Statistical Approach. To assess agreement a stepwise approach was undertaken. Although it is assumed that two techniques for measuring the same output will be closely related, Xxxxxxx’x correlation coefficient (PCC) was calculated to clarify the presence of a linear relationship between Syr- 1-Syr-2, Vac-1-Vac-2, Syr-1-Vac-1, Syr-2-Vac-2. PCC is highly sensitive to the range of values and lacks information about systematic difference therefore PCC does not assess agreement as a high degree of correlation is possible when agreement is poor [1]. Passing and Bablock regression analysis was then undertaken on the above pairs. This is a non-parametric linear regression procedure which is non-sensitive to outliers and fits the parameters of a and b in the linear equation y = a + bx. This reveals constant (regression line intercept a) and proportional (regression line slope b) difference with confidence intervals of 95% (95% Cis). Therefore, if the 95% Cis for a include zero one can conclude that there is no constant difference between methods. Additionally, if the 95% Cis for b include the value one, then it can be concluded that there is no proportion difference between methods. Overall this allows the for the assumption that x = y and agreement between methods to be presumed. The primary fallacy with the Passing and Bablock regression model is that it derives the agreement of two methods from the data and neglects whether this is within clinically relevant parameters [2]. Therefore, Xxxxx-Xxxxxx analysis was undertaken (explained in the main text) to assess whether the agreement between the above pairs was within an a priori 5% agreement limit. With this analysis, a paired students t-test was also computed testing the null hypothesis H0 that the mean of the differences between the results does not differ from 0, against the alternative Ha that it does. Finally, Xxxxxxxxxx correction was applied to adjust the significant p value for paired students t-tests to account for multiple comparisons.
Statistical Approach. The primary goal of this present study was to evaluate the predictive content of the two outpatient arrival series with respect to making forecasts of inpatient arrivals. Initially, several univariate and naïve models common to the time-series literature were used to make forecasts, using ARIMA, exponential smoothing, autoregressive neural networks, and a Poisson generalized linear regression having day-of-the-week and holiday covariates. Naïve models such as random walk, seasonal naïve, and baseline mean forecasts were also used to establish the relative performance of more complex models. Finally, a Poisson model incorporating the same day-of-week and holiday variables was modified to include distributed lag terms to capture the relationships between the inpatient arrivals and lagged outpatient visits to neurologists or neurosurgeons. This structure of the two distributed lag terms was chosen based on the model having the minimum AIC among all other possible candidate models. Ultimately, the comparison of the forecast accuracy for short (7-day) and long (30-day) forecasts between this model and the other models were evaluated based on the root mean squared error (RMSE) and mean absolute error (MAE) of the forecasted values and the actual values. One important feature of DL models is their straightforward incorporation into a generalized regression model. A simple polynomial DL model of order q over the lag range 1 to n, where t denotes the time index, has the form: In the above model, only the weights b0, α0, α1, … αq need to be estimated. This model has the equivalent representation: from which it becomes apparent that distributed-lag terms can be added to a regression model as a simple linear combination of terms. Note that, in this model, the effect at lag zero is ignored (it represents a same-day effect and is therefore not useful in a forecasting context). In addition, it is important to note that, for a q-degree polynomial lag-response structure, only q + 1 coefficients need to be estimated, allowing even dozens of lag terms to be used with relatively flexible restrictions on their structure. For both the neurologist arrivals and the neurosurgeon arrivals, the maximum lag length of the range to be considered was fixed at 30 days. In other words, I chose to model the relationship between the inpatient arrivals and the history of outpatient arrivals up to and including observations from 30 days prior, with the minimum lag allowed to vary in order to ...
Statistical Approach
