Population Marginal Means in the Linear Model: An Alternative to Least Squares Means

Searle, S. R.; Speed, F. M.; Milliken, George A.

doi:10.1080/00031305.1980.10483031

Cited by 1,206 publications

(596 citation statements)

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“…All other independent variables were retained in the model if they were significant at or below the 10% level. Mean QoL change was estimated for each treatment group from the regression parameter estimates using the Least Squares Means (LSM) method (Searle et al, 1980). T statistics were then generated to test for differences in mean change scores between the treatment groups.…”

Section: Methodsmentioning

confidence: 99%

Multivariate regression analyses of data from a randomised, double-blind, placebo-controlled study confirm quality of life benefit of epoetin alfa in patients receiving non-platinum chemotherapy

Fallowfield

Gagnon

Zagari³

et al. 2002

Br J Cancer

112

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Cancer-related anaemia is associated with a wide spectrum of symptoms that can negatively affect quality of life. Because epoetin alfa has demonstrated efficacy in correcting cancer-related anaemia, the impact of this treatment on quality of life was evaluated in a multinational, randomised, double-blind, placebo-controlled trial in 375 anaemic cancer patients receiving nonplatinum-based chemotherapy. The cancer-specific measures of quality of life included the general scale (FACT-G Total) and fatigue subscale (FACT-An Fatigue subscale) of the Functional Assessment of Cancer Therapy-Anaemia and the Cancer Linear Analogue Scales measuring energy, ability to do daily activities, and overall quality of life. These measures were also used to examine the relationship between haemoglobin levels and quality of life. Both univariate and multiple linear regression analyses of quality of life data were performed. Results of the univariate analysis have been reported previously. The a priori-planned multiple linear regression analysis, which accounted for the effects of disease progression and several other possibly confounding variables on quality of life, showed a significant advantage for epoetin alfa over placebo for the five scales (all, P50.05), and confirmed the results of the univariate analysis. For cancer-specific measures, significant correlations were demonstrated between baseline haemoglobin and quality of life (r, range: 0.14 -0.26, all P50.05) and between change in haemoglobin and change in quality of life (r, range: 0.26 -0.34, all P50.01). These findings provide evidence that increasing haemoglobin levels by epoetin alfa administration can significantly improve cancer patients' quality of life.

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Section: Methodsmentioning

confidence: 99%

Multivariate regression analyses of data from a randomised, double-blind, placebo-controlled study confirm quality of life benefit of epoetin alfa in patients receiving non-platinum chemotherapy

Fallowfield

Gagnon

Zagari³

et al. 2002

Br J Cancer

112

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“…Having defined appropriate partially adjusted means for each breed-management group, we can define appropriate main effect means (termed population marginal means by Searle, Speed and Milliken, 1980) for the breeds averaged over the management groups and for the management groups averaged over the breeds. The partially adjusted main effect mean for breed i is defined to be…”

Section: Partially Adjusted Main Effect Meansmentioning

confidence: 99%

Covariance Adjustment in Studies Involving Observational Factors or Covariates Influenced by Treatments

Allen¹,

Mandell²,

Wilton³

et al. 1997

Conference on Applied Statistics in Agriculture

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We extend the definition of adjusted treatment means in the analysis of covariance to deal with the case where some of the covariates are influenced by treatments or where some of the factors are observational. In these cases, comparison of treatment means adjusted to a common value of the covariate may be inappropriate. Partially adjusted means are defined and it is shown that special cases include the usual adjusted means (adjusted to a common value for each of the covariates) and unadjusted means. In fact, in a multifactorial experiment, one can, by appropriate choice of adjustment, compare adjusted means for one factor but unadjusted means for the second factor. Partially adjusted means can be computed by any linear models software which will estimate linear combinations of the parameters.

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“…We first calculated the weighted mean slope for each physiographic area, weighting backtransformed route trends by the marginal mean count on the route (Searle et al 1980;Component 6), and a measure of the reliability of the route trend estimate, calculated as the variance of the slope divided by the mean square error for the route-specific regression (Component 7). The marginal mean is an estimate of the count that would have been recorded in the mid-year of the analysis period (i.e., 1980) by an average observer.…”

Section: Thomas and Martin Bbs Analysis Methodsmentioning

confidence: 99%

“…The weightings used were the marginal mean count on the route (Searle et al 1980; Component 6), a precision estimate given by the squared deviations of the counts from the subroute mean (Component 7), and an area weighting (Component 8). We constrained the marginal mean so that it could not exceed the maximum number of birds seen on all routes, or 200, whichever was the least.…”

Section: Cws Route Regressionmentioning

confidence: 99%

The Importance of Analysis Method for Breeding Bird Survey Population Trend Estimates

Thomas

Martin

1996

Conservation Biology

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Population trends from the Breeding Bird Survey are widely used to focus conservation effort toward species thought to be in decline, and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus as to which is the most reliable. In this paper we quantified differences in trend estimates when different analysis methods were applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8 and 29 respectively). In addition, there were many differences among methods in the trend estimates assigned to individual species.Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than Canadian Wildlife Service estimates when the respective agencies data selection criteria were applied separately. These results imply that our ability to detect population declines, and to prioritize species of conservation concern are strongly dependent upon the analysis method used. This highlights the need for further research to determine how best to extract accurate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data. THOMAS AND MARTIN BBS ANALYSIS METHODS 3

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Population Marginal Means in the Linear Model: An Alternative to Least Squares Means

Cited by 1,206 publications

References 1 publication

Multivariate regression analyses of data from a randomised, double-blind, placebo-controlled study confirm quality of life benefit of epoetin alfa in patients receiving non-platinum chemotherapy

Multivariate regression analyses of data from a randomised, double-blind, placebo-controlled study confirm quality of life benefit of epoetin alfa in patients receiving non-platinum chemotherapy

Covariance Adjustment in Studies Involving Observational Factors or Covariates Influenced by Treatments

The Importance of Analysis Method for Breeding Bird Survey Population Trend Estimates

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