0:00:00 | hello my name is my case some tiny and on the order of the article |
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0:00:04 | titled disease progression with cd nist in mc i any subjects |
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0:00:11 | the choice of the c d n s one or disease progression analysis in a |
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0:00:15 | patient population close to the answer to dimension |
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0:00:18 | was guided by addison can be addressed guidance undoubtedly grows or the outsiders disease |
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0:00:23 | the draft guidance of course the concept and ridge in clinical trials |
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0:00:26 | the patient's most likely to progress to moreover dementia using what clinical and but are |
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0:00:32 | basic idea |
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0:00:33 | interestingly what patients with this |
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0:00:36 | the draft guidance kinds of std lsp as an example of or was a disease |
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0:00:40 | progression |
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0:00:41 | and as a candidate for a single whatever the efficacy n y |
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0:00:45 | that combines assessment of all mission and function |
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0:00:50 | this is production has coming to describe using linear exponential and logistic structure models in |
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0:00:55 | the literature |
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0:00:57 | in this analysis we formally compared to structure models |
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0:01:00 | the three bottom adjusting model describes an and shape disease progression curve and follows a |
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0:01:05 | nonlinear and such a verbal trajectory that stays within the boundaries of the scale |
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0:01:10 | this analysis indicated that the three but i'm logistic model best describes your data |
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0:01:18 | a problem can be what's is a very but also the regression israel or at |
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0:01:23 | low and high c d n is these scores with human scores of about and |
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0:01:28 | exhibiting faster rates are duration |
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0:01:30 | this involvement use you have relationship is a cat interest a okay and ship progression |
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0:01:34 | curve described by the t parameter logistic model which is also highlighted here |
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0:01:39 | the model predicts that the production rate approaches you don't as the scores of course |
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0:01:43 | the boundaries of the scale |
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0:01:45 | within the range of the scale the production rate is proportional to the current seriously |
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0:01:49 | score one parameter that i just e subject compression rate |
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0:01:54 | and advantage of the lsp is that less and two percent of the observation reside |
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0:01:59 | on the boundaries of the scale for this population and most of the zero one |
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0:02:02 | observations will present the subject that we're by the market negative |
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0:02:06 | moreover a comparison of models with different probability distributions suggested that the larger normal distribution |
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0:02:13 | capture the behavior but assume error appropriately |
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0:02:16 | this is a letter model has the advantage of the model predictions even after accounting |
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0:02:21 | but as to error stay within the boundaries of the scan |
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0:02:25 | a mixture model was to the baseline by marker data |
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0:02:28 | and i shows the density goes based on the mixture model with the estimated threshold |
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0:02:33 | of zero point one or seven what we are living tissue |
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0:02:37 | you don't densities and i don't be sure that cognitively normal and in beaumont the |
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0:02:42 | distributions are quite a state with low and high recalibrated issues respectively |
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0:02:48 | in contrast the and then see i subjects represent a mixture some of the methodology |
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0:02:53 | and some in l |
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0:02:54 | the of the baseline by the mother data on me has regression because it ellen's |
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0:02:59 | i and my e d is shown by l c and d |
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0:03:03 | subjects the baseline be immediate initial below the zero point one four seven to ensure |
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0:03:08 | exhibit a scroll data collection was as those subjects about the first four we show |
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0:03:13 | how much faster progression |
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0:03:17 | equal syllable models that you improvement in more performance |
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0:03:21 | additional task using a bimodal analysis |
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0:03:25 | the first this study assumption |
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0:03:26 | whether the progresses |
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0:03:28 | which to ration represented not regressors this as often was found to be true |
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0:03:33 | this suggests that the csi biomarkers distinguish progressive still not regressors |
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0:03:38 | secondly it was found the best error or not progresses was greater than their vocal |
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0:03:42 | service |
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0:03:43 | this suggests that study nancy i subjects |
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0:03:46 | i do not biomarkers has the potential to reduce errors in a remote controls |
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0:03:53 | additional covariate analysis indicated that episodic memory as measured by the national remember a skin |
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0:03:58 | related logical memory to an executive functioning well also was also you have a regression |
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0:04:04 | the results also suggest that a prosodic boundary is |
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0:04:07 | more predictable progression in latency identity or that it was obvious to be true what |
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0:04:12 | exactly functioning |
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0:04:15 | as part of our analysis it was found that can be does and will have |
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0:04:19 | lower scores as compared to that one complete study |
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0:04:22 | this was done for by the rubber model which suggested that the likelihood of subjects |
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0:04:26 | database missing was related to this score right really about probably now |
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0:04:32 | this was really project was part of the model classification |
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0:04:36 | the rows represent latency i subjects |
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0:04:39 | while the bottom panels |
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0:04:40 | sure my at subjects |
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0:04:42 | because of the lab |
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0:04:43 | after subjects that of a marker negative |
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0:04:46 | where the graph on the right |
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0:04:47 | or something that i by one |
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0:04:49 | the b c suggest that the model described stimulus progression reasoning about in both in |
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0:04:55 | the and they then cfs subjects and by market positive and negative subgroups |
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0:05:01 | in summary csi biomarkers have the ability to discriminate subjects in the progress snr regressors |
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0:05:07 | and that was either one and reading m c i clinical trials |
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0:05:10 | this means of presentation and we hope that you will find out analysis useful |
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