Hi Jon,
You are describing a problem that bothered me for many years until I researched them. It turns out that there is a much deeper problem here than what SAS can or cannot do.
Here's the issue: The standard analysis that you describe assumes that the repeated measurements are taken within subjects and that subjects are independent of one another. That is, while there may be within-subject correlation between measurements, there is no between-subject correlation. In a medical trial, the patients are not all seen at the same time, so that the same relative time within different patients (Week 1, Week 2, etc.) corresponds to different calendar time. Thus, their repeated measurements are surely not influenced by others' measurements nor by some common factor affecting all of their measurements at a given time.
However, when you take repeated measurements across years in a field trial, this assumption is almost certainly violated. The common annual environments under which the repeated measurements are taken impart a common random effect on all measurements taken in the same year. If it's a wet year, a dry year, a hot year, or whatever, ALL measurements taken in that year are similarly affected. Therefore, the differences you actually see from year to year are a completely confounded combination of the fixed effects you want to find and the random effects that are a nuisance.
What's worse is the possibility that different treatments perform differently under the different environmental conditions. If some of the treatments do better than others in wet years but worse than others in hot years, then you have a random Year*TRT interaction, which again is completely confounded with the fixed effects you want to identify. There is literally no way to separate the fixed and random effects without adding further assumptions to the analysis, such as a model for the fixed-effect pattern.
Please see the two papers:
Loughin, T.M. (2006). Improved Experimental Design and Analysis for Long-Term Experiments.
Crop Science,
46, 2492-2506.
Figure 1 from this paper depicts the problem clearly (to my way of thinking, at least!). The random Year effect is a stripped factor running orthogonal to all of the other experimental units in the design, which interferes with the fixed-effect analysis. The paper gives suggestions for the analysis of these experiments by including extra assumptions. There is also SAS code. More importantly, the paper describes how to properly design such experiments so that the confounding of random and fixed Year effects is no longer an issue: The Staggered-Start design: begin different blocks in different years, so that the relative time and the calendar time are not confounded.
Loughin, T.M.; Roediger, M.P.; Milliken, G.A.; and Schmidt, J.P. (2007). On the analysis of long-term experiments.
JRSS-A,
170, 24-42.
This paper gives the technical details on the problem and some simulations under very realistic conditions (defined as levels of variance components that have been measured in previously published experiments), showing just how wrong the answers can be if one ignores the issue and "blasts away" with the standard within-subjects repeated measures analysis.
I hope this helps. Good luck.
-Tom.
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Thomas Loughin
Simon Fraser University
www.stat.sfu.ca/~tloughin/STATPAGE.html -------------------------------------------