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The statistical analysis considerations for PRO endpoints are not unlike statistical considerations for any other endpoint used in drug development (see appendix point 9).
In addition, the FDA has previously provided guidance to address the use of computerized systems to create, modify, maintain, archive, retrieve, or transmit clinical data to the agency (see appendix point 6) and to clarify the requirements and application of 21 CFR part 11 (see appendix point 7).
The amount and kind of evidence that the FDA expects to support a labeling claim measured by a PRO instrument is the same as that required for any other labeling claim (see appendix point 2).
When electronic PRO instruments are used, sponsors should plan carefully to ensure that FDA regulatory requirements are met for sponsor and investigator record keeping, maintenance, and access (see appendix point 5).
If an MID is to be applied to clinical study results, it is generally helpful to use a variety of methods to discover whether concordance among methods confirms the choice of an MID (see appendix point 3).
It also describes our current thinking on how sponsors can develop and use study results measured by PRO instruments to support claims in approved product labeling (see appendix point 1).
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In the last appendix, we point out some related results and some consequences of our main theorems.
Straightforward extensions to more complicated systems including convection diffusion reaction, multi-relaxation times in collision operator as well as multi-dimensional Navier Stokes equations are also exposed in the Appendix to point out its expediency in solving complicated flow problems.
With self-awareness come the practical questions of how to initiate help-seeking (see Additional file 1: Appendix 3, point 3.2).
Fears concerning the real or imagined consequences of mental health service use took the form of two principal reasons for avoidance (see Additional file 1: Appendix 4, point 4.2).
In the online appendix, they point out that although the biomarker selection process may not have identified the best possible model, it identified a good model, and that there may have been other combinations of markers with equal performance.
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