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1. A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury.

Author

Tignanelli, Christopher, Shah, Surbhi, Vock, David, Siegel, Lianne, Serrano, Carlos, Haut, Elliott, Switzer, Sean, Martin, Christie, Rizvi, Rubina, Peta, Vincent, Jenkins, Peter, Lemke, Nicholas, Thyvalikakath, Thankam, Osheroff, Jerome, Torres, Denise, Vawdrey, David, Callcut, Rachael, Butler, Mary, and Melton, Genevieve

Subjects
Clinical decision support, Health informatics, Implementation science, Learning health system, Mixed methods, Prophylaxis, Randomized controlled trial, Stepped wedge, Traumatic brain injury, Venous thromboembolism, Humans, Venous Thromboembolism, Brain Injuries, Traumatic, Decision Support Systems, Clinical, Practice Guidelines as Topic, Implementation Science, Guideline Adherence
Abstract

BACKGROUND: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. METHODS: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a Living Guideline process model for the VTE prevention CDS system. DISCUSSION: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. TRIAL REGISTRATION: Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05628207 .

Published
2024

2. Group LASSO Variable Selection Method for Treatment Effect Generalization

Author

Deng, Chuyu, Koch, Brandon, Vock, David M., and Koopmeiners, Joseph S.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Often in public health, we are interested in the treatment effect of an intervention on a population that is systemically different from the experimental population the intervention was originally evaluated in. When treatment effect heterogeneity is present in a randomized controlled trial, generalizing the treatment effect from this experimental population to a target population of interest is a complex problem; it requires the characterization of both the treatment effect heterogeneity and the baseline covariate mismatch between the two populations. Despite the importance of this problem, the literature for variable selection in this context is limited. In this paper, we present a Group LASSO-based approach to variable selection in the context of treatment effect generalization, with an application to generalize the treatment effect of very low nicotine content cigarettes to the overall U.S. smoking population.

Published
2023

3. Estimating Longitudinal Causal Effects with Unobserved Noncompliance Using a Semi-Parametric G-computation Algorithm

Author

Peterson, Ross L, Vock, David M, and Koopmeiners, Joseph S

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Participant noncompliance, in which participants do not follow their assigned treatment protocol, often obscures the causal relationship between treatment and treatment effect in randomized trials. In the longitudinal setting, the G-computation algorithm can adjust for confounding to estimate causal effects. Typically, G-computation assumes that both 1) compliance is observed; and 2) the densities of the confounders can be correctly specified. We aim to develop a G-computation estimator in the setting where both assumptions are violated. For 1), in place of unobserved compliance, we substitute in probability weights derived from modeling a biomarker associated with compliance. For 2), we fit semiparametric models using predictive mean matching. Specifically, we parametrically specify only the conditional mean of the confounders, and then use predictive mean matching to randomly generate confounder data for G-computation. In both the simulation and application, we compare multiple causal estimators already established in the literature with those derived from our method. For the simulation, we generated data across different sample sizes and levels of confounding. For the application, we apply our method to a trial that sought to evaluate the effect of cigarettes with low nicotine on cigarette consumption (Center for the Evaluation of Nicotine in Cigarettes Project 2 - CENIC-P2).

Published
2023

4. Efficacy and Safety of Ensovibep for Adults Hospitalized With COVID-19

Author

Barkauskas, Christina, Mylonakis, Eleftherios, Poulakou, Garyfallia, Young, Barnaby E, Vock, David M, Siegel, Lianne, Engen, Nicole, Grandits, Greg, Mosaly, Nilima R, Vekstein, Andrew M, Rogers, Ralph, Shehadeh, Fadi, Kaczynski, Matthew, Mylona, Evangelia K, Syrigos, Konstantinos N, Rapti, Vasiliki, Lye, David C, Hui, Diong Shiau, Leither, Lindsay, Knowlton, Kirk U, Jain, Mamta K, Marines-Price, Rubria, Osuji, Alice, Overcash, J Scott, Kalomenidis, Ioannis, Barmparessou, Zafeiria, Waters, Michael, Zepeda, Karla, Chen, Peter, Torbati, Sam, Kiweewa, Francis, Sebudde, Nicholus, Almasri, Eyad, Hughes, Alyssa, Bhagani, Sanjay R, Rodger, Alison, Sandkovsky, Uriel, Gottlieb, Robert L, Nnakelu, Eriobu, Trautner, Barbara, Menon, Vidya, Lutaakome, Joseph, Matthay, Michael, Robinson, Philip, Protopapas, Konstantinos, Koulouris, Nikolaos, Kimuli, Ivan, Baduashvili, Amiran, Braun, Dominique L, Günthard, Huldrych F, Ramachandruni, Srikanth, Kidega, Robert, Kim, Kami, Hatlen, Timothy J, Phillips, Andrew N, Murray, Daniel D, Jensen, Tomas O, Padilla, Maria L, Accardi, Evan X, Shaw-Saliba, Katy, Dewar, Robin L, Teitelbaum, Marc, Natarajan, Ven, Laverdure, Sylvain, Highbarger, Helene C, Rehman, M Tauseef, Vogel, Susan, Vallée, David, Crew, Page, Atri, Negin, Schechner, Adam J, Pett, Sarah, Hudson, Fleur, Badrock, Jonathan, Touloumi, Giota, Brown, Samuel M, Self, Wesley H, North, Crystal M, Ginde, Adit A, Chang, Christina C, Kelleher, Anthony, Nagy-Agren, Stephanie, Vasudeva, Shikha, Looney, David, Nguyen, Hien H, Sánchez, Adriana, Weintrob, Amy C, Grund, Birgit, Sharma, Shweta, Reilly, Cavan S, Paredes, Roger, Bednarska, Agnieszka, Gerry, Norman P, Babiker, Abdel G, Davey, Victoria J, Gelijns, Annetine C, Higgs, Elizabeth S, Kan, Virginia, Matthews, Gail, and Thompson, B Taylor

