While the study of the catalytic kinetics of enzymes represents one of the most established and well documented fields in biochemical research, the impact of biochemical state (pH, ionic strength, temperature, and certain cation concentrations) is typically not formally accounted for in kinetic studies [1], [2].
However, the 'homeopathicity' [ 12] of the therapeutic modality and of the clinical outcome measure/s used (i.e. the key characteristics of model validity) have never been formally accounted for in the assessment of trial quality.
In view of the nature of such referrals, many clinical details such as precise gestational age at birth and specific ethnic background of the parents are not provided in a substantial proportion of cases and hence could not be formally accounted for in our models; for example no information regarding exact gestational age at birth, was available in 38% of cases.
Note that we do not formally account for multiple comparisons and report only the unadjusted p values.
30 Unfortunately, because of the small sample size and small number of health facilities included in the study, we were unable to formally account for this in most of the statistical analysis.
Importantly, however, it is possible to formally account for statistical issues by combining the exact aggregation conditions that we present below with methodological tools that have been presented in alternative revealed preference contexts.
Other companies may follow suit by formally accounting for stock options on their income statements.
These population-based clinical trials do not formally account for disease-associated marker genotype or genome-associated therapeutic response.
This paper demonstrates the use of Quantitative Risk Assessment (QRA) to formally account for the uncertainty in each input parameter to assess the probability of achieving a desired degree of wellbore stability at a given mud weight.
We begin by displaying the time series patterns of SSDI/SSI receipt by VR treatment status and then report results from a number of different linear regressions that allow us to formally account for pre-application factors as well as the observed covariates listed in Table 1.
