A General Statistical Framework for Adjustment of Rates

A general framework is presented that integrates standardization procedures common in demography, biometrics, and other areas with statistical methodology for the analysis of log-linear models. A family of rate-adjustment methods is derived from the log-linear model; the conventional method of direct standardization is a special case. Extensions of earlier methods include (a) adjustment for three-factor interaction, (b) adjustment for marginal association between composition and group, (c) adjustments that use a standard group, and (d) adjustments that control for both marginal composition-group interaction and three-factor interaction. Statistical inference for adjusted rates is facilitated in several ways: (a) by presenting key hypotheses that can be tested routinely with log-linear methods, (b) by efficient point and interval estimation of rates, (c) by assessing the sampling variability of absolute or relative comparisons of rates across groups, and (d) by smoothing the data. Examples illustrate the flexibility of the proposed framework.

[1]  L. Santi Partialling and Purging: , 1989 .

[2]  Morris Rosenberg,et al.  Test Factor Standardization as a Method of Interpretation , 1962 .

[3]  D. Discher,et al.  Screening for chronic pulmonary disease: survey of 10,000 industrial workers. , 1969, American journal of public health and the nation's health.

[4]  C. Clogg,et al.  On Regression Standardization for Moments , 1986 .

[5]  D. Pregibon Logistic Regression Diagnostics , 1981 .

[6]  Test-Factor Standardization and Marginal Standardization , 1977 .

[7]  Rupert G. Miller The jackknife-a review , 1974 .

[8]  C. Clogg,et al.  A flexible procedure for adjusting rates and proportions including statistical methods for group comparisons. , 1988 .

[9]  M. Spiegelman,et al.  I. Empirical testing of standards for the age adjustment of death rates by the direct method. , 1966, Human biology.

[10]  Roderick J. A. Little,et al.  The General Linear Model and Direct Standardization , 1979 .

[11]  E. Kitagawa,et al.  Components of a Difference Between Two Rates , 1955 .

[12]  K. Keppel Mortality differentials by size of place and sex in Pennsylvania for 1960 and 1970. , 1981, Social biology.

[13]  Leo A. Goodman,et al.  How to Ransack Social Mobility Tables and Other Kinds of Cross-Classification Tables , 1969, American Journal of Sociology.

[14]  John J. Gart,et al.  The effect of bias, variance estimation, skewness and kurtosis of the empirical logit on weighted least squares analyses , 1985 .

[15]  Robert L. Kaufman,et al.  USING ADJUSTED CROSSTABULATIONS TO INTERPRET LOG-LINEAR RELATIONSHIPS* , 1986 .

[16]  Jan M. Hoem,et al.  Statistical analysis of a multiplicative model and its application to the standardization of vital rates , 1987 .

[17]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[18]  K. Koehler Goodness-of-fit tests for log-linear models in sparse contingency tables , 1986 .

[19]  Robert E. Fay,et al.  A Jackknifed Chi-Squared Test for Complex Samples , 1985 .

[20]  Neil Henry Jackknifing Measures of Association , 1981 .

[21]  N. Keyfitz Sampling variance of demographic characteristics. , 1966, Human biology.

[22]  N Keiding,et al.  The method of expected number of deaths, 1786-1886-1986. , 1987, International statistical review = Revue internationale de statistique.