Subjects
Clinical Research, Clinical Trials and Supportive Activities, Lung, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Good Health and Well Being, Adult, Designed Ankyrin Repeat Proteins, Double-Blind Method, Humans, Recombinant Fusion Proteins, SARS-CoV-2, Treatment Outcome, COVID-19 Drug Treatment, ACTIV-3/TICO Study Group, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundEnsovibep (MP0420) is a designed ankyrin repeat protein, a novel class of engineered proteins, under investigation as a treatment of SARS-CoV-2 infection.ObjectiveTo investigate if ensovibep, in addition to remdesivir and other standard care, improves clinical outcomes among patients hospitalized with COVID-19 compared with standard care alone.DesignDouble-blind, randomized, placebo-controlled, clinical trial. (ClinicalTrials.gov: NCT04501978).SettingMultinational, multicenter trial.ParticipantsAdults hospitalized with COVID-19.InterventionIntravenous ensovibep, 600 mg, or placebo.MeasurementsEnsovibep was assessed for early futility on the basis of pulmonary ordinal scores at day 5. The primary outcome was time to sustained recovery through day 90, defined as 14 consecutive days at home or place of usual residence after hospital discharge. A composite safety outcome that included death, serious adverse events, end-organ disease, and serious infections was assessed through day 90.ResultsAn independent data and safety monitoring board recommended that enrollment be halted for early futility after 485 patients were randomly assigned and received an infusion of ensovibep (n = 247) or placebo (n = 238). The odds ratio (OR) for a more favorable pulmonary outcome in the ensovibep (vs. placebo) group at day 5 was 0.93 (95% CI, 0.67 to 1.30; P = 0.68; OR > 1 would favor ensovibep). The 90-day cumulative incidence of sustained recovery was 82% for ensovibep and 80% for placebo (subhazard ratio [sHR], 1.06 [CI, 0.88 to 1.28]; sHR > 1 would favor ensovibep). The primary composite safety outcome at day 90 occurred in 78 ensovibep participants (32%) and 70 placebo participants (29%) (HR, 1.07 [CI, 0.77 to 1.47]; HR

Published
2022

5. Practical Guidance on Modeling Choices for the Virtual Twins Method

Author

Deng, Chuyu, Vock, David M., Carroll, Dana M., Boatman, Jeffrey A., Hatsukami, Dorothy K., Leng, Ning, and Koopmeiners, Joseph S.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method by Foster et al. \cite{Foster} is a highly cited and implemented method for subgroup identification because of its intuitive framework. However, since its initial publication, many researchers still rely heavily on the authors' initial modeling suggestions without examining newer and more powerful alternatives. This leaves much of the potential of the method untapped. We comprehensively evaluate the performance of VT with different combinations of methods in each of its component steps, under a collection of linear and nonlinear problem settings. Our simulations show that the method choice for step 1 of VT is highly influential in the overall accuracy of the method, and Superlearner is a promising choice. We illustrate our findings by using VT to identify subgroups with heterogeneous treatment effects in a randomized, double-blind nicotine reduction trial.

Published
2021

6. Efficacy and safety of two neutralising monoclonal antibody therapies, sotrovimab and BRII-196 plus BRII-198, for adults hospitalised with COVID-19 (TICO): a randomised controlled trial

Author

Group, ACTIV-3 Therapeutics for Inpatients with COVID-19 Study, Self, Wesley H, Sandkovsky, Uriel, Reilly, Cavan S, Vock, David M, Gottlieb, Robert L, Mack, Michael, Golden, Kevin, Dishner, Emma, Vekstein, Andrew, Ko, Emily R, Der, Tatyana, Franzone, John, Almasri, Eyad, Fayed, Mohamed, Filbin, Michael R, Hibbert, Kathryn A, Rice, Todd W, Casey, Jonathan D, Hayanga, J Awori, Badhwar, Vinay, Leshnower, Bradley G, Sharifpour, Milad, Knowlton, Kirk U, Peltan, Ithan D, Bakowska, Elizieta, Kowalska, Justyna, Bowdish, Michael E, Sturek, Jeffrey M, Rogers, Angela J, Files, D Clark, Mosier, Jarrod M, Gong, Michelle N, Douin, David J, Hite, R Duncan, Trautner, Barbara W, Jain, Mamta K, Gardner, Edward M, Khan, Akram, Jensen, Jens-Ulrik, Matthay, Michael A, Ginde, Adit A, Brown, Samuel M, Higgs, Elizabeth S, Pett, Sarah, Weintrob, Amy C, Chang, Christina C, Murrary, Daniel D, Günthard, Huldrych F, Moquete, Ellen, Grandits, Greg, Engen, Nicole, Grund, Birgit, Sharma, Shweta, Cao, Huyen, Gupta, Rajesh, Osei, Suzette, Margolis, David, Zhu, Qing, Polizzotto, Mark N, Babiker, Abdel G, Davey, Victoria J, Kan, Virginia, Thompson, B Taylor, Gelijns, Annetine C, Neaton, James D, Lane, H Clifford, Jundgren, Jens D, Tierney, John, Barrett, Kevin, Herpin, Betsey R, Smolskis, Mary C, Voge, Susan E, McNay, Laura A, Cahill, Kelly, Crew, Page, Kirchoff, Matthew, Sardana, Ratna, Raim, Sharon Segal, Chiu, Joseph, Hensley, Lisa, Lorenzo, Josua, Mock, Rebecca, Shaw-Saliba, Katy, Zuckerman, Judith, Adam, Stacey J, Currier, Judy, Read, Sarah, Hughes, Eric, Amos, Laura, Carlsen, Amy, Carter, Anita, Davis, Bionca, Denning, Eileen, DuChene, Alain, Harrison, Merrie, Kaiser, Payton, Koopmeiners, Joseph, Meger, Sue, and Murray, Thomas

Subjects
Clinical Trials and Supportive Activities, Lung, Clinical Research, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Good Health and Well Being, Adolescent, Adult, Aged, Antibodies, Monoclonal, Antibodies, Monoclonal, Humanized, Antibodies, Neutralizing, Double-Blind Method, Female, Humans, Male, Middle Aged, SARS-CoV-2, Treatment Outcome, COVID-19 Drug Treatment, ACTIV-3/Therapeutics for Inpatients with COVID-19 (TICO) Study Group, Clinical Sciences, Medical Microbiology, Public Health and Health Services, Microbiology
Abstract

