Modeling Subject Recruitment in Medical and Clinical Investigations Essay

Modeling Subject Recruitment in Medical and Clinical Investigations Essay

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Evaluating and monitoring subject recruitment is important in medical research (Schroen et al., 2010). Delayed subject recruitment will increase the cost of study and/or can lead researchers to settle for smaller sample sizes than originally proposed. If the proposed sample size is not achieved, the study will have low power and may fail to recognize a treatment effect. Slow recruitment tends to increasing resource utilization, which includes the goodwill contribution of patient volunteers. The delayed recruitment may also lead to delay in the adoption of new therapies and slow the advancement of medical progress (Philipson et al., 2010). For example, a year delay in access to Herceptin would represent a total loss of $8 billion dollars of benefit for breast cancer patients in the US (Parreco, 2012). Development of reliable and practical tools for accrual prediction is critical in clinical trial studies.
Modeling subject recruitment has been studied for a long time. The available accrual models are reviewed by Barnard (2010) and Zhang (2012). According to Barnard (2010), the simplest approach is an unconditional model (Carter, 2004). It assumes the accrual rate (e.g. subjects recruited per month) is fixed. The total accrual time is estimated by dividing the planned sample size by the number of subjects they expect to recruit each month. A conditional model allows the accrual rates to vary in any given month depending on other factors that may accelerate or delay the accrual (Carter, Sonne and Brady, 2005). Compared to the unconditional model, the conditional model matches more closely to real situations. Both unconditional and conditional model fail to consider variations in actual accrual progress and therefore, their prediction...


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...collected. In the beginning of the trial, when no data are collected, the model will weigh entirely on the investigator’s historical experience, with P=1. As data collection progresses, the weight on the prior will become less and more weights will be placed on the data. The second approach we proposed is the hedging prior model. It turns out to be the same as modified power prior, if the power is chosen to be beta (1, 1), or a uniform distribution. Detailed model descriptions are provided for the accelerated prior and the hedging prior in section 2. Section 3 shows the application and evaluation of the methods using real clinical data. In section 4, the model efficiency and robustness of the new proposed methods, and the previous proposed Bayesian model are validated using simulated data under eight different scenarios. Section 5 is the discussion and conclusions.

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