Re: 120 subjects on 120 occassion: a model ?
What I can propose is rather simple, so it may well be completely wrong (especially as no true expert has posted anything on the topic so far), but perhaps it will be of some use: why not pool data for an individual over time-periods - say, months, or to preserve more information, weeks? (Perhaps not by averaging, but - depending on data chracteristics - using median, geometric mean, or some fancy M-estimator?) - This will give you the possibility to conduct an ANOVA-type analysis - mixed model with some "nonrepeated" factors (three fixed, if I get it right, i.e., treat/control, sex and age, plus eventual others) and week (or whatever time-period) as "repeated". As emphasised in the introduction, this may be less than two cents. Best regards, Gaj Vidmar Univ. of Ljubljana, Dept. of Psychology = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: 120 subjects on 120 occassion: a model ?
Dr. Waldman, there seems to be no word from professional statisticians yet, so here's an addenum. Namely, I have overlooked two important aspects of the study; which, hovever, doesn't invalidate the basic idea of pooling individual data over appropriate time-periods. The first aspect are the three groups of patients. - I'm not sure whether they were formed on the basis of the (quote) design variables (in which case there is one factor instead of the three nonrepeated ones), or they define another factor (a rondom one, I guess, as opposed to the three fixed ones), but the pooling approach is independent on this fact. The same goes for the second aspect, i.e., that there were several measures taken, not just one. Theoretically, MANOVA might thus be feasible instead of several ANOVAs. But with such a complex model (say, one random plus three fixed factors plus one repeated-measures factor) properly checking all the various assumptions and interpreting all the results is rather ... Not to mention that the analysis must be properly set up in the first place (contrasts issues ...), as well as sample size issues ... At least, fully understanding such an analysis is probably beyond the horizon of the majority of the "consumers" in social/health sciences, to which you will presumably have to present the findings. So if the outcome variables are not too many and/or they are not too correlated, I believe they can be analysed "one by one". Awaiting judgement from the sci.stat.* community and wishing you all the best with the research, Gaj Vidmar = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: 120 subjects on 120 occassion: a model ?
"Gaj Vidmar" <[EMAIL PROTECTED]> writes: > there seems to be no word from professional statisticians yet, so here's an > addenum. This message was posted in many places, so presumably we will get a summary of responses if we care? My own suggestion (mangled by a bad emailer) was to use vector time series methods, but this could lead to a fairly large computation probelm without extra information. I wasn't able to recommend a very good specialist reference off the top of my head, though. MJR = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
RE: 120 subjects on 120 occassion: a model ?
I don't know enough about time series really to provide much advice. However, I have seen methods by which a slope was calculated across time for each subject with the first measurement as the incercept (within subjects). Subsequently, the individual slope was regressed on other factors. Thus, answering the question what factors (X) influence the rate of change/direction across time in Y. -Original Message- From: MJ Ray [mailto:[EMAIL PROTECTED]] Sent: Friday, October 13, 2000 8:15 AM To: [EMAIL PROTECTED] Subject: Re: 120 subjects on 120 occassion: a model ? "Gaj Vidmar" <[EMAIL PROTECTED]> writes: > there seems to be no word from professional statisticians yet, so here's an > addenum. This message was posted in many places, so presumably we will get a summary of responses if we care? My own suggestion (mangled by a bad emailer) was to use vector time series methods, but this could lead to a fairly large computation probelm without extra information. I wasn't able to recommend a very good specialist reference off the top of my head, though. MJR = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
RE: 120 subjects on 120 occassion: a model ?