BackgroundWe aimed to assess the efficacy and safety of two neutralising monoclonal antibody therapies (sotrovimab [Vir Biotechnology and GlaxoSmithKline] and BRII-196 plus BRII-198 [Brii Biosciences]) for adults admitted to hospital for COVID-19 (hereafter referred to as hospitalised) with COVID-19.MethodsIn this multinational, double-blind, randomised, placebo-controlled, clinical trial (Therapeutics for Inpatients with COVID-19 [TICO]), adults (aged ≥18 years) hospitalised with COVID-19 at 43 hospitals in the USA, Denmark, Switzerland, and Poland were recruited. Patients were eligible if they had laboratory-confirmed SARS-CoV-2 infection and COVID-19 symptoms for up to 12 days. Using a web-based application, participants were randomly assigned (2:1:2:1), stratified by trial site pharmacy, to sotrovimab 500 mg, matching placebo for sotrovimab, BRII-196 1000 mg plus BRII-198 1000 mg, or matching placebo for BRII-196 plus BRII-198, in addition to standard of care. Each study product was administered as a single dose given intravenously over 60 min. The concurrent placebo groups were pooled for analyses. The primary outcome was time to sustained clinical recovery, defined as discharge from the hospital to home and remaining at home for 14 consecutive days, up to day 90 after randomisation. Interim futility analyses were based on two seven-category ordinal outcome scales on day 5 that measured pulmonary status and extrapulmonary complications of COVID-19. The safety outcome was a composite of death, serious adverse events, incident organ failure, and serious coinfection up to day 90 after randomisation. Efficacy and safety outcomes were assessed in the modified intention-to-treat population, defined as all patients randomly assigned to treatment who started the study infusion. This study is registered with ClinicalTrials.gov, NCT04501978.FindingsBetween Dec 16, 2020, and March 1, 2021, 546 patients were enrolled and randomly assigned to sotrovimab (n=184), BRII-196 plus BRII-198 (n=183), or placebo (n=179), of whom 536 received part or all of their assigned study drug (sotrovimab n=182, BRII-196 plus BRII-198 n=176, or placebo n=178; median age of 60 years [IQR 50-72], 228 [43%] patients were female and 308 [57%] were male). At this point, enrolment was halted on the basis of the interim futility analysis. At day 5, neither the sotrovimab group nor the BRII-196 plus BRII-198 group had significantly higher odds of more favourable outcomes than the placebo group on either the pulmonary scale (adjusted odds ratio sotrovimab 1·07 [95% CI 0·74-1·56]; BRII-196 plus BRII-198 0·98 [95% CI 0·67-1·43]) or the pulmonary-plus complications scale (sotrovimab 1·08 [0·74-1·58]; BRII-196 plus BRII-198 1·00 [0·68-1·46]). By day 90, sustained clinical recovery was seen in 151 (85%) patients in the placebo group compared with 160 (88%) in the sotrovimab group (adjusted rate ratio 1·12 [95% CI 0·91-1·37]) and 155 (88%) in the BRII-196 plus BRII-198 group (1·08 [0·88-1·32]). The composite safety outcome up to day 90 was met by 48 (27%) patients in the placebo group, 42 (23%) in the sotrovimab group, and 45 (26%) in the BRII-196 plus BRII-198 group. 13 (7%) patients in the placebo group, 14 (8%) in the sotrovimab group, and 15 (9%) in the BRII-196 plus BRII-198 group died up to day 90.InterpretationNeither sotrovimab nor BRII-196 plus BRII-198 showed efficacy for improving clinical outcomes among adults hospitalised with COVID-19.FundingUS National Institutes of Health and Operation Warp Speed.

Published
2022

7. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
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9. Hyperimmune immunoglobulin for hospitalised patients with COVID-19 (ITAC): a double-blind, placebo-controlled, phase 3, randomised trial

Author

Group, The ITAC Study, Polizzotto, Mark N, Nordwall, Jacqueline, Babiker, Abdel G, Phillips, Andrew, Vock, David M, Eriobu, Nnakelu, Kwaghe, Vivian, Paredes, Roger, Mateu, Lourdes, Ramachandruni, Srikanth, Narang, Rajeev, Jain, Mamta K, Lazarte, Susana M, Baker, Jason V, Frosch, Anne EP, Poulakou, Garyfallia, Syrigos, Konstantinos N, Arnoczy, Gretchen S, McBride, Natalie A, Robinson, Philip A, Sarafian, Farjad, Bhagani, Sanjay, Taha, Hassan S, Benfield, Thomas, Liu, Sean TH, Antoniadou, Anastasia, Jensen, Jens Ulrik Stæhr, Kalomenidis, Ioannis, Susilo, Adityo, Hariadi, Prasetyo, Jensen, Tomas O, Morales-Rull, Jose Luis, Helleberg, Marie, Meegada, Sreenath, Johansen, Isik S, Canario, Daniel, Fernández-Cruz, Eduardo, Metallidis, Simeon, Shah, Amish, Sakurai, Aki, Koulouris, Nikolaos G, Trotman, Robin, Weintrob, Amy C, Podlekareva, Daria, Hadi, Usman, Lloyd, Kathryn M, Røge, Birgit Thorup, Saito, Sho, Sweerus, Kelly, Malin, Jakob J, Lübbert, Christoph, Muñoz, Jose, Cummings, Matthew J, Losso, Marcelo H, Turner, Dan, Shaw-Saliba, Kathryn, Dewar, Robin, Highbarger, Helene, Lallemand, Perrine, Rehman, Tauseef, Gerry, Norman, Arlinda, Dona, Chang, Christina C, Grund, Birgit, Holbrook, Michael R, Holley, Horace P, Hudson, Fleur, McNay, Laura A, Murray, Daniel D, Pett, Sarah L, Shaughnessy, Megan, Smolskis, Mary C, Touloumi, Giota, Wright, Mary E, Doyle, Mittie K, Popik, Sharon, Hall, Christine, Ramanathan, Roshan, Cao, Huyen, Mondou, Elsa, Willis, Todd, Thakuria, Joseph V, Yel, Leman, Higgs, Elizabeth, Kan, Virginia L, Lundgren, Jens D, Neaton, James D, and Lane, H Clifford

