“…If not the results will not mirror the data generating process. To the extent that the constraint, represented by the vector c times the vector of population effect coefficients is not zero (c β = 0), then the estimates of the effect coefficients are biased (see Kupper et al 1983 for an explication of the bias associated with violating this assumption).…”
Section: Discussionmentioning
confidence: 99%
“…Specifically, v is the null vector (the vector of coefficients that when multiplied times the columns of the X-matrix results in the zero vector). To the extent that this assumption is not true, the estimates associated with this constraint will be biased (again Kupper et al 1983 explicate the degree of bias associated with violating this assumption). The generalized inverse that is associated with this the constraint used by Yang et al (2008) is the Moore-Penrose generalized inverse (Mazumdar et al 1980).…”
Section: Second Fundamental Problem: Model Identificationmentioning
confidence: 98%
“…The assumption is that c T β = 0, where c is the m × 1 vector for the constraint and β is the m × 1 vector of population effect coefficients. To the extent that c T β = 0 is not true, the estimates will be biased (see Kupper et al 1983 for an explication of the bias associated with violating this assumption).…”
Section: Second Fundamental Problem: Model Identificationmentioning
“…If not the results will not mirror the data generating process. To the extent that the constraint, represented by the vector c times the vector of population effect coefficients is not zero (c β = 0), then the estimates of the effect coefficients are biased (see Kupper et al 1983 for an explication of the bias associated with violating this assumption).…”
Section: Discussionmentioning
confidence: 99%
“…Specifically, v is the null vector (the vector of coefficients that when multiplied times the columns of the X-matrix results in the zero vector). To the extent that this assumption is not true, the estimates associated with this constraint will be biased (again Kupper et al 1983 explicate the degree of bias associated with violating this assumption). The generalized inverse that is associated with this the constraint used by Yang et al (2008) is the Moore-Penrose generalized inverse (Mazumdar et al 1980).…”
Section: Second Fundamental Problem: Model Identificationmentioning
confidence: 98%
“…The assumption is that c T β = 0, where c is the m × 1 vector for the constraint and β is the m × 1 vector of population effect coefficients. To the extent that c T β = 0 is not true, the estimates will be biased (see Kupper et al 1983 for an explication of the bias associated with violating this assumption).…”
Section: Second Fundamental Problem: Model Identificationmentioning
“…The methodological challenge in the estimation of the individual effects of age, period, and cohort in APC models is that multiple estimators yield the same fitted values, thus true effects are difficult to estimate simultaneously [27]. Any analysis within the framework of CGLM requires an additional assumption about those effects.…”
The effect of population aging on FNF incidence is predictable because projections are based on an existing population. The increasing health and improvement in measures of physical status of older people through the last century, explain the decline in FNF incidence in later cohorts. The steady increase in period effect may be due to increased survival of the very frail. This burden of illness resulting from FNFs must be addressed by population based, research proven approaches to fall and fracture prevention.
“…Because the interactions saturate the model that only includes main effects due to age and period, you might think of the cohort effect as a particular type of age-period interaction (25). Similarly , you could describe the period effect as a particular type of age-cohort interaction.…”
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