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SAS (Statistical Analysis System) is a software suite for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. SAS can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

When performing a Gage R & R study, it is vital that the data be random in nature. It is standard practice to have multiple appraisers measure the same set of parts in a random order. It is common practice to use two or three appraisers and 5 or 10 parts. The purpose of this study is for example only and not an analysis of the process. In this study, two appraisers / operators measured a set of ten parts two times in random order and recorded the data. The parts were representative of the entire range of process output. The measurements for the study were taken using digital calipers. The unit of measure is decimal inches.

Statistical Analysis System.full.rar

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The results of this study using include % Contribution, % Study Variance and % Tolerance statistical reports. Each of these statistics provides valuable information; how you interpret each of them depends on the purpose of the study.

In conclusion, the % Tolerance results indicate that the measurement system is capable of detecting a non-conforming part. The data results of our study indicate no apparent problems with our measurement system. For further examination, the data can be interpreted through graphical analysis methods.

Graphical analysis is often included in a Gage R & R study. The graphical analysis methods can validate the study findings and give additional insight regarding the data. Graphical analysis is an effective method for visualizing the data. Remember, the graphs shown illustrate the effectiveness of the measurement system, not the manufacturing process. This study includes the following graphs:

TopSpin is Bruker's standard NMR software used in a wide range of workflows. Starting with the control of the spectrometer up to the processing and analysis of multidimensional NMR spectra. The acquisition interface provides easy access to vast NMR experiment libraries including standard Bruker pulse sequences and user generated experiment libraries, for both routine and advanced NMR users. TopSpin provides numerous options for setting up and optimizing NMR experiments, making the setup of sophisticated experiments simple and efficient. For users who must be compliant with the GxP regulations, the software supports the various principles of data integrity.

TopSpin offers a fully workflow-oriented user interface and leverages the latest 64-bit features of modern Windows / CentOS / macOS operating systems for optimal performance. The software is designed to accelerate the operation and throughput of sample analysis for increased cost efficiency.

We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Telmisartan and Insulin Resistance in HIV (TAILoR) was a phase II dose-ranging multi-centre randomised open-label trial investigating the potential of telmisartan to reduce insulin resistance in HIV patients on combination antiretroviral therapy [31]. It used a MAMS design [32] with one interim analysis to assess the activity of three telmisartan doses (20, 40 or 80 mg daily) against control, with equal randomisation between the three active dose arms and the control arm. The primary endpoint was the 24-week change in insulin resistance (as measured by a validated surrogate marker) versus baseline.

The interim analysis was conducted when results were available for half of the planned maximum of 336 patients. The two lowest dose arms were stopped for futility, whereas the 80 mg arm, which showed promising results at interim, was continued along with the control. Thus, the MAMS design allowed the investigation of multiple telmisartan doses but recruitment to inferior dose arms could be stopped early to focus on the most promising dose.

Once funding has been secured, one of the next challenges is to obtain ethics approval for the study. While this step is fairly painless in most cases, we have had experiences where further questions about the AD were raised, mostly around whether the design makes sense more broadly, suggesting unfamiliarity with AD methods overall. These clarifications were easily answered, although in one instance we had to obtain a letter from an independent statistical expert to confirm the appropriateness of the design. In our experience, communications with other stakeholders, such as independent data monitoring committees (IDMCs) and regulators, have been straightforward and at most required a teleconference to clarify design aspects. Explaining simulation results to stakeholders will help to increase their appreciation of the benefits and risks of any particular design, as will walking them through individual simulated trials, highlighting common features of data sets associated with particular adaptations.

Being clear about the design of the study is a key requirement when recruiting patients, which in practice will be done by staff of the participating sites. While, in general, the same principles apply as for traditional designs, the nature of ADs makes it necessary to allow for the specified adaptations. Therefore, it is good practice to prepare patient information sheets and similar information for all possible adaptations at the start of the study. For example, for a multi-arm treatment selection trial where recruitment to all but one of the active treatment arms is terminated at an interim analysis, separate patient information sheets should be prepared for the first stage of the study (where patients can be randomised to control or any active treatment), and for the second stage, there should be separate sheets for each active versus control arm.

Various AD methods have been implemented in validated and easy-to-use statistical software packages over the past decade [21, 56, 57]. However, especially for novel ADs, off-the-shelf software may not be readily available, in which case quality control and validation of self-written programmes will take additional time and resources.

In addition to these practical challenges around planning and running a trial, ADs also require some extra care when making sense of trial results. The formal numerical analysis of trial data will likely be undertaken by a statistician. We recommend consulting someone with expertise in and experience of ADs well enough in advance. The statistician can advise on appropriate analysis methods and assist with drafting the statistical analysis plan as well as pre-trial simulation studies to assess the statistical and operating characteristics of the proposed design, if needed.

The analysis of an AD trial often involves combining data from different stages, which can be done e.g. with the inverse normal method, p value combination tests or conditional error functions [70, 71]. It is still possible to compute the estimated treatment effect, its CI and a p value. If these quantities are, however, naively computed using the same methods as in a fixed-design trial, then they often lack the desirable properties mentioned above, depending on the nature of adaptations employed [72]. This is because the statistical distribution of the estimated treatment effect can be affected, sometimes strongly, by an AD [73]. The CI and p value usually depend on the treatment effect estimate and are, thus, also affected.

Illustration of bias introduced by early stopping for futility. This is for 20 simulated two-arm trials with no true treatment effect. The trajectories of the test statistics (as a standardised measure of the difference between treatments) are subject to random fluctuation. Two trials (red) are stopped early because their test statistics are below a pre-defined futility boundary (blue cross) at the interim analysis. Allowing trials with random highs at the interim to continue but terminating trials with random lows early will lead to an upward bias of the (average) treatment effect

While this paper focuses on frequentist (classical) statistical methods for trial design and analysis, there is also a wealth of Bayesian AD methods [100] that are increasingly being applied in clinical research [23]. Bayesian designs are much more common for early-phase dose escalation [101, 102] and adaptive randomisation [103] but are gaining popularity also in confirmatory settings [104], such as seamless phase II/III trials [105] and in umbrella or basket trials [106]. Bayesian statistics and adaptivity go very well together [4]. For instance, taking multiple looks at the data is (statistically) unproblematic as it does not have to be adjusted for separately in a Bayesian framework.

Besides these statistical issues, the interpretability of results may also be affected by the way triallists conduct an AD trial, in particular with respect to mid-trial data analyses. Using interim data to modify study aspects may raise anxiety in some research stakeholders due to the potential introduction of operational bias. Knowledge, leakage or mere speculation of interim results could alter the behaviour of those involved in the trial, including investigators, patients and the scientific community [116, 117]. Hence, it is vital to describe the processes and procedures put in place to minimise potential operational bias. Triallists, as well as consumers of trial reports, should give consideration to: be457b7860