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Emerging Infectious Diseases, Clinical Research, Vaccine Related, Clinical Trials and Supportive Activities, Lung, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Adenosine Monophosphate, Alanine, Antibodies, Neutralizing, Antiviral Agents, COVID-19, COVID-19 Vaccines, Double-Blind Method, Female, Hospitalization, Humans, Inpatients, Internationality, Male, Middle Aged, Treatment Outcome, Vaccines, Inactivated, ITAC (INSIGHT 013) Study Group, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundPassive immunotherapy using hyperimmune intravenous immunoglobulin (hIVIG) to SARS-CoV-2, derived from recovered donors, is a potential rapidly available, specific therapy for an outbreak infection such as SARS-CoV-2. Findings from randomised clinical trials of hIVIG for the treatment of COVID-19 are limited.MethodsIn this international randomised, double-blind, placebo-controlled trial, hospitalised patients with COVID-19 who had been symptomatic for up to 12 days and did not have acute end-organ failure were randomly assigned (1:1) to receive either hIVIG or an equivalent volume of saline as placebo, in addition to remdesivir, when not contraindicated, and other standard clinical care. Randomisation was stratified by site pharmacy; schedules were prepared using a mass-weighted urn design. Infusions were prepared and masked by trial pharmacists; all other investigators, research staff, and trial participants were masked to group allocation. Follow-up was for 28 days. The primary outcome was measured at day 7 by a seven-category ordinal endpoint that considered pulmonary status and extrapulmonary complications and ranged from no limiting symptoms to death. Deaths and adverse events, including organ failure and serious infections, were used to define composite safety outcomes at days 7 and 28. Prespecified subgroup analyses were carried out for efficacy and safety outcomes by duration of symptoms, the presence of anti-spike neutralising antibodies, and other baseline factors. Analyses were done on a modified intention-to-treat (mITT) population, which included all randomly assigned participants who met eligibility criteria and received all or part of the assigned study product infusion. This study is registered with ClinicalTrials.gov, NCT04546581.FindingsFrom Oct 8, 2020, to Feb 10, 2021, 593 participants (n=301 hIVIG, n=292 placebo) were enrolled at 63 sites in 11 countries; 579 patients were included in the mITT analysis. Compared with placebo, the hIVIG group did not have significantly greater odds of a more favourable outcome at day 7; the adjusted OR was 1·06 (95% CI 0·77-1·45; p=0·72). Infusions were well tolerated, although infusion reactions were more common in the hIVIG group (18·6% vs 9·5% for placebo; p=0·002). The percentage with the composite safety outcome at day 7 was similar for the hIVIG (24%) and placebo groups (25%; OR 0·98, 95% CI 0·66-1·46; p=0·91). The ORs for the day 7 ordinal outcome did not vary for subgroups considered, but there was evidence of heterogeneity of the treatment effect for the day 7 composite safety outcome: risk was greater for hIVIG compared with placebo for patients who were antibody positive (OR 2·21, 95% CI 1·14-4·29); for patients who were antibody negative, the OR was 0·51 (0·29-0·90; pinteraction=0·001).InterpretationWhen administered with standard of care including remdesivir, SARS-CoV-2 hIVIG did not demonstrate efficacy among patients hospitalised with COVID-19 without end-organ failure. The safety of hIVIG might vary by the presence of endogenous neutralising antibodies at entry.FundingUS National Institutes of Health.

Published
2022

10. Borrowing from Supplemental Sources to Estimate Causal Effects from a Primary Data Source

Author

Boatman, Jeffrey A., Vock, David M., and Koopmeiners, Joseph S.

Subjects
Statistics - Methodology
Abstract

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.

Published
2020

11. Assessing effect heterogeneity of a randomized treatment using conditional inference trees

Author

Venkatasubramaniam, Ashwini, Koch, Brandon, Erickson, Lauren, French, Simone, Vock, David, and Wolfson, Julian

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, i.e., the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. TEHTrees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.

Published
2019

15. Preliminary Outcomes from Project TRUST, a school-based intervention to promote student-school connectedness and engagement

Author

Allen, Michele, Vock, David, Hang, Mikow, Pergament, Shannon, Bigelow, Martha, Evans, Michael, and Wilhelm, April

Subjects
Health care disparities -- Demographic aspects -- Social aspects -- Economic aspects, Vulnerability (Psychology) -- Health aspects, Behavioral health care -- Methods, Teacher-student relationships -- Analysis, Health, Science and technology
Abstract

Context: School connectedness and engagement are key proximal social determinants of adolescent health that are associated with improved behavioral health including lower levels of substance use, improved emotional well-being, and higher academic achievement. These determinants are less accessible to Black, Indigenous, and People of Color (BIPOC) students due to structural barriers at the school level, with long-term implications for health. Objective: Assess the effectiveness of Project TRUST, a multicomponent school-based intervention, at addressing student- school connectedness and engagement. Study Design TRUST was a controlled trial that utilized participatory research approaches. The multicomponent intervention focused on school environment included: 1) Training to build teacher-student relationships and promote positive youth development, 2) Youth-defined school policy, practice, or procedure change to promote connectedness, and 3) Parent- defined school policy, practice, or procedure change to promote parent and community connection. Primary outcomes were student-school connectedness and engagement assessed through longitudinal online student surveys between 2017 and 2019. Five diverse middle and high schools were assigned to Wave 1 intervention implementation at study onset and five to delayed-control Wave 2 condition two years later. We used a novel weighting algorithm to mitigate the lack of randomization and potential imbalances across conditions. Setting: A large U.S. Midwestern school district. Participants: 809 6th and 9th grade students. Results: Participants were predominately female (53%), BIPOC (74%) and included 16% English language learners. School connectedness and engagement declined for all students from baseline to follow up (school connectedness, 3.15 to 3.02; school engagement, 3.22 to 3.07). No overall significant intervention effect was noted in school connectedness (difference in change 0.03 (95% CI -0.11, 0.17)) or school engagement (0.06 (-0.02, 0.14)). However Wave 1 BIPOC students maintained their level of school engagement compared to Wave 2 where it dropped (difference in change 0.109 (0.005, 0.214)). Conclusions: A multicomponent participatory intervention maintained school engagement for BIPOC students, emphasizing the importance of school environment for BIPOC student health in particular. Social determinants of health focused interventions are important for addressing health inequities and promoting population health., Presenters Michele Allen, MD, MS, MS, David Vock, PhD, Mikow Hang, Shannon Pergament, MPH, MSW, Martha Bigelow, Michael Evans, April Wilhelm, MD, [...]

Published
2023

17. Estimating restricted mean treatment effects with stacked survival models

Author

Wey, Andrew, Vock, David, Connett, John, and Rudser, Kyle

Subjects
Statistics - Applications
Abstract

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is to estimate a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing over the covariate distribution. We demonstrate that the mean-squared error of the restricted mean difference is bounded by the mean-squared error of the covariate-adjusted survival distribution estimators. This implies that a better estimator of the covariate-adjusted survival distributions is associated with a better estimator of the restricted mean difference. Thus, this paper proposes estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric and semi-parametric models, stacked survival models can effectively estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that the new estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. The proposed estimator is also illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large and small-volume centers.

