Sensitivity Analyses. Calculate one-way and/or multi-way sensitivity analyses for direct prescription costs and incremental prescription costs (savings). § An explanation of the methods used to calculate the sensitivity analyses must be included as well as the assumptions used in calculating the values. § Cite all relevant references.
Sensitivity Analyses. Sensitivity and Issue Target Modelling Assumption Volume Predictions Quality of inventory information – demonstrate band of reasonable results. All stands All volumes +/- 15% Pine Volume Storage Uncertainty with regard to quantity and quality of pine stands stored on xxxxx. Pine stands Volumes deteriorate with age after age 140 years Operational adjustment factors Very aggressive OAF values in base case. All managed stands Use MoF standard OAFs: 15%, 5% Sub-unit factors Differences in modelling dynamics All stands Individual harvest requests in wet and dry belts
Sensitivity Analyses. The sensitivity analyses, which investigated the implications of including patients with missing information in at least one data source for routes to diagnosis or milestone date, displayed similar findings as the main analysis (Tables A.3-A.4 in Appendix A). And the basic characteristics of excluded and included patients were similar (Table A.5 in Appendix A).
Sensitivity Analyses. We have planned other analyses to assess the robustness of our findings. The first set will include any outlying variables excluded during data cleaning. The next set will trim the upper and lower 5% tails of observations with continuous distributions to identify any associations that may be due to or exaggerated by extreme, but not spurious, observations. The final set of planned sensitivity analyses will consider process measures and measures of intervention fidelity to consider the association between variable exposure and outcomes. This will be particularly important both for planning future studies and for efficient resource allocation when planning for scale-up and wider implementation. Additional sensitivity analyses may be undertaken if missing data is a serious concern, as discussed below. Sensitivity analyses are recommended in PCORI Methodology Standards 3:IR-5 (which recommend the use of sensitivity analyses to determine the impact of key assumptions) and 4:MD-5 (which recommend that the sensitivity of inferences to missing data methods and assumptions should be examined and incorporated into the interpretation of the results).1
Sensitivity Analyses. In the development of the HIV-ERSD cohort, those who were preemptively referred or waitlisted were excluded. Those who were preemptively referred and waitlisted were compared to those to evaluate the differences between the two groups. Additionally, one of the classifications for being HIV positive was having a prescription for an antiretroviral regimen. Though, only patients who used Medicare to fill their prescriptions were captured. Differences between those who had a Medicare prescription claim and those who did not was assessed.
Sensitivity Analyses. This section briefly describes the sensitivity analyses that will be performed on the base case. The sensitivities reflect the stability of the base case in the face of uncertainty surrounding specific analysis assumptions. They also reflect the impact of alternative management or potential changes in forest practices. Sensitivity analysis provides a measure of the reasonable upper and lower bounds of the harvest forecast, reflecting the uncertainty of assumptions made in the base case. The magnitude of the increase and decrease in the sensitivity variable reflects the degree of uncertainty surrounding the assumption associated with that given variable. By developing and testing a number of sensitivity analyses, it is possible to determine which variables most influence results. To allow meaningful comparison of sensitivity analyses, they are usually performed using the base case (i.e. current performance) and varying only the assumption being tested (i.e. all other assumptions remain the same as in the base case). The sensitivities that will be carried out for this analysis are listed in Table 31. Table 31 Sensitivity analyses Issue Sensitivity Test Growth and Yield Adjust natural stand yields by ± 10% Adjust managed stand yields by ± 5% Adjust minimum harvest age by ± 10 years Adjust minimum harvest volume to 100 m3/ha Adjust stand productivity ± 3 meters Adjust regen delay in beetle-killed stands, NSIFS to define Remove Xxxxxxxxx from AAC Landbase classification Adjust net harvesting landbase (THLB) by ± 10% Adjust landbase for Community Forest and Woodlots expansions Resource Emphasis Adjust greenup height in IRM by ± 1 metre Visual retention requirements ± 5% Temperature Sensitive Streams, management recommendations Harvest Flow Alternative harvest queue, random vs. oldest first Extend uplift for 20 year period at highest level possible MPB Adjust shelf-life assumptions, NSIFS to define Beetle spread rates, ± 5 years Managed stand mortality, MoFR Region to define Adjacency Adjust duration of spatial adjacency rules by ± 10 years Xxxxxxx TSA TSR 4 Draft Data Package
Sensitivity Analyses. In collaboration with City staff, develop up to three (3) sensitivity analyses to evaluate the financial and rate impacts of changes to key variables or other factors of interest.
Sensitivity Analyses. Offer up to three (3) sensitivity analyses to evaluate the financial and rate impacts of changes to load growth, power costs, project timing and priority, project funding, conservation initiatives or other changes specified by the City. » The resulting revenue requirement and multi-year rate strategy developed will meet the City’s specified financial metrics, goals and objectives and deliver a self-sustaining, electric utility financial planning toolset TASK 4: COST-OF-SERVICE ANALYSIS The cost-of-service analytical task will establish a defensible basis for assigning “cost shares” and establishing “equity” for the City’s customers. This is accomplished with the development of a series of allocations, based on customer data and engineering / planning criteria to assign utility cost recovery to customers in proportion to their estimated demands. Specific consideration will be given to total utility costs in relationship to the functions identified in Exhibit 1. We have included traditional and unbundled cost-of-service functions. The unbundling analysis has proven beneficial in aligning rates by service function in preparation of meeting alternative service requests for example - distributed generation.
Sensitivity Analyses. All analyses were re-estimated to control for the effect of possible confounders sex and cognitive ability (MSEL). The effect of missing data was addressed by re- estimating the models with listwise deletion. The results of the models after adjustment for sex and with listwise deletion did not change. However, when controlling for cognitive ability at 36 months, the relationship between BI at 9 months and anxiety at 36 months become significant; all other patterns of findings remain the same. Further results for Models 2.1 to 2.3 are summarised in Appendix 1. Table 2.3 Summary of Exploratory Mediation Analyses Predictors (24 months) Mediator (36 months) Outcome (36 months) Total Effect Direct Effect Indirect Effect (95% CI Bootstrap) Percentage of Total Effect Mediated Behavioural Inhibition ASD Anxiety .42 (.10) *** .28 (.08) ** .14 (.06, .22) ** 33 % Effortful Control ASD Anxiety -.39 (.08) *** -.09 (.09) -.31 (-.41, -.21) *** 79 % Behavioural Inhibition Anxiety ASD .28 (.10) ** .06 (.09) .22 (.12, .32) *** 79 % Effortful Control Anxiety ASD -.59 (.07) *** -.43 (.09) *** -.16 (-.25, -.08) ** 27 % 85 *** p < .001, ** p < .01, * p < .05
