a form of censoring I have not met before
Hi folks, I was wondering if anyone could direct me to a reference about the following situation. In a 3-factor experiment, measurements of a continuous variable, which is increasing monotonically over time, are made every 2 hours from 0 to 192 hours on the experimental units (this is an engineering experiment). If the response exceeds a set maximum level the unit is not observed any more (so we only know that the response is that level). If the measuring equipment could do so it would be preferred to observe all units for the full 192 hours. The time to censoring is of no interest as such, the aim is to estimate the form of the response for each unit which is the trace of some curve that we observe every 2 hours. Ignoring the censored traces in the time period after they are censored puts a huge downward bias into the results and is clearly not the thing to do although that's what has been done in the past with these experiments. Any suggestions of where people have addressed data of this or related form would be very gratefully received. TIA, Margaret ___ Margaret Mackisack PhD CStat Consulting Statistician Stillman Mackisack Pty Ltd ABN 65 091 869 091 23 Sherwood St Kurrajong NSW 2758 Australia = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Marijuana
On Fri, 15 Jun 2001 15:23:03 +0100, Paul Jones [EMAIL PROTECTED] wrote: David C. Ullrich wrote: But analyzing it this way simply makes no sense. Those trials you're talking about are _far_ from independent; each trial is associated with a particular person, and there will be a very strong correlation between various trials for the same person at different hours. Okay then, how should it be analysed? I've explained at least twice why I do not believe it is _possible_ to draw the sort of inference you want to draw from the data you've given us. You must be reading _some_ of those posts or you wouldn't keep replying. Take care, Paul All About MS - the latest MS News and Views http://www.mult-sclerosis.org/ David C. Ullrich *** Sometimes you can have access violations all the time and the program still works. (Michael Caracena, comp.lang.pascal.delphi.misc 5/1/01) = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: a form of censoring I have not met before
On 21 Jun 2001 00:35:11 -0700, [EMAIL PROTECTED] (Margaret Mackisack) wrote: I was wondering if anyone could direct me to a reference about the following situation. In a 3-factor experiment, measurements of a continuous variable, which is increasing monotonically over time, are made every 2 hours from 0 to 192 hours on the experimental units (this is an engineering experiment). If the response exceeds a set maximum level the unit is not observed any more (so we only know that the response is that level). If the measuring equipment could do so it would be preferred to observe all units for the full 192 hours. The time to censoring is of no interest as such, the aim is to estimate the form of the response for each unit which is the trace of some curve that we observe every 2 hours. Ignoring the censored traces in the time period after they are censored puts a huge Well, it certainly *sounds* as if the time to censoring should be of great interest, if you had an adequate model. Thus, when you say that ignoring them gives a huge downward bias, it sounds to me as if you are admitting that you do not have an acceptable model. Who can you blame for that? What leverage do you have, if you try to toss out those bad results? (Surely, you do have some ideas about forming estimates that *do* take the hours into account. The problem belongs in the hands of someone who does.) - maybe you want to segregate trials into the ones with 192 hours, or less than 192 hours; and figure two (Maximum Likelihood) estimates for the parameters, which you then combine. downward bias into the results and is clearly not the thing to do although that's what has been done in the past with these experiments. Any suggestions of where people have addressed data of this or related form would be very gratefully received. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Marijuana
David C. Ullrich wrote: On Fri, 15 Jun 2001 15:23:03 +0100, Paul Jones [EMAIL PROTECTED] wrote: David C. Ullrich wrote: But analyzing it this way simply makes no sense. Those trials you're talking about are _far_ from independent; each trial is associated with a particular person, and there will be a very strong correlation between various trials for the same person at different hours. Okay then, how should it be analysed? I've explained at least twice why I do not believe it is _possible_ to draw the sort of inference you want to draw from the data you've given us. You must be reading _some_ of those posts or you wouldn't keep replying. Well, although I've agreed with most of your complaints about trying to derive any information from the scanty data shown, there is *something* we can notice about the data set which has some relevance. Let's say we look at a sampling of 100 people who have both had heart attacks within the last year and have smoked an aspirin an average of once a week during that year. Now, without knowing what the average percentage of people who smoke aspirin each year, and the average percentage of people who have heart attacks each year without smoking aspirin, these numbers alone would be pretty useless. But if 95% of the people in the data set had their 1 heart attack inside of 1 minute after smoking an aspirin, you'd have some reason to further examine the hypothesis that, for some segment of the population, smoking an aspirin could trigger a heart attack. (Of course it could also be that impending heart attacks bring on the desire to smoke aspirin, or some other hypothesis that correlates the two phenomena). One the other hand, one would expect if there were no immediate correlation between smoking aspirin and heart attacks, the average time between smoking aspirin and heart attack would be more like 1/2 week. This would then indicate that it was not particularly worthwhile to investigate an immediate link between asprinin smoking and heart attacks. That seems to be the type of correlation that was reported here - some distribution of MJ smoking, and its *temporal* correlation with heart attacks. Now, that says exactly nothing about whether MJ use increases or decreases the liklihood of having a heart attack in general (it could in fact in general *decrease* heart attacks, even in our data set); but instead would say, there is a segment of the population for whom MJ use is followed by a high liklihood of a heart attack. Would those people have had a heart attack anyway? Is this some small segment of the population that reacts this way? These questions would still remain without any further figures. Even in the abscence of this data, though, one might want to take some precautions during the hour following MJ usage, for those with an otherwise high liklihood of heart attack, such as: be near medical facilities, etc. Cheers - Chas --- C Brown Systems Designs Multimedia Environments for Museums and Theme Parks --- = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
