Survival data prism,erectile dysfunction drugs free trial 360,vacuum pump for erectile dysfunction in india 2015 - New On 2016

Progression-free survival (PFS) as surrogate endpoint for overall survival (OS) in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer (MBC): An individual patient data (IPD) analysis.
Evaluation of disease-free survival as surrogate endpoint for overall survival using two individual patient data meta-analyses of adjuvant chemotherapy in operable non-small cell lung cancer. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
I have the following survival data and have constructed a survival plot however cannot mark the right censored points (which makes me think my survival graph is also incorrect) - how can I manage this please? Not the answer you're looking for?Browse other questions tagged data-visualization stata survival or ask your own question. How can I give sufficient XP per session for 7 high-level PCs without encounters being a slog?
Many papers reported the recent findings concerning long-term survival rates of cancer patients that indicated an alternative method of looking at the data yielded more favorable rates. The survival cohorts consist of different people and so there are wobbles in the data, as indicated by the standard errors in the table.
For most presentations, this table with its structure and reporting of standard errors will be the best way to see the cancer data. Applying the widely-used default designs for statistical graphics in PowerPoint to this nice straightforward table yields these analytical disasters below.
For such small data sets, usually a simple table shows the data more effectively than a graph, let alone a chartjunk graph. I disagree.Conveying proportional relationships between the types of Cancer is extraneous to the intent of the graphic.
I see now that the flush-left labels in the table-graphic make it easier to find the cancers, just as it would in an ordinary table. I work closely with English Cancer Treatment Networks and am an health information analyst, and this thread is like fresh air. When I hear an overly definitive analysis of medical causality applied to a single individual, I ask the analyst "Where, then, is your Nobel Prize in Medicine?" Even cancers blamed on "lifestyle" (an awful euphemism) now sometimes appear to be a product of the blamed behavior interacting with certain inherent genetic factors specific to the patient.
In having had all too many dogs treated for cancer, I have noticed among veterinarians and in most of the writings on the subject a wonderful absence of blame, punitive metaphors, accusation, and guilt-provocation. Kindly Contributor "Zuil Serip" asks shouldn't the survival rates be monotonically decreasing? Many other anomalies due to improved diagnostic practice and insurance incentives to vary coding or use preferred treatments could drive a change between cohorts. This is a belated response to Zuil's and Bill's questions about the survival rates for some cancers going up.



Is there a way to create a graphic like the cancer survival ones above using a program other than Adobe Illustrator? It doesn't mark the censoring times by default, but its help page states that the censored() option gives three different ways of marking censoring times, depending how you want to deal with multiple individuals censored at the same timepoint. The table-graphic below, however, gives an idea of survival time gradients for each cancer.
In general, the zero point should only be shown if it occurs reasonably near the range of the actual data.
The spacing between values in the five-year survival rate column is not proportional, resulting a visual element inconsistent with the actual values. The graphic needs to merely show survival time gradients for each Cancer type no matter how gravely low their numbers may be.The data stacking in order of starting percentage variables at 5 years seems incidental and an echo from the first redraw of the orginal table. Or more accurately, taking a cue from the Princeton acceptance letter dialogue:Wow!An elegant balance between complex content -- still readily available for the looking -- and an economy of form that actually adds content.
Also, as a result of triage by blame, a blamed patient may receive lower quality medical care than an unblamed patient. Instead, cancer at the level of treating the dog patient is simply a very nasty problem to be thought through deeply and rationally--and, if possible, solved. Yet for "Liver, bile duct" the data seem to imply that survival rates are higher at 20 years than at either 10 or 15 years.
However, these are presumably not data for the same cohort, but %age of patients alive from those treated 5, 10, 15, and 20 year ago. If this is the ONLY disease with better 20yr survival than 10 or 15 year, the cost containment has been less malign than widely suspected. If some non-life-threatening disease X was 20 years ago bundled under "Liver Cancer", the blended cohort of cancer survivors and X "survivors" would have a combined mortality better than a pure cohort. The original article provided this table of relative survival rate (and standard error) for various types of cancer. The journalistic allergy to tables of data in the news section (not in the sports or financial section however) denied their readers some interesting information.
In the table-graphic and in the original table, every visual element contributes directly to understanding. The data explode into 6 separate chaotic slides, consuming several times the area of the table.
Kouchoukos, et al., "Replacement of the Aortic Root with a Pulmonary Autograft in Children and Young Adults with Aortic-Valve Disease," New England Journal of Medicine, 330 (January 6, 1994), p.
Instead of empty space vertically reaching down to a number which never occurs empirically, the way to show context is more data horizontally.


In some cases a difference of just one percent results in a larger gap than one of as much as six percent. In the first redraw the white space does not hint anywhere that it should convey information, so the stacking order manifests itself as convenience rather than significant data. Some of those with very high survival rates are also very common (breast cervix and prostate), whilst some of the most deadly are also thankfully very rare (pancreas, and liver).
Everything is wrong with these smarmy, nearly unreadable graphs: incoherent, uncomparative, low data-density, encoded legends, color without content, logotype branding, chartjunk, indifference to content and evidence. Note that The Visual Display of Quantitative Information never recommends showing zero-points. Perhaps an additional column with the initial survival rates plotted proportionally could be added while preserving the elegance of the original graphic. It is difficult to guess how the original by Hermann Brenner determined its stacking order. Chartjunk is a clear sign of statistical stupidity; use these designs in a presentation and your audience will rightly conclude that you don't know all that much about statistical data. Poking a finger into the eye of thought, these graphics would turn into a particularly nasty prank if ever used for a serious purpose, such as cancer patients seeking to assess their survival chances. Tufte, The Visual Display of Quantitative Information (Cheshire, CT, 1983; second edition, 2001), chapter 5. 74-75 for a sequence of displays that provide increasing context by showing more data horizontally rather than reaching down to a zero point.
Such a stacking order might be more important to the trend information in terms of lives lost.Using the percentage variable at 5 years as a stacking order could cause confusion (and it seems it has?) that the graphic should convey more than it does.
To deal with a product that clutters and corrupts data with such systematic intensity must require an enormous insulation from statistical reasoning by Microsoft PP executives and programmers, PP textbook writers, and presenters of such chartjunk. The graphic could list the Cancer types alphabetically for a more impartial trend analysis where the starting percentage variable is moot. To enable the graphic to go beyond graphical integrity and become useful to ordinary people to facilitate the process of change, we need some ideological dimensions too.



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