Published
2014

18. Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data

Author

Bandyopadhyay, Sunayan, Wolfson, Julian, Vock, David M., Vazquez-Benitez, Gabriela, Adomavicius, Gediminas, Elidrisi, Mohamed, Johnson, Paul E., and O'Connor, Patrick J.

Subjects
Statistics - Machine Learning, Statistics - Applications
Abstract

Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restricts the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and cardiovascular event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a cardiovascular event within five years. In such data, event status may be unknown for some individuals as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modelling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with {\em{ad hoc}} approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large U.S. Midwestern health care system., Comment: 31 pages (including references), 6 figures

Published
2014

21. Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

Author

Christopherson, Kaitlin M., Ghosh, Alokananda, Mohamed, Abdallah Sherif Radwan, Kamal, Mona, Gunn, G. Brandon, Dale, Timothy, Kalpathy-Cramer, Jayashree, Messer, Jay, Garden, Adam S., Elhalawani, Hesham, Frank, Steven J., Lewin, Jan, Morrison, William H., Phan, Jack, Gross, Neil, Ferrarotto, Renata, Weber, Randal S., Rosenthal, David I., Lai, Stephen Y., Hutcheson, Katherine, Fuller, Clifton David, Radwan Mohamed, Abdallah Sherif, Marai, G. Elisabeta (Liz), Canahuate, Guadalupe, Vock, David M., and Fuller, David

Published
2019
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27. A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter?

Author

Ng, Sweet Ping, Dyer, Brandon A., Kalpathy-Cramer, Jayashree, Mohamed, Abdallah Sherif Radwan, Awan, Musaddiq J., Gunn, G. Brandon, Phan, Jack, Zafereo, Mark, Debnam, J. Matthew, Lewis, Carol M., Colen, Rivka R., Kupferman, Michael E., Guha-Thakurta, Nandita, Canahuate, Guadalupe, Marai, G. Elisabeta, Vock, David, Hamilton, Bronwyn, Holland, John, Cardenas, Carlos E., Lai, Stephen, Rosenthal, David, and Fuller, Clifton David

Published
2018
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31. Abstract P174: The Association Between Infection-Related Hospitalization and Incident Heart Failure: The Atherosclerosis Risk in Communities Study

Author

Molinsky, Rebecca L, primary, Shah, Amil M, additional, Yuzefpolskaya, Melana, additional, Yu, Bing, additional, Misialek, Jeffrey, additional, Bohn, Bruno, additional, Vock, David, additional, Maclehose, Richard, additional, Colombo, Paolo C, additional, Ndumele, Chiadi E, additional, Ishigami, Junichi, additional, Matsushita, Kuni, additional, Lutsey, Pamela L, additional, and Demmer, Ryan, additional

Published
2023
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36. Data carve out in the midst of the COVID-19 pandemic

Author

Mannon, Roslyn B., primary, Khush, Kiran K., additional, Mohan, Sumit, additional, Vock, David M., additional, Knight, Richard, additional, Pittman, James, additional, Zinner, Christopher, additional, and Orlowski, Jeffrey P., additional

Published
2022
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40. Efficacy and Safety of Ensovibep for Adults Hospitalized With COVID-19 : A Randomized Controlled Trial

Author

ACTIV-3/TICO Study Group, Barkauskas, Christina, Mylonakis, Eleftherios, Poulakou, Garyfallia, Young, Barnaby E, Vock, David M, Siegel, Lianne, Engen, Nicole, Grandits, Greg, Mosaly, Nilima R, Vekstein, Andrew M, Rogers, Ralph, Shehadeh, Fadi, Kaczynski, Matthew, Mylona, Evangelia K, Syrigos, Konstantinos N, Rapti, Vasiliki, Lye, David C, Hui, Diong Shiau, Leither, Lindsay, Knowlton, Kirk U, Jain, Mamta K, Marines-Price, Rubria, Osuji, Alice, Overcash, J Scott, Kalomenidis, Ioannis, Barmparessou, Zafeiria, Waters, Michael, Zepeda, Karla, Chen, Peter, Torbati, Sam, Kiweewa, Francis, Sebudde, Nicholus, Almasri, Eyad, Hughes, Alyssa, Bhagani, Sanjay R, Rodger, Alison, Sandkovsky, Uriel, Gottlieb, Robert L, Nnakelu, Eriobu, Trautner, Barbara, Menon, Vidya, Lutaakome, Joseph, Matthay, Michael, Robinson, Philip, Protopapas, Konstantinos, Koulouris, Nikolaos, Kimuli, Ivan, Baduashvili, Amiran, Braun, Dominique L, Günthard, Huldrych F, Ramachandruni, Srikanth, Kidega, Robert, Kim, Kami, Hatlen, Timothy J, Phillips, Andrew N, Murray, Daniel D, Jensen, Tomas O, Padilla, Maria L, Accardi, Evan X, Shaw-Saliba, Katy, Dewar, Robin L, Teitelbaum, Marc, Natarajan, Ven, Laverdure, Sylvain, Highbarger, Helene C, Rehman, M Tauseef, Vogel, Susan, Vallée, David, Crew, Page, Atri, Negin, Schechner, Adam J, Pett, Sarah, Hudson, Fleur, Badrock, Jonathan, Touloumi, Giota, Brown, Samuel M, Self, Wesley H, North, Crystal M, Ginde, Adit A, Chang, Christina C, Kelleher, Anthony, Nagy-Agren, Stephanie, Vasudeva, Shikha, Looney, David, Nguyen, Hien H, Sánchez, Adriana, Weintrob, Amy C, Grund, Birgit, Sharma, Shweta, Reilly, Cavan S, Paredes, Roger, Bednarska, Agnieszka, Gerry, Norman P, Babiker, Abdel G, Davey, Victoria J, Gelijns, Annetine C, Higgs, Elizabeth S, Kan, Virginia, and Matthews, Gail

Subjects
Adult, SARS-CoV-2, Recombinant Fusion Proteins, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, Medical and Health Sciences, COVID-19 Drug Treatment, Treatment Outcome, Good Health and Well Being, Double-Blind Method, Clinical Research, 6.1 Pharmaceuticals, General & Internal Medicine, Humans, Designed Ankyrin Repeat Proteins, ACTIV-3/TICO Study Group, Lung
Abstract

