A priori belief concerning the true values of parameters, incorporating uncertainty, is quantified (prior distributions) based on previous knowledge of test performance and updated by the addition of empirical data (expressed in the likelihood) to generate modified estimates (posterior distributions) of said parameters.
Abbreviations: mRS = modified estimated Rankin Scale; NIHSS = estimated National Institutes of Health Stroke Scale.
A simulation study demonstrated that 'bending' could substantially increase the achieved response to selection based on an index derived using the modified estimates [ 14].
While this is reassuring, the modified estimates evidently cannot have any accuracy, which further stresses the qualitative nature of the inference from the single-locus coalescence analyses.
We used these probabilities to inflate prevalence estimates, such that the modified estimates could be interpreted as (extrapolated) measures of the prevalence of 5-year history of metastatic bone disease.
Possible coincidence with some climatic events in MYA (after [21]) is indicated, as well as modified estimates derived from slightly different molecular clocks (1.5 to 2.5%/MY; see additional file 1b for further details).
Other relevant potential confounders (see Methods), were also tested but not included in the final model because they were not independently related to the outcome, nor modified estimates for the remaining variables.
These modified estimates may be high, as the most common causes of non-continuous enrollment – a recent employment change in the non-elderly and a recent 65th birthday in the elderly – likely predict the absence of metastatic bone disease.
† Dependent defined as Barthel Index < 100 Age, socioeconomic status, level of education, marital status, smoking status, DLco, RV/TLC, PaO2 and PaCO2, were tested as potential confounders and finally not included because they were not independently related to the outcome, nor modified estimates for the remaining variables.
The following confounders were tested and finally included in the final model if they were independently related to the outcome, or modified estimates for the remaining variables: age, gender, level of education, work status, marital status, dyspnea, FEV1, DLco, RV/TLC, PaO2, PaCO2, 6MWD, BMI and comorbidities.
How should we modify estimates of achievable market share and size and target markets?
