Statistical analysis cost data

The typical cDNA microarray study can be described in nine steps (1) establishing an appropriate experimental design (2) isolation and conversion of mRNA to labeled cDNA (3) hybridization of labeled cDNA to the microarray slide (4) image acquisition, (5) data storage, (6) normalization (7) statistical analysis (8) data mining and (9) validation of the results. Each of these steps is multifaceted and the introduction of error at any point in the process can lead to costly loss of data. The following section describes the steps followed in experimental design. 

Current Developments. A number of low-cost proprietary temperature loggers are being trialled in conjunction with the above IS Controller. In one form (14) these produce only a strip chart data table. Although convenient for statistical analysis these require keying into a further microcomputer plotter to draw a complete process temperature profile, as shown in Figure lb. As an illustration of the IS Controller s performance, statistics for the 150 minutes after exothermic overshoot indicate a mean temperature within 0.1"C of the set point and a standard deviation of 0.4°C. 

In most studies, phytoestrogen intake has been estimated by direct methods that evaluate food intake either by recall (food-frequency questionnaires -FFQs) or by record (food diary), and subsequently by composition databases based on information of this kind. Food-frequency questionnaires are widely administered to subjects involved in epidemiological studies. Their validity and reproducibility is considered sufficient when statistically correlated to data obtained from dietary records (a properly-completed and comprehensive food diary) and from analysis of blood and urine samples (Kirk et ah, 1999 Huang et al, 2000 Yamamoto et al, 2001 Verkasalo et al, 2001). FFQs can be repeated several times a year and may be administered to large populations. Such an approach provides an easy and low-cost method of assessing the... 

Operating costs can be estimated based on statistical analysis of operating costs in existing plants. Costs of waste disposal can be evaluated in the same way as costs for any chemical process since procedures for disposal include, in fact, unit chemical processes and operations. Costs of utilities and maintenance are best assessed based on the company data banks. Typical utility figures per m capacity of reactors in MPPs are 800-1100 kg steam/h, 60-80 kW power, and 7,000-8,000 kJ/h refrigeration capacity. 

How clean the data must be depends on the importance of the data. Critical analysis variables must be clean, so this is where the site and data management groups should focus their resources. If the data are dirty at the time of statistical analysis, many inefficient and costly workarounds may need to be applied in the statistical programming, and the quality of the data analysis could suffer. However, if a variable is not important to the statistical analysis, then it is better to save the expense of cleaning that variable. 

A basic assumption underlying r-tests and ANOVA (which are parametric tests) is that cost data are normally distributed. Given that the distribution of these data often violates this assumption, a number of analysts have begun using nonparametric tests, such as the Wilcoxon rank-sum test (a test of median costs) and the Kolmogorov-Smirnov test (a test for differences in cost distributions), which make no assumptions about the underlying distribution of costs. The principal problem with these nonparametric approaches is that statistical conclusions about the mean need not translate into statistical conclusions about the median (e.g., the means could differ yet the medians could be identical), nor do conclusions about the median necessarily translate into conclusions about the mean. Similar difficulties arise when - to avoid the problems of nonnormal distribution - one analyzes cost data that have been transformed to be more normal in their distribution (e.g., the log transformation of the square root of costs). The sample mean remains the estimator of choice for the analysis of cost data in economic evaluation. If one is concerned about nonnormal distribution, one should use statistical procedures that do not depend on the assumption of normal distribution of costs (e.g., nonparametric tests of means). 

To design an experiment means to choose the optimal experiment design to be used simultaneously for varying all the analyzed factors. By designing an experiment one gets more precise data and more complete information on a studied phenomenon with a minimal number of experiments and the lowest possible material costs. The development of statistical methods for data analysis, combined with development of computers, has revolutionized the research and development work in all domains of human activities. 

A consequence of the above ideas is a rule of thumb that to obtain adequate precision with some assurance that gross error has been avoided, at reasonable cost, you will often be well served to perform three or four trials of a single experiment. The experiments outlined in this textbook rarely call for such replication, due to time and financial constraints of the educational process. Remember, however, that a properly designed experiment should be performed multiple times, and that the data should be presented with a well-defined statistical analysis to allow the reader to ascertain the precision of the experiment. 

Grabowski and Vernon (159) also used published aggregate R D expenditure data to estimate the cost of successful drug development. Though Grabowski and Vernon did not estimate development time profiles with statistical analysis, their estimate provides another point of reference for comparison among methods, and it is also summarized here. 

Willan AR, Briggs AH (2006) Statistical Analysis of Cost-effectiveness Data. John Wiley Sons, Inc., Hoboken. 

Willan and Briggs - Statistical Analysis of Cost Effectiveness Data... 

In essence, a statistical experiment implies a systematic varying of process, observation of change in response, collection and analysis of data, and extraction of information to arrive at a conclusion. Experiments are designed so that the appropriate decision can be arrived at in the shortest time and within cost constraints. 

Following this procedure, we merge firm-level data from the survey to claimant-level data from Miimesota s workers compensation files at the Department of Labor and Industry. Since costs are the product of claim fi equency, claim duration, and benefits, we partition our statistical analysis into claim frequency and claim duration components to see whether the HRM practices affect claim fi equency, claim duration, or both. This will provide evidence about whether costs are reduced either because of loss prevention effects (in that a particular practice reduces the number of claims) or loss control effects (in that a particular practice limits the costs of those injuries that have occurred). We assume that the benefit parameters (maximum and minimum benefits) are exogenous relative to the choices made by the firms in our survey and do not model benefit determination here. 

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