Data flag

Data qualification consists of placing a data qualifier, also known as a data flag, next to the numerical value that is uncertain. Data qualifiers are usually the letters of the Latin alphabet. The most common data qualifier is U, used to identify undetected compounds as illustrated in Table 4.2. 

In the course of data validation, data qualifiers are attached to the data. Data qualifiers are the alphabetic symbols that indentify an undetected compound or a deviation from acceptance criteria. Data qualifiers are also called data flags. The findings of data validation are detailed in a data validation report, which documents the validation process and explains the reasons for attaching the qualifiers to the data. Laboratories also use data qualifiers for indicating deviations from laboratory acceptance criteria. These qualifiers are replaced with the validation qualifiers in the course of data validation. Qualifiers are rarely used in data review. 

The integrated ion current for each analyte ion listet in Table 5 must be at least 2.5 times background noise and must not have saturated the detector. The internal standard ions must be at least 10.0 times background noise and must not have saturated the detector. However, if the M-[COCl]+ ion does not meet the 2.5 times S/N requirement but meets all the other criteria listed in Section 11 and in the judgement of the GC/MS Interpretation Specialist die peak is a PCDD/PCDF, the peak may be reported as positive and the data flagged on Form I. 

At this time more than 85)1 of the archival test data processed have been acted upon by the programs without manual intervention. The errer checks built into the programs also identify input errors for current experiments. Specific experiments rejected by any of the programs are reviewed by the appropriate Individual, corrections made as required, and the data flagged for reprocessing. 

Error flag, integer variable normally zero ERG = 1 indicates binary data are missing. 

The results processor computes the test results from the raw data furnished by the AP and coUates these results together with the demographic patient data into test reports. Test results falling outside normal limits are flagged on the report to speed up the diagnosis process. These data managers can also store thousands of patient reports in their current memory. Some of the more sophisticated systems also store the actual reaction curves used to determine the test results. 

All data should be checked for validity and to determine if they are within reasonable limits. Data that are beyond predetermined limits should be discarded and flagged for investigation. An unreasonable result or analysis should set up a routine for identification of possible discrepant input data. 

If Modify is chosen from the menu, a drop-down menu with the available data types appears Family, Event " ees, Systems, End States, Basic Events, Attributes, Analysis Types, Gates, Hi rams, P Ls, Change Sets, and Flag Sets. After selecting an option, a dialog containing a li f of all records for the selected data type appears. The functions Add, Copy, Modify, and C be selected from another pop-up menu. 

The graphioal output from the computer shows the process flowsheet, with several separation units and projeoted equipment and operating costs. It also flags information that is uncertain because it had to use thermodynamio data extrapolated from measured values. At the engineer s request, the oomputer shows several alternative flowsheets it had considered, indicates their projected costs, and tells why it eliminated eaoh of them. Some of the flowsheets were eliminated because of high cost, others beoause they were oonsidered unsafe, others because the startup procedures would be difficult, and still others because they were based on uncertain extrapolation of experimental data. 

An automated system that immediately validates and flags areas where human intervention is required, coupled with a second layer of more extensive validation executed by data management specialists... 

Validation of the data management system is typically done in two rounds. First, correctly completed data forms are entered to ensure that the system is not flagging any good data. In the second round, completed data forms with intentional data errors are entered. All errors must be identified by the system. 

The list of available data sources associated with a specific concept type (source, concept type, flag security, position in the list of displayed Ultra-Links for that source)... 

Usually a separate CRF is used to capture serious adverse events, as those must be reported to the FDA within 24 hours. That often means that the serious adverse events CRF data and the regular trial CRF adverse events are collected in different data tables, if not entirely different software systems. Pharmaceutical companies often want to reconcile the two databases to ensure that all serious adverse events appear in the regular-trial CRF adverse events database and that any event in the serious adverse events database is flagged properly as serious in the regular CRF adverse events database. 

Often you want to redefine an already existing variable within a SAS DATA step. As simple as this may sound, it can lead to unexpected results if not done carefully. The following example displays some unexpected behavior that may occur when you redefine a variable within a DATA step. In this example you want to flag the subject who had the Fatal MI adverse event as having died (death =1). 

FLAG EVENTS THAT RESULTED IN DEATH data aes ... 

FLAG LAB VALUE AS LOW OR HIGH data labs set labs ... 

So for every clinical event of concern there is an event binomial flag and a time-to-event variable. Time-to-event data sets are typically represented in a flat denormalized single observation per subject data set. 

SUBJECT = PATIENT NUMBER, SEIZURE = BOOLEAN FLAG INDICATING A SEIZURE AND SEIZDATE = DATE OF SEIZURE. data seizure informat seizdate date9. format seizdate date9. label subject = "Patient Number" seizdate = "Date of Seizure" seizure = "Seizure l=Yes,0=No" input subject seizure seizdate datalines ... 

CREATE LASTERC FLAG AT THE END OF THE DATA SET FOR A SPECIAL END OF REPORT LINE IN PROC REPORT. lastrec = eof ... 

This DATA step rearranges the counts data set created by PROC FREQ. The data set is essentially merged with itself three times in order to get each treatment into its proper column. A group variable is created to help separate the ANY MEDICATION row from the other true medications. Percentages are calculated, and the columns (coll-col3) are formatted as XXX (XXX%). Finally, the lastrec variable is created to help make a continuation flag in the PROC REPORT output. 

The following is a table specification for a laboratory normal range shift table. In order to create this table, you need to have a laboratory data set where the lab values have been flagged as normal, low, or high. The highlighted items in the table shell are parameters that change for the laboratory data in the study. 

CARRY FORWARD BASELINE LABORATORY ABNORMAL FLAG. data lb set lb ... 

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