat. 2022 Jan;21(1):163-175. doi: 10.1002/pst.2159. Epub 2021 Aug 4. PMID: 34346173; PMCID: PMC8776584. https://pubmed.ncbi.nlm.nih.gov/34346173/

Weir IR, Wasserman S. Treatment Effect Measures for Culture Conversion Endpoints in Phase IIb Tuberculosis Treatment Trials. Clin Infect Dis. 2021 Dec 6;73(11):2131-2139. doi: 10.1093/cid/ciab576. PMID: 34254635; PMCID: PMC8664460. https://pubmed.ncbi.nlm.nih.gov/34346173/