Transcranial infrared laser stimulation (TILS) has shown effectiveness in improving human cognition and was investigated using broadband near-infrared spectroscopy (bb-NIRS) in our previous study, but the effect of laser heating on the actual bb-NIRS measurements was not investigated. To address this potential confounding factor, 11 human participants were studied. First, we measured time-dependent temperature increases on forehead skin using clinical-grade thermometers following the TILS experimental protocol used in our previous study. Second, a subject-averaged, time-dependent temperature alteration curve was obtained, based on which a heat generator was controlled to induce the same temperature increase at the same forehead location that TILS was delivered on each participant. Third, the same bb-NIRS system was employed to monitor hemodynamic and metabolic changes of forehead tissue near the thermal stimulation site before, during, and after the heat stimulation. The results showed that cytochrome-c-oxidase of forehead tissue was not significantly modified by this heat stimulation. Significant differences in oxyhemoglobin, total hemoglobin, and differential hemoglobin concentrations were observed during the heat stimulation period versus the laser stimulation. The study demonstrated a transient hemodynamic effect of heat-based stimulation distinct to that of TILS. We concluded that the observed effects of TILS on cerebral hemodynamics and metabolism are not induced by heating the skin.
Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell-Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence.
In this paper, we develop the steps of the expectation maximization algorithm (EM algorithm) for the determination of the maximum likelihood estimates (MLEs) of the parameters of the destructive exponentially weighted Poisson cure rate model in which the lifetimes are assumed to be Weibull. This model is more flexible than the promotion time cure rate model as it provides an interesting and realistic interpretation of the biological mechanism of the occurrence of an event of interest by including a destructive process of the initial number of causes in a competitive scenario. The standard errors of the MLEs are obtained by inverting the observed information matrix. An extensive Monte Carlo simulation study is carried out to evaluate the performance of the developed method of estimation. Finally, a known melanoma data are analyzed to illustrate the method of inference developed here. With these data, a comparison is also made with the scenario when the destructive mechanism is not included in the analysis.
In this paper, we develop likelihood inference based on the expectation maximization algorithm for the Box-Cox transformation cure rate model assuming the lifetimes to follow a Weibull distribution. A simulation study is carried out to demonstrate the performance of the proposed estimation method. Through Monte Carlo simulations, we also study the effect of model misspecification on the estimate of cure rate. Finally, we analyze a well-known data on melanoma with the model and the inferential method developed here.
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