BackgroundEnsovibep (MP0420) is a designed ankyrin repeat protein, a novel class of engineered proteins, under investigation as a treatment of SARS-CoV-2 infection.ObjectiveTo investigate if ensovibep, in addition to remdesivir and other standard care, improves clinical outcomes among patients hospitalized with COVID-19 compared with standard care alone.DesignDouble-blind, randomized, placebo-controlled, clinical trial. (ClinicalTrials.gov: NCT04501978).SettingMultinational, multicenter trial.ParticipantsAdults hospitalized with COVID-19.InterventionIntravenous ensovibep, 600 mg, or placebo.MeasurementsEnsovibep was assessed for early futility on the basis of pulmonary ordinal scores at day 5. The primary outcome was time to sustained recovery through day 90, defined as 14 consecutive days at home or place of usual residence after hospital discharge. A composite safety outcome that included death, serious adverse events, end-organ disease, and serious infections was assessed through day 90.ResultsAn independent data and safety monitoring board recommended that enrollment be halted for early futility after 485 patients were randomly assigned and received an infusion of ensovibep (n=247) or placebo (n=238). The odds ratio (OR) for a more favorable pulmonary outcome in the ensovibep (vs. placebo) group at day 5 was 0.93 (95% CI, 0.67 to 1.30; P=0.68; OR>1 would favor ensovibep). The 90-day cumulative incidence of sustained recovery was 82% for ensovibep and 80% for placebo (subhazard ratio [sHR], 1.06 [CI, 0.88 to 1.28]; sHR>1 would favor ensovibep). The primary composite safety outcome at day 90 occurred in 78 ensovibep participants (32%) and 70 placebo participants (29%) (HR, 1.07 [CI, 0.77 to 1.47]; HR&nbsp

Published
2022

41. Lung Cancer Screening and Smoking Cessation Clinical Trials. SCALE (Smoking Cessation within the Context of Lung Cancer Screening) Collaboration

Author

Joseph, Anne M., Rothman, Alexander J., Almirall, Daniel, Begnaud, Abbie, Chiles, Caroline, Cinciripini, Paul M., Fu, Steven S., Graham, Amanda L., Lindgren, Bruce R., Melzer, Anne C., Ostroff, Jamie S., Seaman, Elizabeth L., Taylor, Kathryn L., Toll, Benjamin A., Zeliadt, Steven B., and Vock, David M.

Published
2018
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43. Effect of Integrating Access to a Prescription Drug Monitoring Program Within the Electronic Health Record on the Frequency of Queries by Primary Care Clinicians

Author

Neprash, Hannah T., primary, Vock, David M., additional, Hanson, Alexandra, additional, Elert, Brent, additional, Short, Sonja, additional, Karaca-Mandic, Pinar, additional, Rothman, Alexander J., additional, Melton, Genevieve B., additional, Satin, David, additional, Markowitz, Rebecca, additional, and Golberstein, Ezra, additional

Published
2022
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44. Efficacy and safety of two neutralising monoclonal antibody therapies, sotrovimab and BRII-196 plus BRII-198, for adults hospitalised with COVID-19 (TICO):a randomised controlled trial

Author

Self, Wesley H., Sandkovsky, Uriel, Reilly, Cavan S., Vock, David M., Gottlieb, Robert L., Mack, Michael, Golden, Kevin, Dishner, Emma, Vekstein, Andrew, Ko, Emily R., Der, Tatyana, Franzone, John, Almasri, Eyad, Fayed, Mohamed, Filbin, Michael R., Hibbert, Kathryn A., Rice, Todd W., Casey, Jonathan D., Hayanga, J. Awori, Badhwar, Vinay, Leshnower, Bradley G., Sharifpour, Milad, Knowlton, Kirk U., Peltan, Ithan D., Bakowska, Elizieta, Kowalska, Justyna, Bowdish, Michael E., Sturek, Jeffrey M., Rogers, Angela J., Files, D. Clark, Mosier, Jarrod M., Gong, Michelle N., Douin, David J., Hite, R. Duncan, Trautner, Barbara W., Jain, Mamta K., Jensen, Jens Ulrik, Clausen, Clara Lundetoft, Hovmand, Nichlas, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Rastoder, Ema, Helleberg, Marie, Gerstoft, Jan, Lindegaard, Birgitte, Pedersen, Thomas Ingemann, Johnsen, Stine, Wiese, Lothar, Knudsen, Lene Surland, Self, Wesley H., Sandkovsky, Uriel, Reilly, Cavan S., Vock, David M., Gottlieb, Robert L., Mack, Michael, Golden, Kevin, Dishner, Emma, Vekstein, Andrew, Ko, Emily R., Der, Tatyana, Franzone, John, Almasri, Eyad, Fayed, Mohamed, Filbin, Michael R., Hibbert, Kathryn A., Rice, Todd W., Casey, Jonathan D., Hayanga, J. Awori, Badhwar, Vinay, Leshnower, Bradley G., Sharifpour, Milad, Knowlton, Kirk U., Peltan, Ithan D., Bakowska, Elizieta, Kowalska, Justyna, Bowdish, Michael E., Sturek, Jeffrey M., Rogers, Angela J., Files, D. Clark, Mosier, Jarrod M., Gong, Michelle N., Douin, David J., Hite, R. Duncan, Trautner, Barbara W., Jain, Mamta K., Jensen, Jens Ulrik, Clausen, Clara Lundetoft, Hovmand, Nichlas, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Rastoder, Ema, Helleberg, Marie, Gerstoft, Jan, Lindegaard, Birgitte, Pedersen, Thomas Ingemann, Johnsen, Stine, Wiese, Lothar, and Knudsen, Lene Surland

