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Radiobiological models have been used to calculate the outcomes of treatment plans based on dose–volume relationship. This study examines several radiobiological models for the calculation of tumor control probability (TCP) of intensity modulated radiotherapy plans for the treatment of lung, prostate, and head and neck (H&N) cancers. Dose volume histogram (DVH) data from the intensity modulated radiotherapy plans of 36 lung, 26 prostate, and 87 H&N cases were evaluated. The Poisson, Niemierko, and Marsden models were used to calculate the TCP of each disease group treatment plan. The calculated results were analyzed for correlation and discrepancy among the three models, as well as different treatment sites under study. The median value of calculated TCP in lung plans was 61.9% (34.1–76.5%), 59.5% (33.5–73.9%) and 32.5% (0.0–93.9%) with the Poisson, Niemierko, and Marsden models, respectively. The median value of calculated TCP in prostate plans was 85.1% (56.4–90.9%), 81.2% (56.1–88.7%) and 62.5% (28.2–75.9%) with the Poisson, Niemierko, and Marsden models, respectively. The median value of calculated TCP in H&N plans was 94.0% (44.0–97.8%) and 94.3% (0.0–97.8%) with the Poisson and Niemierko models, respectively. There were significant differences between the calculated TCPs with the Marsden model in comparison with either the Poisson or Niemierko model (p < 0.001) for both lung and prostate plans. The TCPs calculated by the Poisson and Niemierko models were significantly correlated for all three tumor sites. There are variations with different radiobiological models. Understanding of the correlation and limitation of a TCP model with dosimetric parameters can help develop the meaningful objective functions for plan optimization, which would lead to the implementation of outcome-based planning. More clinical data are needed to refine and consolidate the model for accuracy and robustness. This study has tested three radiobiological models with varied disease sites. It is significant to compare different models with the same data set for better understanding of their clinical applicability. [ABSTRACT FROM AUTHOR]