Modeling random effects

In all previous dissolution models described in Sections 5.1 and 5.2, the variability of the particles (or media) is not directly taken into account. In all cases, a unique constant (cf. Sections 5.1, 5.1.1, and 5.1.2) or a certain type of time dependency in the dissolution rate constant (cf. Sections 5.1.3, 5.2.1, and 5.2.2) is determined at the commencement of the process and fixed throughout the entire course of dissolution. Thus, in essence, all these models are deterministic. However, one can also assume that the above variation in time of the rate or the rate coefficient can take place randomly due to unspecified fluctuations in the heterogeneous properties of drug particles or the structure/function of the dissolution medium. Lansky and Weiss have proposed [130] such a model assuming that the rate of dissolution k (t) is stochastic and is described by the following equation  

The stochastic nature of k (t) allows the description of the fraction of dose dissolved, p (t), in the form of a stochastic differential equation if coupled with the simplest dissolution model described by (5.16), assuming complete dissolution 9 = 1)  

A discretized version of (5.26) can be used to perform Monte Carlo simulations using different values of a and generate p (t)-time profiles [130]. The random fluctuation of these profiles becomes larger as the value of a increases. 

Stochastic variation may be introduced in other models as well. In this context, Lansky and Weiss [130] have also considered random variation for the parameter k of the interfacial barrier model (5.20). 

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Lag periods Fixed-effect model Random-effect model Hausman test... 

Lee Y, Nelder JA (2001) Hierarchical generahsed linear models A synthesis of generalised linear models, random-effect models and structured dispersions. Biometrika 88 987-1006. 

Five forces model of competition, 33 Fixed asset management (ERP), 336 Fixed-effect models, random-effect vs., 2229-2230... 

In discussion of meta-analysis, there is often much attention given to the random effects model versus the fixed effects model. Random effects models assume that the true treatment effects of the individual trials represent a random sample from some population. The random effects model estimates the population mean of the treatment effects and accounts for the variation in the observed effects. It is sometimes stated that the fixed effects model assumes that the individual trial effects are constant. However, this is not a necessary assumption. An alternative view is that the fixed effects model estimates the mean of the true treatment effects of the trials in the meta-analysis. Senn (2000) discussed the analogy with center effects in multicenter trials. In safety, random effects models may be problematic because of the need to estimate between-trial effects with sparse data. Additionally, the random effects model is less statistically powerful than the fixed effects model, albeit the hypotheses are different. In the fixed effects model, the variance estimate should account for trial effect differences either through stratification, conditioning, or modeling of fixed effects. 

Fixed effect model Random effects model Heterogeneity /-squared = 0%, tau-squared = 0,/ = 0.8727... 

Langevin dynamics simulates the effect of molecular collisions and the resulting dissipation of energy that occur in real solvents, without explicitly including solvent molecules. This is accomplished by adding a random force (to model the effect of collisions) and a frictional force (to model dissipative losses) to each atom at each time step. Mathematically, this is expressed by the Langevin equation of motion (compare to Equation (22) in the previous chapter) ... 

P. Lansky, M. Weiss. Modeling heterogeneity of particles and random effects in drug dissolution. Pharm. 

Hoffmann, D., Kringle, R. Two-sided tolerance intervals for balanced and unbalanced random effects models. J. Biopharm. Stat., 15, 2005, 283-293. 

We usually seek to distinguish between two possibilities (a) the null hypothesis—a conjecture that the observed set of results arises simply from the random effects of uncontrolled variables and (b) the alternative hypothesis (or research hypothesis)—a trial idea about how certain factors determine the outcome of an experiment. We often begin by considering theoretical arguments that can help us decide how two rival models yield nonisomorphic (i.e., characteristically different) features that may be observable under a certain set of imposed experimental conditions. In the latter case, the null hypothesis is that the observed differences are again haphazard outcomes of random behavior, and the alternative hypothesis is that the nonisomorphic feature(s) is (are) useful in discriminating between the two models. 

Data used to describe variation are ideally representative of some population of risk assessment interest. Representativeness was a focus of an earlier workshop on selection of distributions (USEPA 1998). The role of problem formulation is emphasized. In case of representativeness issues, some adjustment of the data may be possible, perhaps based on a mechanistic or statistical model. Statistical random-effects models may be useful in situations where the model includes distributions among as well as within populations. However, simple approaches may be adequate, depending on the assessment tier, such as an attempt to characterize quantitatively the consequences of assuming the data to be representative. 

With regard to relevant statistical methodologies, it is possible to dehne 2 situations, which can be termed a meta-analysis context and a shrinkage estimation context. Similar statistical models, in particular random-effects models, may be applicable in both situations. However, the results of such a model will be used somewhat differently. 

Methods of statistical meta-analysis may be useful for combining information across studies. There are 2 principal varieties of meta-analytic estimation (Normand 1995). In a hxed-effects analysis the observed variation among estimates is attributable to the statistical error associated with the individual estimates. An important step is to compute a weighted average of unbiased estimates, where the weight for an estimate is computed by means of its standard error estimate. In a random-effects analysis one allows for additional variation, beyond statistical error, making use of a htted random-effects model. 

The Hausman test was used to test the null hypothesis that the coefficients estimated by the efficient random-effect model are the same as the ones estimated by the consistent fixed-effect model. If this null hypothesis cannot be rejected (insignificant P-value in general, it is larger than 0.05), then the random-effect model is more appropriate. 

There is a growing literature that addresses the transferability of a study s pooled results to subgroups. Approaches include evaluation of the homogeneity of different centers and countries results use of random effects models to borrow information from the pooled results when deriving center-specific or country-specific estimates direct statistical inference by use of net monetary benefit regression and use of decision analysis. 

The fixed effects model considers the studies that have been combined as the totality of all the studies conducted. An alternative approach considers the collection of studies included in the meta-analysis as a random selection of the studies that have been conducted or a random selection of those that could have been conducted. This results in a slightly changed methodology, termed the random effects model The mathematics for the two models is a little different and the reader is referred to Fleiss (1993), for example, for further details. The net effect, however, of using a random effects model is to produce a slightly more conservative analysis with wider confidence intervals. 

In a Model II ANOVA (random effect model) the result can be decomposed as yij=iu+Aj+eij, where Aj represents a normally distributed variable with mean zero and variance a]j. In this model one is not interested in a specific effect due to a certain level of the factor, but in the general effect of all levels on the variance. That effect is considered to be normally distributed. Since the effects are random it is of no interest to estimate the magnitude of these random effects for any one group, or the differences from group to group. What can be done is to estimate their contribution [Pg.141]

The subordinate level of a nested ANOVA is always Model II (random effect model). The highest level of classification of a nested ANOVA may be Model I (fixed effect model) or Model II. If it is Model II it is called a pure Model II nested ANOVA. If the highest level is Model I it is called a mixed model nested ANOVA. 

Perform randomized experiments according to statistical model Calculate effects of each factor and test for statistical significance... 

The algorithm used is attributed to J. B. J. Read. For many manipulations on large matrices it is only practical for use with a fairly large computer. The data are arranged in two matrices by sample i and nuclide j one matrix, V, contains the amount of each nuclide in each sample the other matrix, E, contains the variances of these numbers, as estimated from counting statistics, agreement between replicate analyses, and known analytical errors. It is also possible to add an arbitrary term Fik to each variance to account for random effects between samples not considered in the model this is usually done in terms of an additional fractional error. Zeroes are inserted for missing data in cases in which not all nuclides were measured in every sample. 

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