Data, historical

http //www.fda.gov/downloads/AdvisoryCommittees/ CommitteesMeetingMaterials/Drugs/Endocrinologicand MetabolicDrugsAdvisoryCommittee/UCM148109.pdf 

Reference/Control Type Number of Subjects (N) Number of CV Events Total Patient Years Annualized Event Rate (%) 

In subsequent subsections, we discuss how to elicit the fitting priors using these five historical datasets shown in Table 2.2. 

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It Is important to know how much each well produces or injects in order to identify productivity or injectivity changes in the wells, the cause of which may then be investigated. Also, for reservoir management purposes (Section 14.0) it is necessary to understand the distribution of volumes of fluids produced from and injected into the field. This data is input to the reservoir simulation model, and is used to check whether the actual performance agrees with the prediction, and to update the historical data in the model. Where actual and predicted results do not agree, an explanation is sought, and may lead to an adjustment of the model (e.g. re-defining pressure boundaries, or volumes of fluid in place). 

Our near future improvements include automatic defect detection and reporting, and integration of reports into Word or Excel documents. On the long nin, automatic comparison with historical data will be possible, and if acquisition cards improve, acquisition may be perfomied by the analysis tools. 

These tests generate several Gigabytes of data that are fed into a historical database. Although most of the analysis is performed automatically, human interaction is still needed to compare current and past data. Data are stored on optical CD S s from which the historical data bank are retrieved during field inspections from a mobile unit. Each of these is equipped with a CD-jukebox linked to an analysis station. The jukebox can handle 100 CD s, enough to store all previously recorded data. A dedicated software pre-fetches the historical data and compares it on-line with the newly acquired NDT-data. It is based on fuzzy algorithms applied to signal features. 

An outstanding advantage of common differential pressure meters is the existence of extensive tables of discharge coefficients ia terms of beta ratio and Reynolds numbers (1,4). These tables, based on historic data, are generally regarded as accurate to within 1—5% depending on the meter type, the beta ratio, the Reynolds number, and the care taken ia manufacture. Improved accuracy can be obtained by miming an actual flow caUbration on the device. 

Somatotropin, the P-adrenergic agonists, and the anaboHc steroids are considered metaboHsm modifiers because these compounds alter protein, Hpid, carbohydrate, mineral metaboHsm, or combinations of these and they partition nutrient use toward greater rates of protein deposition, ie, muscle growth, and lesser rates of Hpid accretion. Historical data leading to understanding of the mechanism (s) of action are found in reviews on anaboHc steroids (1), somatotropin (2—4), and the phenethano1 amines (5—7). 

From historical data and Procter Gamble analyses. 

The historical data is sampled at user-specified intervals. A typical process plant contains a large number of data points, but it is not feasible to store data for all points at all times. The user determines if a data point should be included in the list of archive points. Most systems provide archive-point menu displays. The operators are able to add or delete data points to the archive point hsts. The samphng periods are normally some multiples of their base scan frequencies. However, some systems allow historical data samphng of arbitraiy intei vals. This is necessaiy when intermediate virtual data points that do not have the scan frequency attribute are involved. The archive point lists are continuously scanned bv the historical database software. On-line databases are polled for data. The times of data retrieval are recorded with the data ootained. To consei ve storage space, different data compression techniques are employed by various manufacturers. 

Frequency Estimation There are two primary sources for estimates of incident frequencies. These are historical records and the apphcation of fault tree analysis and related techniques, and they are not necessarily applied independently. Specific historical data can sometimes be usehiUy applied as a check on frequency estimates of various subevents of a fault tree, for example. 

The use of historical data provides the most straightforward approach to the generation of incident frequency estimates but is subject to the apphcability and the adequacy of the records. Care should be exercised in extrac ting data from long periods of the historical record over which design or operating standards or measurement criteria may have changed. 

Maintenance/ The QRA team will need information about process configurations reliability during maintenance activities and historic data on equipment per-... 

The accuracy of absolute risk results depends on (1) whether all the significant contributors to risk have been analyzed, (2) the realism of the mathematical models used to predict failure characteristics and accident phenomena, and (3) the statistical uncertainty associated with the various input data. The achievable accuracy of absolute risk results is very dependent on the type of hazard being analyzed. In studies where the dominant risk contributors can be calibrated with ample historical data (e.g., the risk of an engine failure causing an airplane crash), the uncertainty can be reduced to a few percent. However, many authors of published studies and other expert practitioners have recognized that uncertainties can be greater than 1 to 2 orders of magnitude in studies whose major contributors are rare, catastrophic events. 

Historical data management—This includes the data acquisition and storage capabilities. Present-day prices of storage mediums have been dropping rapidly, and systems with 80 gigabyte hard disks are available. These disks could store a minimum of five years of one-minute data for most plants. One-minute data is adequate for most steady state operation, while start-ups and shutdowns or other non steady state operation should be monitored and stored at an interval of one second. To achieve these time rates, data for steady state operation can be obtained from most plant-wide D-CS systems, and for unsteady state conditions, data can be obtained from control systems. 

Historical data on similar vessels and fractionation towers can best be used by correlating the costs of this equipment vs. weight. Many methods can be found in the literature for estimating the weight and costs of vessels and fractionators (References 8. 9, 10, and 11). Make sure the estimated weight is complete including skirt, ladders and platfonns, special internals, nozzles, and manholes. 