Abstract

Background: We aimed to assess the efficacy and safety of two neutralising monoclonal antibody therapies (sotrovimab [Vir Biotechnology and GlaxoSmithKline] and BRII-196 plus BRII-198 [Brii Biosciences]) for adults admitted to hospital for COVID-19 (hereafter referred to as hospitalised) with COVID-19. Methods: In this multinational, double-blind, randomised, placebo-controlled, clinical trial (Therapeutics for Inpatients with COVID-19 [TICO]), adults (aged ≥18 years) hospitalised with COVID-19 at 43 hospitals in the USA, Denmark, Switzerland, and Poland were recruited. Patients were eligible if they had laboratory-confirmed SARS-CoV-2 infection and COVID-19 symptoms for up to 12 days. Using a web-based application, participants were randomly assigned (2:1:2:1), stratified by trial site pharmacy, to sotrovimab 500 mg, matching placebo for sotrovimab, BRII-196 1000 mg plus BRII-198 1000 mg, or matching placebo for BRII-196 plus BRII-198, in addition to standard of care. Each study product was administered as a single dose given intravenously over 60 min. The concurrent placebo groups were pooled for analyses. The primary outcome was time to sustained clinical recovery, defined as discharge from the hospital to home and remaining at home for 14 consecutive days, up to day 90 after randomisation. Interim futility analyses were based on two seven-category ordinal outcome scales on day 5 that measured pulmonary status and extrapulmonary complications of COVID-19. The safety outcome was a composite of death, serious adverse events, incident organ failure, and serious coinfection up to day 90 after randomisation. Efficacy and safety outcomes were assessed in the modified intention-to-treat population, defined as all patients randomly assigned to treatment who started the study infusion. This study is registered with ClinicalTrials.gov, NCT04501978. Findings: Between Dec 16, 2020, and March 1, 2021, 546 patients were enrolled and randomly assigned to sotrovimab (n=184)

Published
2022

45. Hyperimmune immunoglobulin for hospitalised patients with COVID-19 (ITAC):a double-blind, placebo-controlled, phase 3, randomised trial

Author

Polizzotto, Mark N., Nordwall, Jacqueline, Babiker, Abdel G., Phillips, Andrew, Vock, David M., Eriobu, Nnakelu, Kwaghe, Vivian, Paredes, Roger, Mateu, Lourdes, Ramachandruni, Srikanth, Narang, Rajeev, Jain, Mamta K., Lazarte, Susana M., Baker, Jason V., Frosch, Anne E.P., Poulakou, Garyfallia, Syrigos, Konstantinos N., Arnoczy, Gretchen S., McBride, Natalie A., Robinson, Philip A., Sarafian, Farjad, Bhagani, Sanjay, Taha, Hassan S., Benfield, Thomas, Liu, Sean T.H., Antoniadou, Anastasia, Jensen, Jens Ulrik Stæhr, Kalomenidis, Ioannis, Susilo, Adityo, Hariadi, Prasetyo, Jensen MD, Tomas O., Morales-Rull, Jose Luis, Helleberg, Marie, Meegada, Sreenath, Johansen, Isik S., Canario, Daniel, Fernández-Cruz, Eduardo, Metallidis, Simeon, Shah, Amish, Sakurai, Aki, Koulouris, Nikolaos G., Trotman, Robin, Weintrob, Amy C., Podlekareva, Daria, Hadi, Usman, Lloyd, Kathryn M., Røge, Birgit Thorup, Saito, Sho, Muñoz, Jose, Lundgren, Jens D., Polizzotto, Mark N., Nordwall, Jacqueline, Babiker, Abdel G., Phillips, Andrew, Vock, David M., Eriobu, Nnakelu, Kwaghe, Vivian, Paredes, Roger, Mateu, Lourdes, Ramachandruni, Srikanth, Narang, Rajeev, Jain, Mamta K., Lazarte, Susana M., Baker, Jason V., Frosch, Anne E.P., Poulakou, Garyfallia, Syrigos, Konstantinos N., Arnoczy, Gretchen S., McBride, Natalie A., Robinson, Philip A., Sarafian, Farjad, Bhagani, Sanjay, Taha, Hassan S., Benfield, Thomas, Liu, Sean T.H., Antoniadou, Anastasia, Jensen, Jens Ulrik Stæhr, Kalomenidis, Ioannis, Susilo, Adityo, Hariadi, Prasetyo, Jensen MD, Tomas O., Morales-Rull, Jose Luis, Helleberg, Marie, Meegada, Sreenath, Johansen, Isik S., Canario, Daniel, Fernández-Cruz, Eduardo, Metallidis, Simeon, Shah, Amish, Sakurai, Aki, Koulouris, Nikolaos G., Trotman, Robin, Weintrob, Amy C., Podlekareva, Daria, Hadi, Usman, Lloyd, Kathryn M., Røge, Birgit Thorup, Saito, Sho, Muñoz, Jose, and Lundgren, Jens D.

Abstract

Background: Passive immunotherapy using hyperimmune intravenous immunoglobulin (hIVIG) to SARS-CoV-2, derived from recovered donors, is a potential rapidly available, specific therapy for an outbreak infection such as SARS-CoV-2. Findings from randomised clinical trials of hIVIG for the treatment of COVID-19 are limited. Methods: In this international randomised, double-blind, placebo-controlled trial, hospitalised patients with COVID-19 who had been symptomatic for up to 12 days and did not have acute end-organ failure were randomly assigned (1:1) to receive either hIVIG or an equivalent volume of saline as placebo, in addition to remdesivir, when not contraindicated, and other standard clinical care. Randomisation was stratified by site pharmacy; schedules were prepared using a mass-weighted urn design. Infusions were prepared and masked by trial pharmacists; all other investigators, research staff, and trial participants were masked to group allocation. Follow-up was for 28 days. The primary outcome was measured at day 7 by a seven-category ordinal endpoint that considered pulmonary status and extrapulmonary complications and ranged from no limiting symptoms to death. Deaths and adverse events, including organ failure and serious infections, were used to define composite safety outcomes at days 7 and 28. Prespecified subgroup analyses were carried out for efficacy and safety outcomes by duration of symptoms, the presence of anti-spike neutralising antibodies, and other baseline factors. Analyses were done on a modified intention-to-treat (mITT) population, which included all randomly assigned participants who met eligibility criteria and received all or part of the assigned study product infusion. This study is registered with ClinicalTrials.gov, NCT04546581. Findings: From Oct 8, 2020, to Feb 10, 2021, 593 participants (n=301 hIVIG, n=292 placebo) were enrolled at 63 sites in 11 countries; 579 patients were included in the mITT analysis. Compared with placebo

Published
2022

47. Premature Death in Kidney Transplant Recipients: The Time for Trials is Now

Author

Vinson, Amanda J., primary, Singh, Sunita, additional, Chadban, Steven, additional, Cherney, David, additional, Gaber, Osama, additional, Gill, John S., additional, Helgeson, Erika, additional, Herzog, Charles A., additional, Jardine, Meg, additional, Jha, Vivekanand, additional, Kasiske, Bertram L., additional, Mannon, Roslyn B., additional, Michos, Erin D., additional, Mottl, Amy K., additional, Newby, Kristin, additional, Roy-Chaudhury, Prabir, additional, Sawinski, Deirdre, additional, Sharif, Adnan, additional, Sridhar, Vikas S., additional, Tuttle, Katherine R., additional, Vock, David M., additional, and Matas, Arthur, additional

