Class registration and file name extension information is stored under both the HKEY_LOCAL_MACHINE and HKEY_CURRENT_USER keys. The HKEY_LOCAL_MACHINE\Software\Classes key contains default settings that can apply to all users on the local computer. The HKEY_CURRENT_USER\Software\Classes key contains settings that apply only to the interactive user. The HKEY_CLASSES_ROOT key provides a view of the registry that merges the information from these two sources. HKEY_CLASSES_ROOT also provides this merged view for applications designed for previous versions of Windows.
You would have to extropolate the registry values to the particular AutoCAD vertical. i.e. MEP, Electrical, Mechanical etc. This process is often called reinitiating the secondary installer. This doesn't work in AutoCAD 2013 anymore and the registry keys are in different locations. Hacking the Registry is so passé these days!
FULL Maya 2007 Registry Keys
This section, method, or task contains steps that tell you how to modify the registry. However, serious problems might occur if you modify the registry incorrectly. Therefore, make sure that you follow these steps carefully. For added protection, back up the registry before you modify it. Then, you can restore the registry if a problem occurs. For more information about how to back up and restore the registry, see How to back up and restore the registry in Windows.
In addition to a full understanding of study design and methodology, analysis of registry events and outcomes requires an assessment of data quality. One must consider whether most or all important covariates were collected, whether the data were complete, and whether the problem of missing data was handled appropriately, as well as whether the data are accurate.
Confounding may be evaluated using stratified analysis, multivariable analysis, sensitivity analyses, and simple or quantitative bias analysis.12 Appropriate methods should be used to adjust for confounding. For example, if an exposure or treatment varies over time and the confounding variable also varies over time, traditional adjustment using conventional multivariable modeling will introduce selection bias. Marginal structural models use inverse probability weighting to account for time-dependent confounding without introducing selection bias.21 The extensive information and large sample sizes available in some registries also support use of more advanced modeling techniques for addressing confounding by indication, such as the use of propensity scores to create matched comparison groups, or for stratification or inclusion in multivariable risk modeling.22-25 New methods also include the high-dimensional propensity score (hd-PS) for adjustment using administrative data.26 The uptake of these approaches in the medical literature in recent years has been extremely rapid, and their application to analyses of registry data has also been broad. Examples are too numerous for a few selections to be fully representative, but registries in nearly every therapeutic area, including cancer,27 cardiac devices,28 organ transplantation,29 and rare diseases,30 have published the results of analyses incorporating approaches based on propensity scores. As noted in Chapter 3, instrumental variable methods present opportunities for assessing and reducing the impact of confounding by indication,31-33 but verification of the assumptions are important to ensure that an instrument is valid.34 Violations in the instrumental variable assumptions or the use of a weak instrument will lead to results more biased than those from conventional methods.35 While a variety of methods have been developed to address confounding, particularly confounding by indication, residual confounding may still be present even after adjustment; therefore, these methods may not fully control for unmeasured confounding.35 For specific examples of the application of these methods, please see Chapter 18. Information bias, such as misclassification, and selection bias are also threats to the validity of our findings and examples can be found in Chapter 18. For further information on how to quantify bias, please see Lash, Fox, and Fink.13
In summary, a meaningful analysis requires careful consideration of study design features and the nature of the data collected. Most typical epidemiological study analytical methods can be applied, and there is no one-size-fits-all approach. Efforts should be made to carefully evaluate the presence of biases and to control for identified potential biases during data analysis. This requires close collaboration among clinicians, epidemiologists, statisticians, study coordinators, and others involved in the design, conduct, and interpretation of the registry.
If you have an equation that has already been produced using Microsoft Word 2007 or 2010 and you have access to the full version of MathType 6.5 or later, you can convert this equation to MathType by clicking on MathType Insert Equation. Copy the equation from Microsoft Word and paste it into the MathType box. Verify that your equation is correct, click File, and then click Update. Your equation has now been inserted into your Word file as a MathType Equation.
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