Advantages of a Two-Stage Randomized Trial Design to Evaluate Antimicrobial Treatment Strategies: a Simulation Study

Advantages of a Two-Stage Randomized Trial Design to Evaluate Antimicrobial Treatment Strategies: a Simulation Study | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Advantages of a Two-Stage Randomized Trial Design to Evaluate Antimicrobial Treatment Strategies: a Simulation Study View ORCID Profile Juan Gago , View ORCID Profile Christopher Boyer , View ORCID Profile Marc Lipsitch doi: https://doi.org/10.64898/2026.03.16.26347803 Juan Gago 1 Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juan Gago For correspondence: juangago{at}hsph.harvard.edu Christopher Boyer 1 Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, USA 2 Department of Medicine, Case Western Reserve School of Medicine , Cleveland, OH, USA 3 Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, OH, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Boyer Marc Lipsitch 1 Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA, USA 4 Department of Medicine (Division of Infectious Diseases), Stanford University School of Medicine; Department of Biology, Stanford University; Freeman Spogli Institute for International Studies (Center for International Security and Cooperation, CISAC) , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marc Lipsitch Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Background Antimicrobial prescribing policies affect not only treated patients but also their contacts. Two-stage randomized (2SR) designs can be used to estimate these spillover effects, yet this study design has not been widely applied to evaluate antimicrobial strategies. Methods We developed a stochastic agent-based model that simulates a hospital ward with two competing bacterial strains (drug-A-susceptible and drug-A-resistant). We used the simulation to emulate a 2SR trial: six hospital ward clusters were randomized 1:1 to either a 90/10 (90% Drug A, 10% Drug B, drug B was assumed to have no known resistance) or 50/50 treatment allocation strategy; individuals within clusters were then randomized to Drug A or Drug B following the assigned cluster-level allocation strategy. We estimated direct, indirect, total, and overall causal effects on incident infection and mortality. Sensitivity analyses varied the treatment effect, transmission rate, mortality structure, and number of clusters. Results The direct effect of drug choice showed that Drug A recipients had higher mortality (due to non-concordant treatment of resistant infections). This effect varied over time as the ward’s strain ecology diverged between strategies. There was also an indirect effect for Drug A recipients—reflecting spillover from higher resistant-strain prevalence under 90/10—but it was approximately null for Drug B recipients, whose broad-spectrum coverage insulated them from changes in the ward strain distribution. The overall effect—the policy-level comparison—showed that the 50/50 strategy reduced total mortality, but this net benefit concealed a redistribution: resistant-strain deaths decreased while susceptible-strain deaths increased, a consequence captured by the overall effect but invisible to the direct effect. These findings were qualitatively consistent across all sensitivity scenarios. Conclusions We demonstrate that antimicrobial prescribing produces spillover effects not captured by conventional individually randomized trials. These effects can substantially alter treatment outcomes in a population. We propose that the 2SR design, grounded in a formal causal framework for interference, is better suited for evaluating population-level effects of antimicrobial strategies—whether implemented as a randomized trial or emulated with observational data. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was funded by VK fund for CCDD Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Conflict of Interest: The authors declare no conflicts of interest. Funding: This work was supported by VK Fund for the CCDD. Data and Code Availability: Simulation code available. Data Availability All data produced in the present study and the code for the simulation are available upon reasonable request to the authors Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted March 19, 2026. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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