Published
2022
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48. Interleukin-28B Polymorphism Improves Viral Kinetics and Is the Strongest Pretreatment Predictor of Sustained Virologic Response in Genotype 1 Hepatitis C Virus

Author

Thompson, Alexander J., Muir, Andrew J., Sulkowski, Mark S., Ge, Dongliang, Fellay, Jacques, Shianna, Kevin V., Urban, Thomas, Afdhal, Nezam H., Jacobson, Ira M., Esteban, Rafael, Poordad, Fred, Lawitz, Eric J., McCone, Jonathan, Shiffman, Mitchell L., Galler, Greg W., Lee, William M., Reindollar, Robert, King, John W., Kwo, Paul Y., Ghalib, Reem H., Freilich, Bradley, Nyberg, Lisa M., Zeuzem, Stefan, Poynard, Thierry, Vock, David M., Pieper, Karen S., Patel, Keyur, Tillmann, Hans L., Noviello, Stephanie, Koury, Kenneth, Pedicone, Lisa D., Brass, Clifford A., Albrecht, Janice K., Goldstein, David B., and McHutchison, John G.

Published
2010
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50. Accounting for model uncertainty in estimating global burden of disease/Prise en compte de l'incertitude des modeles dans l'estimation des effets globaux induits par la maladie/Consideracion de la incertidumbre del modelo al calcular la carga global de una enfermedad

Author

Vock, David M., Atchison, Elizabeth A., Legler, Julie M., McClure, David R.J., Carlyle, Jamie C., Jeavons, Elysia N., and Burton, Anthony H.

Subjects
Mortality -- Research -- Switzerland -- United States, Public health -- Research, Rotavirus infections -- Research, Health
Abstract

Objective To illustrate the effects of failing to account for model uncertainty when modelling is used to estimate the global burden of disease, with specific application to childhood deaths from rotavirus infection. Methods To estimate the global burden of rotavirus infection, different random-effects meta-analysis and meta- regression models were constructed by varying the Stratification criteria and including different combinations of covariates. Bayesian model averaging was used to combine the results across models and to provide a measure of uncertainty that reflects the choice of model and the sampling variability. Findings In the models examined, the estimated number of child deaths from rotavirus infection varied between 492 000 and 664 000. While averaging over the different models' estimates resulted in a modest increase in the estimated number of deaths (541 000 as compared with the World Health Organization's estimate of 527 000), the width of the 95% confidence interval increased from 105 000 to 198 000 deaths when model uncertainty was taken into account. Conclusion Sampling variability explains only a portion of the overall uncertainty in a modelled estimate. The uncertainty owing to both the sampling variability and the choice of model(s) should be given when disease burden results are presented. Failure to properly account for uncertainty in disease burden estimates may lead to inappropriate uses of the estimates and inaccurate prioritization of global health needs. Objectif Illustrer les effets de rabsence de prise en compte de L'incertitude des modeles quand la modelisation est utilisee pour estimer les effets globaux induits par la maladie, avec une application specifique a la mortalite infantile liee a L'infection par rotavirus. Methodes Pour estimer la charge giobale de L'infection par rotavirus, differents modeles de meta-analyse d'effet aleatoire et de meta-regression ont ete elabores en faisant varier les criteres de stratification et en incluant differentes associations de covariantes. Un modele bayesien de calcul de moyenne a ete utilise pour combiner les resultats entre les modeles et fournir une mesure de l'incertitude qui reflete la variabilite du choix du modele et de rechantillonnage. Resultats Dans les modeles examines, le nombre estime d'enfants morts par infection de rotavirus variait entre 492 000 et 664 000. Tandis que le calcul de la moyenne des estimations des differents modeles donnait une Iegere augmentation du nombre estime de morts (541 000, par comparaison aux 527 000 estimes par l'Organisation mondiale de la Sante), l'ampleur de L'intervalle de confiance de 95% passait de 105 000 a 198 000 morts Iorsque L'incertitude du modele etait prise en compte. Conclusion La variabilite de L'echantillonnage n'explique qu'une partie de l'incertitude globale dans une estimation par modele. L'incertitude liee a la fois a la variabilite de L'echantillonnage et au choix de modele(s) doit etre indiquee dans la presentation de resultats concernant les effets induits par la maladie. Ne pas prendre en compte L'incertitude comme il se doit dans les estimations des effets de la maladie peut conduire a un usage inapproprie des estimations et a une affectation erronee des priorites dans les besoins globaux de sante. Objetivo Ilustrar las consecuencias que acarrea no contemplar la incertidumbre del modelo cuando dicho modelo se emplea para calcular la carga de enfermedad global, especialmente en el caso de las muertes infantiles por una infeccion por rotavirus. Metodos Con el fin de calcular la carga global de la infeccion por rotavirus, se desarrollaron diversos modelos de metaanalisis y metarregresion de erectos aleatorios modificando los criterios de estratificacion e incluyendo diversas combinaciones de covariables. Se empleo el metodo bayesiano de estimacion de la media para combinar los resultados entre los modelos y ofrecer una medida de incertidumbre que reflejara la eleccion de modelo y la variabilidad del muestreo. Resultados En los modelos examinados, la cantidad estimada de muertes infantiles provocadas por una infeccion por rotavirus oscilo entre 492 000 y 664 000. Si bien la media de las estimaciones de los diversos modelos dio como resultado un aumento moderado en la cantidad estimada de muertes (541 000 en comparacion con la estimacion de la Organizacion Mundial de la Salud, de 527 000); la amplitud del intervalo de confianza del 95% provoco un aumento de 105 000 a 198 000 muertes cuando se tomo en consideracion la incertidumbre de los modelos. Conclusion La variabilidad del muestreo explica unicamente una parte de la incertidumbre global de una estimacion extraida empleando un modelo. La incertidumbre atribuible tanto a la variabilidad del muestreo como a la eleccion de uno o varios modelos debe indicarse cuando se presenten los resultados de carga de enfermedad. No contemplar debidamente la incertidumbre de las estimaciones en la carga de enfermedad, podria acarrear un uso inadecuado de las estimaciones y una priorizacion inexacta de las necesidades sanitarias globales., Introduction Because vital registration and surveillance data on etiology-specific causes of death are frequently unavailable, incomplete or inaccurate, statistical models are commonly used to estimate burden of disease. Mathers et [...]

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2011
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