Once the weight has been determined, the cost is obtained by multiplying by a /lb figure. Up-to-date numbers for the type of vessel or fractionator being estimated can be obtained from a vendor, in-house historical data, literature, or estimating books such as Reference 12. Make sure the cost reflects the materials of construction to be used. 

The price of air-cooled exchangers should be obtained from vendors if possible. If not, then by coirelating in-house historical data on a basis of /ft of bare surface vs. total bare surface. Correction factors for materials of construction. pressure, numbers of tube rows, and tube length must be used. Literature data on air coolers is available (Reference 15). but it should be the last resort. In any event, at least one air-cooled heat exchanger in each project should be priced by a vendor to calibrate the historical data to reflect the supply and demand situation at the expected time of procurement. 

Since the adjustments for inflation are so large, it is important to fix the date for historical data as closely as possible. For instance, a historical cost estimate from a vendor or contractor for equipment to be delivered in two years would have escalation built in, so the index should be for two years later, when the equipment was expected to be manufactured. However, data based on purchased equipment delivered on a certain date should use the index for the date the equipment was manufactured. 

Once the cost of each piece of major equipment is known, it must be adjusted by construction cost indexes. Due to inflation and changing competitive situations, the price of equipment changes from year to year (Reference 26). Fortunately, there are several indexes that help in estimating today s costs based on historical data. Some of these indexes are Nelson Refinery Construction Cost... 

A factor of 0.6 is often used in lieu of literature or historical data, so this estimating technique is commonly referred to as the 0.6 factor method. 

Every location has advantages and disadvantages that impact on the operating costs. Data must be collected for the proposed site and used to adjust historical data to get a proper estimate. Such costs as transportation, labor, and purchased utilities can be determined. However, such items as operating labor productivity, local customs, weather, and local management attitudes can only be estimated. 

Produces internal control documentation and wemmentally required reports. Manifest printing from files containing information on approved transporters and disposers, waste materials, and historical data. 

Quantitative assessment requires historical data which may be suspect for two reasons. There is the possibility that there are latent accidents not in the database. It is possible that past accidents have been rectified and will not recurr. In the absence of data, judgment based on experience and speculation must be used. Notwithstanding this weakness, the quantitative approach was adopted, d he investigating team identified situations that could cause a number of public casualties. R vents limited to the employees or which might cause single off-site casualties were not included in the assessment. 

Typical events that are considered are fire, explosion, ship collision, and the failure of pressurized storage vessels for which historical data established the failure frequencies. Assessment of consequences was based partly on conservative treatment of past experience. For example ilic assessment of the number of casualties from the release of a toxic material was based on past histoiy conditioned by knowledge of the toxicology and the prevailing weather conditions. An altemati. e used fault trees to estimate probabilities and identify the consequences. Credit is taken in this process for preventative measures in design, operation, and maintenance procedures. Historical data provide reliability expected from plant components and humans. 

Both the.se methods have weaknesses. The historical data, implicit in both, may not be appropriate for the component in the assumed environment for the particular circumslances considered and may be irrelevant because of design rectification based on knowledge of previous failure. The broad approach may then be unduly pessimistic. On the other hand, the fault tree may fail to identify a primary cause which may have been missed by the plant designer to underestimate the probability of failure. The fault tree approach also takes credit for the preventative measures which may not be present in practice. The broad approach is likely to overestimate the risks because of insufficient account of preventative measures. The PSA team used the broad approach, recognizing that more accuracy may be attained by detailed industry studies. 

Class F No demonstrated capability. This is the class of those subcontractors you have not used before and therefore have no historical data. 

Unfortunately the address of the gateway in the control computer used for the data transfer was the same as that used to connect to the distributed control system (dcs). As a result data flowed from the simulator through the control computer to the dcs and replaced the current input data by historic data. Some conditions on the plant started to change, but fortunately this was soon noticed by alert operators, and the plant was brought back under control. 

When using failure rate data for a CPQRA, the ideal situation is to have valid historical data from the identical equipment in the same application. In most cases, plant-specific data are unavailable or may carry a level of confidence that is too low to allow those data to be used without corroborating data. Risk analysts often overcome these problems by using generic failure rate data as surrogates for or supplements to plant-specific data. Because of the uncertainties inherent in risk analysis methodology, generic failure rate data are frequently adequate to identify the major risk contributors in a process or plant. 

Event frequencies estimated from historical data failure rates from System Reliability Service... 

The bulk of the information in the report is included in a 317-page appendix that contains systems descriptions, station blackout fault trees, diesel generator historical data, and diesel generator common cause failure analysis results for 18 different nuclear power plants. Tables and graphs are well organized and present data correlated to each plant studied. The study also contains conclusions and recommendations for improving reliability. 

Historical data Data receded from actual past experience. 

Historical data are used for facility types where tliere is extensive experience available from similar or identical installations. 

Failure sequence modeling techniques such as fault tree analysis or event tree analysis are used to estimate tlie likelihood of incidents in facilities where historical data is unai ailable, or is inadequate to accurately estimate tlie likelihood of the liazardous incidents of concern. Otlier modeling tecluiiques may be required to consider tlie impact of external events (eartliquakes, floods, etc.), common cause failures, and human factors and hmnan reliability. 

Expert Judgment quantifies an expert s state of knowledge or perceptions of the likelihood of an incident. This knowledge may be based on historical data, insights gained from models, experience, or a combination of these factors. 

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