Blinded data

In tong term trials there will usually be an opportunity to check the assumptions which underlay the original design and sample size calculations. This may be particularly important if the trial specifications have been made on preliminary and/or uncertain information. An interim check conducted on the blinded data may reveal that overall response variances, event rates or survival experience are not as anticipated. A revised sample size may then be calculated using suitably modified assumptions... ... 

Note that this calculation must be undertaken on the blinded data to avoid any formal or informal treatment comparison. If such a comparison were to be made then there would be a price to pay in terms of the type I error. We will say much more about this in a later section dealing with interim analysis where the goal is to formally compare the treatment arms as the data accumulates. 

The blind review does offer an opportunity to make some final changes to the planned statistical methods and this opportunity should not be missed but remember this is based on blinded data. 

This document has set down some initial thoughts from a regulatory point of view about the issues involved in allowing the design of a clinical trial to be adapted as the trial progresses. Modification of the sample size based on blinded data and stopping for overwhelming efficacy or futility are forms of adaptation that are already well accepted, but this Reflection Paper considers other possibilities that are more controversial. 

Fig. 1.8 Clinical trial simulation of the response (%) in blinded data after enrolment of 50% of the patients.

Once the Phase III clinical trials were started, a blinded data survey was done when 50% of the patients were enrolled. This is not an uncommon practice. Blinded data can be used to assess the appropriateness of assumptions that were made when designing the trials, such as the overall response or variability, among others. However, to judge the observed data it is necessary to have an idea of what reasonably can be expected. Due to the multitude of factors involved, clinical trial simulations can help here. The observed blinded response rate was lower than the antipicitated value, but was well within what could be expected from the simulation results (Fig. 1.8). Finally, when the true outcome of the Phase III trial was compared with the predictions from the clinical trial simulations, an overall good agreement was observed (Fig. 1.9). 

The blinded data was the only valid data. However, Eli Lilly ran roughshod over science by breaking the blind in providing its new evaluators data indicating what each of the patients were taking when they were found to have attempted suicide. Thus, when evidence-based ... 

Training requirements for pharmacologists and toxicologists Definition of nomenclature project, study, indication, NCE ID, etc. Review and approval process for PK summaries and related reports Standards for PK data analysis basic parameters to be obtained Use of blinded data... 

Analysis plan A detailed description of the intended analysis written before un-blinding data in the pharmaceutical industry and somewhat later, if at all, elsewhere. 

Vertical blinds. [Data from Rapacki, S. R., J. Vinyl Additive Technol., 4,1, 12-21,1998.]... 

Gao F, Zhang Z, Gao Y, et al. 2010. Evaluation model of rock burst risk in mining based on blind data theory. Journal of China Coal Society 35(S) 28-32. 

Finally, as researchers, we should never lose sight of the fact that chemical intuition and knowledge will never be substituted by blind data analysis. The vibrant debate on how to analyze the data from complex cooperative assemblies is a good example of how work in this area can stimulate research but research on complex supramolecular assemblies is undoubtedly going to be one of the key growth areas in the near future. The methods required to understand and quantify these interactions, including the necessary mathematical equations and robnst computational approaches for data analysis, are likely to be the subject of considerable effort as the field advances. 

National Eye Institute (2004). Statistics and Data prevalence of blindness data. U.S. National Institutes of Health, National Eye Institute, Bethesda, MD. MD.http //www.nei.nih.gov/evedata/pbd tables.asp... 

Classifier structures resulting from the training were verified in a blind test. To evaluate the reliability and performance of the NSC it was subjected to a blind test using unknown data containing spectra measured for various sizes and locations of the disbonds (from 50% to over 100% of the probe size). 

Blind test data was classified 100% correctly between flawless and defect samples. Layer containing flaw was determined correctly in 97.2% of the cases (see Table 2 for details). 

Much of the experience and data from wastewater treatment has been gained from municipal treatment plants. Industrial liquid wastes are similar to wastewater but differ in significant ways. Thus, typical design parameters and standards developed for municipal wastewater operations must not be blindly utilized for industrial wastewater. It is best to run laboratory and small pilot tests with the specific industrial wastewater as part of the design process. It is most important to understand the temporal variations in industrial wastewater strength, flow, and waste components and their effect on the performance of various treatment processes. Industry personnel in an effort to reduce cost often neglect laboratory and pilot studies and depend on waste characteristics from similar plants. This strategy often results in failure, delay, and increased costs. Careful studies on the actual waste at a plant site cannot be overemphasized. 

In the limit Wpsp 0 or if no calibration data are available, myopic deconvolution becomes identical to blind deconvolution which involves to find the PSF and the brightness distribution of the object from only an image of the object. 

Figure 5 shows an example of blind deconvolution by the resulting algorithm applied to simulated data. Of course the interest of blind deconvolution is not restricted to astronomy and it can be applied to other cases for which the instrumental response cannot be properly calibrated for instance in medical imaging (see Fig. 6a and Fig. 6b). 

A series of calibration standards (CS) is made up that covers the concentration range from just above the limit of detection to beyond the highest concentration that must be expected (extrapolation is not accepted). The standards are made up to resemble the real samples as closely as possible (solvent, key components that modify viscosity, osmolality, etc.). A series of blinded standards is made up (usually low, medium, high the analyst and whoever evaluates the raw data should not know the concentration). Aliquots are frozen in sufficient numbers so that whenever the method is again used (later in time, on a different instrument or by another operator, in another laboratory, etc.), there is a measure of control over whether the method works as intended or not. These so-called QC-standards (QCS) must contain appropriate concentrations of all components that are to be quantified (main component, e.g., drug, and any impurities or metabolites). 

Apparently there are psychiatrist and nonpsychiatrist clinicians whose experience convinces them that MDMA can have therapeutic uses, mainly as an adjunct to psychotherapy. Despite these convictions, there appear to be no published data to support these claims. There is an urgent need for objective data from well-controlled, blinded clinical studies, if these claims of therapeutic usefulness are to be taken seriously. If a bona fide use is evident, then it may be possible to produce other drugs with the same desirable action, lacking the toxicity inherent in MDMA. 

The randomization of a patient in a given therapy is the cornerstone of a randomized clinical trial. You may find these data in more than one place. They are often found within some form of Interactive Voice Response System (IVRS), but they may also be found in an electronic file containing the treatment assignments or on the CRF itself. If randomization data are found on the CRF, they usually consist only of the date of randomization for treatment-blinded trials. IVRS data are often found outside the confines of the clinical data management system and usually consist of the following three types of data tables. 

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