Sentence examples for incremental statistics from inspiring English sources

Findings from previous work showing that increased sample size and an increase in informativeness of the prior distributions improve the precision of parameter estimates from a non-identifiable latent class model would also apply here [ 33], as all incremental statistics that we have described are functions of the prevalence, sensitivity and specificity parameters in the latent class model.

The statistical significance for individual coefficients is evaluated by the t statistic, and the significance level is indicated using a symbol in Tables 2 and 3. Based on the results of the t tests, it is found that the incremental F statistics value is not significant.

Correspondingly, the median estimated incremental value statistics were all lower than those in Table  5.

Interestingly, the true value of both incremental value statistics suggested a small incremental value even when T2 was not useful: AUC = 0.04 and IDI = 0.02.

Even in the absence of a gold standard, incremental value statistics may be estimated and can aid decisions about the practical value of a new diagnostic test.

In all scenarios, the estimated values of the incremental value statistics were very close to the true values across the 1000 datasets.

When the model was mis-specified and ignored conditional dependence between the tests, both incremental value statistics were over-estimated as expected.

To study how ignoring conditional dependence will affect the incremental value statistics, we used the simulated datasets from the scenario where T2 had both improved sensitivity and specificity compared to T1 and the two tests are conditionally dependent.

Model fit was assessed by inspecting absolute [standardised root mean square residual (SRMR) and root mean square error of approximation (RMSEA)] and incremental fit statistics [Tucker-Lewis Index (TLI), Comparative Fit Index (CFI ], as well as parameter estimates.

For the incremental fit statistics (Goodness of Fit Index :GFI; the Tucker-Lewis Index :TLI; and the Comparative Fit index: CFI) values less than.90 indicate lack of fit, values between.90 and.95 indicate reasonable fit and values between.95 and 1.00 indicate good fit [ 21].

It was evaluated using absolute and incremental goodness-of-fit statistics and published cut-off criteria [ 13- 15].