Why Historical study used in research design?
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Historical studies are utilized in research design to enhance the efficiency and accuracy of various types of trials. Incorporating historical data can improve the operating characteristics of model-assisted designs, such as the Bayesian optimal interval (BOIN) and modified toxicity probability interval (mTPI) designs, similar to model-based designs . Furthermore, leveraging historical data in phase 2 randomized controlled trials can lead to reduced sample sizes without compromising statistical power, especially in oncology Bayesian augmented control (BAC) trials . Historical control trials, which compare experimental treatments with previously conducted controls, can better identify promising treatments in early phases by assigning all samples to the experimental arm . However, it is crucial to consider the limitations of retrospective studies, such as missing information and biases, when interpreting results and planning prospective studies .
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| Papers (5) | Insight |
|---|---|
1 Citations | Historical control studies are utilized in research design to compare new treatments with prior controls, aiding in early phase trials to identify promising treatments more effectively than randomized control trials. |
4 Citations | Historical studies are used in research design to improve efficiency by reducing sample size, enhancing power, adjusting for prognostic factors, and enabling a more scientific approach to drug development programs. |
104 Citations | Historical studies are valuable for investigating rare diseases and outcomes, guiding prospective research. However, they have limitations like missing data and biases, requiring careful interpretation and avoiding overgeneralization. |
| Historical studies are utilized in research design to analyze the evolution and context of design practices, aiding in understanding the historical panorama and trajectory of the field. | |
8 Citations | Incorporating historical data in research designs improves phase I clinical trials by enhancing efficiency, accelerating drug development, and refining operating characteristics of model-assisted designs. |
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