Determining the efficacy of treatment requires a wide-range consideration of the short-term and long-term effects of the treatment. Therefore, it would be necessary to measure the magnitude and precision of the treatment effect or relative benefits (Boissel et al., 2019). Figure 8.1 indicates that patients who received immediate antivirals stayed fewer days in the hospitals than patients who received antivirals after a post-laboratory confirmation of diagnosis (The Efficacy of Treatment).
In this scenario, only one measurement, hospital stay, remain applicable to determine the efficacy or mode of treatment. Other measures can be added as leaves in the decision tree to help determine for whom antiviral medications are the best choice, including adverse drug events and rehospitalization. For instance, a leaf of adverse drug events would help indicate the number of patients who developed complications or adverse events after taking the antivirals.
The Efficacy of Treatment
A leaf on readmission or rehospitalization would indicate the number of patients readmitted after initial treatment with antivirals. Increasing the number of measures would help increase the validity of the trial or treatment outcomes. In this case, administering antiviral medication to treat avian influenza remains well supported. Moreover if patients receiving the antivirals stay less in the hospital, develop minor or no adverse drug events. Hence, remain less likely for readmission (The Efficacy of Treatment).
Whether a larger data pool would help make this decision
Increasing the number of leaves increases the data pool. Although the amount of data does not indicate increased usefulness, it is essential to have more information to make a more comprehensive decision considering multiple factors. In other words, it is not entirely true that the more data you have, the more accurate the decision will be.
Still, more data would be appreciated to make the analysis more representative (Leetaru, 2019). The will and capacity to act on the extensive data pool are critical in increasing its usefulness. In this case, widening the data pool helps arrive at a better decision. Moreover, because multiple measures and factors remain considerable in the decision-making process (The Efficacy of Treatment).
References
Boissel, J., Cogny, F., Marko, N., & Boissel, F. (2019). From clinical trial efficacy to real-life effectiveness: Why conventional metrics do not work. Drugs – Real World Outcomes, 6(3), 125-132. https://doi.org/10.1007/s40801-019-0159-z
Leetaru, K. (2019, June 14). Does More Data Really Lead To Better Decision Making? Forbes. https://www.forbes.com/sites/kalevleetaru/2016/06/14/does-more-data-really-lead-to-better-decision-making/?sh=7c6893731895
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