55,806 research outputs found

    Comparisons Between the Kaplan-Meier Complement and the Cumulative Incidence for Survival Prediction in the Presence of Competing Events

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    Estimating cumulative event probabilities in time-to-event data can be complicated by competing events. Competing events occur when individuals can experience events other than the primary event of interest. These “other events” are often treated as censored observations. This thesis compares point estimates and relative differences between two cumulative event probability estimators, the Kaplan-Meier complement (KMC) and the cumulative incidence (CI), in the presence of competing events. The KMC does not allow for the possibility of experiencing competing events, whereas the CI does. Consequently, the KMC overestimates the CI in the presence of competing events. In this thesis, data were simulated with different combinations of primary event hazards, competing event hazards, random censoring hazards, and sample sizes. Cumulative event probabilities using the KMC and CI methods were calculated over a time period of 10 years. Several conclusions were drawn. High primary event hazards resulted in high KMC’s and CI’s and slightly narrowed the variability of the relative differences between the two estimates. High competing event hazards did not affect KMC’s but resulted in low CI’s, causing high relative differences. High random censoring hazards did not affect KMC’s, CI’s, or relative differences. Large sample sizes did not affect the median relative differences but did narrow the variability of the relative differences. The public health relevance of this thesis is to help medical clinicians and researchers understand the advantages and disadvantages of different approaches of calculating cumulative event probabilities in situations where competing events occur. This is particularly important in the area of personalized medicine in diseases like cancer where clinicians attempt to predict their patients' mortality or recurrence probabilities over time given certain clinical, pathologic, or demographic characteristics

    Teddy Bear Triage and Treatment: Novel Technique of Mass Casualty Incident Education

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    Providing education on medical care and triage during mass casualty incidents (MCI) can be challenging. Table top exercises lack the impact of a hands-on experience necessary to emphasize the scale of a real event, while full scale events are often time and resource intensive. We present a novel method using low-cost stuffed bears to expose learners to MCI triage methodology and medical care

    Rule 10b-5 and the Corporation’s Affirmative Duty to Disclose

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    In order to make responsible investment decisions investors must be adequately informed. In this article Professor Bauman argues that the existing disclosure requirements of the federal securities laws do not meet the informational needs of investors because there is no affirmative duty to disclose all material information. In order to fill this substantial gap in the existing disclosure scheme, Professor Bauman argues that rule lob-5 should be read to require prompt disclosure of all material information subject only to limited exceptions and should be applicable even in the absence of trading or prior inaccurate disclosure
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