(1) The generalized linear interactive modeling (GLIM) technique used at TRRL has stricter assumptions, but is better suited for model testing.
(2) In this analysis, poisson regression models were fit using the statistical program GLIM (Generalized Linear Models) to determine the separate effects of age, period of death, and birth cohort on the specific cancers of interest (lung, liver, bladder, CNS, leukemia, lympho-reticulosarcoma, all cancers).
(3) This paper presents a simple Statistical Analysis System (SAS) program for the generation of such data in a form that can be read directly by GLIM and used in a Poisson regression analysis.
(4) The models were fitted by the GLIM system assuming a Poisson error function.
(5) The necessary calculations are easily performed in the statistical computer program package GLIM, and the necessary commands are noted.
(6) The spreading behavior of adherent platelets was analyzed using generalized linear interactive modeling (GLIM).
(7) Analysis of screen-detected and interval cancer data by means of statistical modelling using GLIM allowed us to estimate the mean sojourn time (1.91 and 3.97 years in 40-49 and 50-69 year old women respectively), sensitivity (about 90% for those aged 50-69) and predictive value at the prevalence screening test (about 100%).
(8) To this end, loglineal (Poisson regression) models were constructed of the IC mortality rates for both sexes, using the GLIM package, in which the regression coefficients are the natural Relative Risk (RR) logarithms of the various age groups (35-74 years), period of death (1970-1985) and birth cohort (1985-1960) with respect to the reference group mortality, controlled by the effect of other groups.
(9) Variable results on a particular bioassay exhibited by extracts from different colonies of a given species were analysed by the Generalized Linear Interactive Modelling system (GLIM).
(10) In particular, it is argued that classical methods as implemented in the computer package GLIM can be used as approximations to Bayesian methods, particularly at the initial stage of model selection.
(11) The bootstrap can be used in many statistical packages such as MINITAB, SPSS, SAS, BMDP, or GLIM, all of which are widely available, and could be useful in other areas of the pharmaceutical sciences where regression analysis is employed.
(12) In this report we present an accurate statistical approach using the Generalized Linear Interactive Modeling (GLIM) computer package developed by the Numerical Algorithms Group, Oxford, U.K.
(13) A piecewise exponential model is used to model relative risks of delivery associated with a previous spontaneous abortion and a model incorporating digit preference is fitted as a generalized bilinear model in GLIM.
(14) A log linear model was fitted using GLIM, assuming Poisson errors in incidence; linear and non-linear regression techniques were also used.
(15) Data on cycles to pregnancy can be used to estimate the beta parameters by maximum likelihood in a straightforward manner with a package such as GLIM.
(16) In this form, convergence to the least-squares estimates using the Gauss-Newton method [see Kennedy & Gentle (1980) Statistical Computing, Marcel Dekker, New York] is virtually ensured, or, as the model in this form is a member of the class of 'generalized linear models', it may be fitted by packages such as those of Rothamsted Experimental Station [(1977) GENSTAT (A General Statistical Program), Rothamsted Experimental Station, Harpenden] and the Numerical Algorithms Group [(1978) GLIM (Generalised Linear Interactive Modeling), Numerical Algorithms Group, Oxford].
(17) As the two groups were not balanced despite randomization, multivariate methods (GLIM) were used to identify significant prognostic factors.
(18) A generalized log linear model was fitted to the data using the statistical package GLIM, confirming a significant trend for labelled cells to occupy higher sites in the oxyntic gland as the time since labelling of cells increased.
(19) A method, using the GLIM computer package, for determining primary and secondary HLA associations with disease is described and is applied to data from patients with psoriasis and psoriatic arthritis.
(20) The statistical significance of temporal, age, sex, and seasonal variations in incidence rates was ascertained by Poisson regression models (GLIM statistical software).