Plans for data analysis Sample Clauses

Plans for data analysis. A basic description of each program begins the data analysis section. Basic services delivered, a timeline of how long the program has existed and details of program implementation were collected from agency records. From there, information regarding the program participants was described, if available. The data was analyzed for program reach, defined as the, “proportion of the intended priority audience that participates in the intervention” in order to determine how much of the target audience participated in the program and if this increased or decreased over time (Saunders, Evans, and Joshi 2005). Recruitment practices were also described for each program to determine if the target population was truly reached. After the participants involved in the program were described, the program itself was described in detail. Fidelity, or the extent to which the intervention was implemented as planned was assessed using information about changes in the program overtime as it relates to initial plans (Saunders, Evans, and Joshi 2005). Programs were also evaluated for the amount of the program that was fully delivered (dose) and participant satisfaction where available. In conjunction with the above, program quality was assessed by comparing the activities of the program with participant satisfaction and resources available to the program. Justifications for activities used by programs were analyzed to determine if there were other possible activities that would provide a higher dose or higher satisfaction among participants. Data analysis is concluded with an overall assessment of challenges and opportunities in the implementation of the program that was not covered in the above sections.

Plans for data analysis. The plan for missing data analysis will follow a systematic pattern that will be replicated for each study. The multivariate regression models of each study will be recreated and complete-case analysis will be performed. Parameter estimates and confidence intervals from the complete-case analysis will be compared to the multiple imputation methods. The regression framework employed for Case study 1 was the logistic model expressed as: 𝑃 𝐷 = 1 𝑋1, 𝑋2, … , 𝑋𝑘 = 𝑃 𝑋 = 1 , 𝑖!1 !(𝛼+ 𝑘 𝛽𝑖X𝑖+𝗌) where i = 1, 2, 3,…, k, D = 1 denotes the outcome of interest (e.g., achieving 25% in lipid reduction), Xi denote the k number of independent variables in the regression model, α and β denote unknown parameters, P(X) denotes the probability of achieving the clinical goals (X=1) given that the following independent variables (Xk) are present, and 𝜀 denotes the error term. The logit form of the logistic model is expressed as: 𝐿𝑜𝑔𝑖𝑡 𝑃 𝑋 = 𝛼 + 𝑘 𝑖!1 𝛽𝑖𝑋𝑖 + 𝜀, where i = 1, 2, 3,…, k, Xk denotes the k number independent variables for in the regression model, and 𝜀 denotes the error term. The odds ratio (OR) is computed as the product of exponentials: 𝑂𝑑𝑑𝑠 𝑟𝑎𝑡𝑖𝑜 𝑂𝑅 = 𝑘 𝑖!1 𝑒𝛽𝑖(K1𝑖!K0𝑖), where X1 and X0 are two specifications of the collection of k independent variables X1, X2, X3,…, Xk. The regression framework employed for Case study 2 was modeled as a multiple linear regression: 𝑌𝑖 = 𝛼 + 𝑘 𝑖!1 𝛽��𝑋𝑖 + 𝜀, where Xx denotes the outcome of interest (e.g., reduction in HbA1c), i = 1, 2, 3,…, k, α denotes the Y-intercept or constant, βi denotes the parameter coefficient for the independent variable (Xi), Xi denotes the k number of independent variables, and 𝜀 denotes the error term. Multiple imputation methods will require an initial assumption that the missing data mechanism is MAR where the probability of the missing value depends of the observed value (Yobs), not on the unobserved value (Ymis). Multiple imputation will be carried out using Markov chain Monte Carlo (MCMC) simulation where a large number of samples are drawn from a posterior distribution yielding an estimate for the missing value.6,20 Five imputed dataset (m = 5) will be generated and combined for the analysis of multivariate regression models using the methods developed by Xxxxxx and Xxxxx.6 Parameter estimates and confidence intervals will be compared to results of the complete-case analysis. Sensitivity analysis will be performed comparing the parameter ...

Plans for data analysis. Utilizing Excel, both a descriptive and comparative analysis of the data collected was conducted. The descriptive analysis consisted of two prongs, detailed below. A comparative analysis was then conducted on the two sources of coverage data available.

Plans for data analysis. No patient or hospital identifiers will be presented in the analysis of the data. Only information that is accessible in the public XXXXX system will be presented. The reports will be analyzed to identify the number of reports related to surgical fires. Additionally, the number of reports resulting in injury or death, those that note the use of supplemental oxygen, the location of the surgical fire (on the patient, in the patient, elsewhere), and whether or not an alcohol-based skin preparation agent was referenced will be tallied. The data will also be compared to existing surgical fire estimates in the literature.

Plans for data analysis. The amounts spent and quantities purchased for the respective supplies required to maintain school WASH systems will be totaled and used to determine the average expenditure and supply amount needed per pupil in an academic year. This will be done for the specific WASH systems (water collection, drinking water, hand washing, sanitation, and WASH education systems) to determine system-specific funding and supply needs. The same procedures will be used to analyze funding needs for services employed by the school per academic year for school WASH systems’ repair, maintenance, and operations. Frequency analyses will be carried out to assess the data collected in the transportation, education, and donations sections. These will be used to characterize school-level practices and their capacity for maintaining school WASH systems over time. Budget and expense records will be reviewed both individually and against one another to characterize bookkeeping and accounting practices at the school level, and may be used to make inferences about the quality of these systems. We will compare budget and expense records to answers given in the head teacher survey in order to verify answers given by head teachers, and to identify any costs related to school WASH that may not have been captured in the head teacher survey. Line items will be divided into WASH and non-WASH categories (e.g. textbooks), and the total amounts budgeted and spent in both categories will be determined and compared to assess how non-WASH expenses compete with school WASH systems for funding. Records will also be examined to determine what funding sources are used, and what percentage of each fund is used for WASH and non-WASH expenses. The shop survey will be used to determine the average prices of commonly-used WASH supplies. These averages will be compared to the amounts spent in the head teacher surveys to determine if the amounts spent by schools are in line with average item costs.