Statistics data description

Table 3.3 Duration Model Using Claimant Data Descriptive Statistics... 

AiVhen doing measurements, statistics are needed if we want to describe the data (descriptive statistics) or if we want to draw conclusions based on the data (inferential statistics). There is a vast amount of statistical methods in the literature, and the choice of method depends on what we want to know and what type of data we have. In this chapter, we will give an overview of the most basic and the most relevant methods for... 

RTOP data descriptive statistics (non-standardized scores)... 

All those visualization tools which allow the exploration of uni- and oligo-variate data can be considered as instruments of descriptive statistics. Descriptive statistics is usually defined as a way to summarize/extract information out of one or a few variables compared to inferential statistics, whose aim is to assess the validity of a hypothesis made on measured data, descriptive statistics is merely explorative. In particular, some salient facts can be extracted about a variable ... 

Cuantltatlve observations replication, taw data, descriptive statistics, indexes Controls, material standards, paired observations Bloassays Apparatus... 

Descriptive statistics quantify central tendency and variance of data sets. The probability of occurrence of a value in a given population can be described in terms of the Gaussian distribution. 

Hanvelt et al. (1994) estimated the nationwide indirect costs of mortality due to HIV/AIDS in Canada. A descriptive, population-based economic evaluation study was conducted. Data from Statistics Canada were used, which contained information about aU men aged 25-64 years for whom HIV/AIDS or another selected disease was listed as the underlying cause of death from 1987 to 1991. Based on the human capital approach, the present value of future earnings lost for men was calculated. The estimated total loss from 1987 to 1991 was US 2.11 billion, with an average cost of US 558,000 per death associated with HIV/AIDS. Future production loss due to HIV/AIDS was more than double during the period 1987 to 1991, from US 0.27 to US 0.60 billion. A more comprehensive update of this smdy was presented by Hanvelt et al. (1996). The same database and the same data section but for the calendar years 1987-1993 was used. The indirect cost of future production due to HIV/AIDS in Canada based on the human capital approach for that period was estimated to be US 3.28 billion. The authors also calculated the willingness-to-pay to prevent premature death due to HIV/AIDS, which was estimated based on... 

PROC TABULATE is an excellent tool for producing quick descriptive statistics on data, but it does not meet the typical needs of generating clinical trial tables, for several reasons ... 

TRANSPOSE AGE DESCRIPTIVE STATISTICS INTO COLUMNS. proc transpose data = age out = age prefix = col ... 

APPEND AGE DESCRIPTIVE STATISTICS TO AGE P VALUE ROW AND CREATE AGE DESCRIPTIVE STATISTIC ROW LABELS. data age ... 

APPEND gender descriptive statistics to gender p value row AND CREATE GENDER DESCRIPTIVE STATISTIC ROW LABELS. data gender ... 

PROC MEANS, PROC SUMMARY, and PROC TABULATE are other SAS procedures that you can use to get descriptive statistics and place them into output data sets. However, those procedures do not offer any descriptive statistical variables that you cannot get from PROC FREQ or PROC UNIVARIATE. 

For continuous variables you may be required to provide inferential statistics along with the descriptive statistics that you generate from PROC UNIVARIATE. The inferential statistics discussed here are all focused on two-sided tests of mean values and whether they differ significantly in either direction from a specified value or another population mean. Many of these tests of the mean are parametric tests that assume the variable being tested is normally distributed. Because this is often not the case with clinical trial data, we discuss substitute nonparametric tests of the population means as well. Here are some common continuous variable inferential tests and how to get the inferential statistics you need out of SAS. 

In order to determine the optimal number of compartments, literature information on small intestinal transit times was utilized. A total of over 400 human small intestinal transit time data were collected and compiled from various publications, since the small intestinal transit time is independent of dosage form, gender, age, body weight, and the presence of food [70]. Descriptive statistics showed that the mean small intestinal transit time was 199 min with a standard deviation of 78 min and a 95% confidence interval of 7 min. The data set was then analyzed by arranging the data into 14 classes, each with a width of 40 min. Figure 9 shows the distribution of this data set. 

Descriptive statistics are used to summarize the general nature of a data set. As such, the parameters describing any single group of data have two components. One of these describes the location of the data, while the other gives a measure of the dispersion of the data in and about this location. Often overlooked is the fact that the choice of which parameters are used to give these pieces of information implies a particular type of distribution for the data. 

Table 11.3 presents summary descriptive statistics for those relationships for which financial data were available. Descriptive statistics are provided for sales revenue, operating profit, operating margin, concentration of purchases with the top two vendors, share of purchases with the largest alternative vendor, and overall satisfaction with the largest alternative vendor. Again, to protect confidentiality, contribution margin is not disclosed. 

Table 11.3. Descriptive statistics by respondent categories given availability of financial data... 

The bottleneck in utilizing Raman shifted rapidly from data acquisition to data interpretation. Visual differentiation works well when polymorph spectra are dramatically different or when reference samples are available for comparison, but is poorly suited for automation, for spectrally similar polymorphs, or when the form was previously unknown [231]. Spectral match techniques, such as are used in spectral libraries, help with automation, but can have trouble when the reference library is too small. Easily automated clustering techniques, such as hierarchical cluster analysis (HCA) or PCA, group similar spectra and provide information on the degree of similarity within each group [223,230]. The techniques operate best on large data sets. As an alternative, researchers at Pfizer tested several different analysis of variance (ANOVA) techniques, along with descriptive statistics, to identify different polymorphs from measurements of Raman... 

The number of subjects per cohort needed for the initial study depends on several factors. If a well established pharmacodynamic measurement is to be used as an endpoint, it should be possible to calculate the number required to demonstrate significant differences from placebo by means of a power calculation based on variances in a previous study using this technique. However, analysis of the study is often limited to descriptive statistics such as mean and standard deviation, or even just recording the number of reports of a particular symptom, so that a formal power calculation is often inappropriate. There must be a balance between the minimum number on which it is reasonable to base decisions about dose escalation and the number of individuals it is reasonable to expose to a NME for the first time. To take the extremes, it is unwise to make decisions about tolerability and pharmacokinetics based on data from one or two subjects, although there are advocates of such a minimalist approach. Conversely, it is not justifiable to administer a single dose level to, say, 50 subjects at this early stage of ED. There is no simple answer to this, but in general the number lies between 6 and 20 subjects. 

There are two principal forms of statistics descriptive and inferential. The purpose of descriptive statistics is to give a description of the data that have been collected, whether from a clinical trial, epidemiological investigation or survey. Inferential statistics is aimed at making probability-based statements about h)q)otheses, parameters of populations, etc. 

A major part of descriptive statistics is the use of graphical methods to represent data. It is not the scope of this chapter to cover graphical methods however it is good statistical practice to produce a visual summary of data. In the following sections we concentrate on summary statistics that describe important aspects of data. 

Up to now we have been discussing descriptive statistics. Inferential statistics uses statistical techniques to make inferences about wider populations from that from which our data are drawn. This involves making estimates and hypothesis testing. 

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