Change-from-baseline data set

Categorical Data and Why Zero and Missing Results Differ Greatly 102 Performing Many-to-Many Comparisons/Joins 106 Using Medical Dictionaries 108 Other Tricks and Traps in Data Manipulation 112 Common Analysis Data Sets 118 Critical Variables Data Set 118 Change-from-Baseline Data Set 118 Time-to-Event Data Set 121... 

Program 4.14 Creating a Blood Pressure Change-from-Baseline Data Set... 

In this section we take the aforementioned principles and guidelines for analysis data sets and apply them to creating the most common analysis data sets. The critical variables, change-from-baseline, and time-to-event data sets are presented. Although these are the most common analysis data sets that a statistical programmer will encounter, they are by no means all of the possible analysis data sets. When it comes to analysis data sets, there is no limit to the diversity of data that you may have to create. 

The purpose of using change-from-baseline analysis data sets is to measure what effect some therapeutic intervention had on some kind of diagnostic measure. A measure is taken before and after therapy, and a difference and sometimes a percentage difference are calculated for each post-baseline measure. These data sets are generally normalized... 

CALCULATE CHANGE FROM BASELINE SBP AND DBP VALUES. data bp set bp ... 

This PROC TTEST runs a two-sample f-test to compare the LDL change-from-baseline means for active drug and placebo. ODS OUTPUT is used to send the p-values to a data set called pvalue and to send the test of equal mean variances to a data set called variance test. The final pvalue DATA step checks the test for unequal variances. If the test for unequal variances is significant at the alpha =. 05 level, then the mean variances are unequal and the unequal variances p-value is kept. If the test for unequal variances is insignificant, then the equal variances p-value is kept. The final pvalue data set contains the Probt variable, which is the p-value you want. 

At times, particularly if Y is negative, such as may be the case when modeling percent change from baseline as the dependent variable, it may necessary to add a constant c to the data. Berry (1987) presents a method that may be used to define the optimal value of c while still ensuring a symmetrical distribution. The method makes use of skewness and kurtosis, which characterize the degree of asymmetry of a distribution. These statistics will be used to identify a transformation constant c such that the new data set will have better normality properties than the original scores. Let 7, and y2 be the skewness and kurtosis of the distribution as defined by Eqs. (4.36) and (4.37), respectively, substituting Ln(Y + c) for the variable e in Eq. (4.38). If g is defined as... 

Figure 9.13 Index plots of structural model parameter estimates expressed as percent change from baseline using the delete-1 jackknife. Each patient was assigned an index number ranging from 1 to 78 and then singularly removed from the data set. The delete-1 data set was then used to fit the model in Eq. (9.14) using FOCE-I.

Denote Q, i = 1,..., m as estimates of the identified benefit and risk outcomes. Usually, C/s are of different scales or data types, for example, a proportion for a binary outcome and a change from baseline for a continuous parameter. To be able to apply the MCDA approach, the C/s need to be transformed into the same scale to be combined together, for example, between 0 and 1, through some monotone value functions y(x). A simple example of a value function is a linear interpolation when the minimum and maximum levels of a criterion can be identified and their values are set as 0 and 1, respectively. The minimum and maximum levels could be specified based on expert judgment or based on the percentile of the distribution of the criterion. 

Before actual data can be fit to a model, extraneous effects manifested in the trace must be removed, such as the shift in baseline as a result of the change in heat capacity of the sample during the transformation (see section 3.7.2). It may, for some device designs (e.g. post-type DTA), be difficult to purify the instrument output to represent only the latent heat from the transformation because of random baseline float. Hence, the data set fitting a particular model is a necessary but insufficient criterion for guaranteeing that the model describes the measured phenomenon. 

The comparison work also helps to provide a baseline for the monitoring programmes in the event of changing analytical laboratories, or the methods being used for analysis. Results can be assessed against the spread of data from the two laboratories, for the sites compared (as well as against past data sets). 

The acquisition of blank (baseline) runs for comparison with the kinetic data set is to be recommended. Data obtained from suitable blank runs should be subtracted from the main kinetic data in order to correctly define the CD change of interest (i.e. to correct for any CD offset together with any pressure induced artefact present immediately after mixing). 

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