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People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers.Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have 'disproportionate' effects in increasing the total number of signatures. The model is fully documented for further development and replications.

Objective: Ado-trastuzumab emtansine (T-DM1) was recently approved for patients with human epidermal growth factor receptor 2 positive (HER2+) early breast cancer (eBC) with residual invasive disease after neoadjuvant taxane and trastuzumab-based treatment. Cost-effectiveness analysis was conducted to compare T-DM1 versus trastuzumab in the United States. Materials and Methods:A Markov cohort-based model tracked clinical and economic outcomes over a lifetime horizon from a US payer perspective. The model included 6 health states: invasive disease-free, nonmetastatic (locoregional) recurrence, remission, first-line and second-line metastatic BC and death. Model state transitions were based on statistical extrapolation of the head-to-head KATHERINE study and published sources. Dosing and treatment duration reflected prescribing information and trials. Costs (2019 US dollars) associated with pharmaceutical treatment (wholesale acquisition costs), health state specific care, adverse events, and end-of-life care were included. Health state utilities were obtained from KATHERINE and published literature.Results: T-DM1 dominated trastuzumab, yielding lower lifetime costs (−$40,271), and higher life-years (2.980) and quality-adjusted life-years (2.336). Results were driven by patients receiving T-DM1 spending less time in more costly downstream health states, as these patients are less likely to experience a recurrence overall, despite having a higher likelihood of metastatic disease (distant recurrence) in the subset of patients who experience recurrence. Probabilistic sensitivity analysis indicated robust results, with 96.7% of 5000 stochastic simulations producing dominance for T-DM1. The most influential variables were related to treatment costs, off treatment utilities, and health state costs. Additional scenario analyses tested a range of model inputs and assumptions, and produced consistent results. Conclusion:Relative to trastuzumab, T-DM1 treatment for patients with HER2+ eBC who have residual invasive disease after neoadjuvant taxane and trastuzumab-based treatment is likely to reduce the overall financial burden of cancer, while simultaneously improving patient outcomes.