Hans-Christian Waldmann writes: >Now, what am I supposed to do with data from a design giving a T=120 >time series for _each_ of 120 subjects ? There has been a controlled >study where patients in three independent groups were asked to keep >a diary on some outcome variables for ca. 4 months. There are some >design variables like treat/control or sex and age that are expected >to contribute systematically to variation between outcome measures. >But this outcome measure apparently is a time series. I don't think >I should perform an ANOVA-style analysis with a 120-level time factor. >Pooling data and performing ARIMA/transfer-functions on a single time >series of subjects' means for each point in time doesn't make sense >either, assuming that subjects differ in both measurement level and >covariance structure of their individual time series. I admit that >I have no idea how to evaluate, say, an effect of treatment on this >kind of outcome measure. Even though the researchers collected data on 120 consecutive days, I doubt that they are particularly interested in any one day in isolation. Look at some composite measures, such as the slope of the trend line, or the change score at the end of each month. Or perhaps an average for each month, or the standard deviation for each month. Your researchers should be able to elaborate on why they collected the data, and that elaboration should help you decide which composite measure you should use. Once you reduce it to a small number of composite measures, then you can apply the ANOVA types of procedures. An alternative that might be worth exploring is fitting a spline model to each subject's data and then pooling the splines across groups. This is messy and complex, but fun. I hope this helps. Good luck! Steve Simon, [EMAIL PROTECTED], Standard Disclaimer. STATS: STeve's Attempt to Teach Statistics. http://www.cmh.edu/stats = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: 120 subjects on 120 occassion: a model ?
In sci.stat.consult Dr. Hans-Christian Waldmann <[EMAIL PROTECTED]> wrote: : Hello everybody, you havn't really given enough information but here is a suggestion. you have three separate groups. If they are not the treatment groups with random assignment, anything else you do will be VERY dubious. You could use sex as a blocking factor and age as a covariate. What is the purpose of the 120 observations? You could construct a SMALL number of relevant variables from these observations and do a MANOVA, for example linear, quadratic and cubic trends if you are simply interested in what happens over time. You might also do a between groups analysis on the final time or average time. It's hard to say without knowing the details. What your REALLY should do is consult a statistician about the specifics. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: 120 subjects on 120 occassion: a model ?
- also posted to sci.stat.consult, where the same question showed up. Steve tells how to make the best of the data, making the likely assumptions about the 120 days - On 13 Oct 2000 10:38:51 -0700, [EMAIL PROTECTED] (Simon, Steve, PhD) wrote: > Hans-Christian Waldmann writes: > > >Now, what am I supposed to do with data from a design giving a T=120 > >time series for _each_ of 120 subjects ? There has been a controlled > >study where patients in three independent groups were asked to keep > >a diary on some outcome variables for ca. 4 months. There are some > >design variables like treat/control or sex and age that are expected > >to contribute systematically to variation between outcome measures. > >But this outcome measure apparently is a time series. I don't think > >I should perform an ANOVA-style analysis with a 120-level time factor. > >Pooling data and performing ARIMA/transfer-functions on a single time > >series of subjects' means for each point in time doesn't make sense > >either, assuming that subjects differ in both measurement level and > >covariance structure of their individual time series. I admit that > >I have no idea how to evaluate, say, an effect of treatment on this > >kind of outcome measure. > > Even though the researchers collected data on 120 consecutive days, I doubt > that they are particularly interested in any one day in isolation. Look at > some composite measures, such as the slope of the trend line, or the change > score at the end of each month. Or perhaps an average for each month, or the > standard deviation for each month. > > Your researchers should be able to elaborate on why they collected the data, > and that elaboration should help you decide which composite measure you > should use. > > Once you reduce it to a small number of composite measures, then you can > apply the ANOVA types of procedures. - Here are a couple of less-likely possibilities. You don't say where the 120 days exist, so it might be that the are paycheck cycles of 7, 14, 28 days, or a month; or menstrual cycles, or some other. If the subjects have some overlapping '120 days' on the calendar, it might be reasonable to look at calendar-date for cycles, or for extreme events. That's assuming, there is a bit of day-to-day lability that might cover up some information. But if you aren't looking at (say) muggings on the day after Social Security Checks appear, then I doubt that cycles are likely. Still, the detail does allow you to exam on-set variations, or off-set -- That is, there might be a definite curve over the first week or so that does not exist later, if the ratings are something that entail learning or adaption. - This could be something interesting if it varies among the three groups, or it could be something to be eradicated because it is artifact. On the other hand, if the 120-days was known to be a limit, there might be some 'anticipation of the end' -- for instance, patients in hospitals may show remarkable recovery during the last week of the insurance coverage. So, you can probably lump data by weeks or months, but don't forget to take a look at start- and end-effects. If the measures have those hazards. -- 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/ =
