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Incomplete observations, common in epidemiology as in many other fields, lead to problems of bias, precision and power. Using a simple example with 3 binary variables, we discuss situations where the observed odds ratio is biased. We present and compare the main strategies of analysis: complete observations modeling, missing data indicator, weighted analysis, simple imputation, multiple imputation, selection models, shared variable models.
The NIH 3T3 line of mouse fibroblasts undergoes "spontaneous" transformation in culture, exhibited in the development of foci of transformed cells overgrowing the confluent monolayer. Evidence is provided here to support the proposition that the spontaneous generation of individual transformed variants is a product of epigenetic induction by various types of growth inhibition. Novel transformed variants generally arise after prolonged confluence and cessation of net growth, with these new types of foci appearing during a second round of confluence, although not in the first round. Few or no transformed variants exist in the cultures prior to growth constraint. The susceptibility of NIH 3T3 cells to induction of transformation is itself shown to be subject to epigenetic influence, with capacity for transformation reflecting passage histories. Cell sublines maintained in long-term subconfluent passage that allows unimpeded growth become more refractory with time to spontaneous transformation, while sublines passaged in more growth-restricting conditions maintain their sensitivity. The differing capacities of the sublines are reflected in the degree of growth inhibition required to induce individual events of spontaneous transformation, and in the frequency at which such new variants arise. Thus growth inhibition not only induces individual transforming events, but even increases cell susceptibility to further induction of transformation. These phenomena are consistent with the progressive state selection model of heritable change, which postulates a self-regulating selection of better adapted states from among those made available to a biological system during heterogeneous fluctuations in its total pattern of chemical equilibria.
Various models have been proposed by which to understand the phenomenon of sexual coercion within dating relationships. Two are reviewed, including the limitations of each. A third, the situational approach, is outlined as more comprehensive in understanding the problem. One aspect of the model, selective evaluation, is experimentally tested. One-hundred eighty-two college students participated in brief heterosexual interactions and rated each other's behavior in terms of sexual expressiveness, flattery, and interest in future interactions. Behavioral data were also collected to evaluate differences between groups and the role behavioral cues play in the expression and attribution of sexual interest. Results support the hypothesis of selective evaluation in men, and in sexually coercive men in particular. Results are discussed with regard to socialization processes, communication of sexual interest, and recommendations for future research.
Two N-person game theoretical models examining the evolution of co-operation during predator inspection in fish are presented. Predator inspection occurs in small shoals of fish, in which one to a few individuals, the "inspectors" (co-operators) break away from the shoal and cautiously approach a predator to obtain information on this potential danger. In the models presented here, remaining with the shoal and not inspecting is considered an act of defection. Both model I and II produce a stable internal polymorphism of inspectors and noninspectors. While the equilibrial frequency of inspectors can be low (i.e. less than 10%) at large shoal size, the proportion of shoals containing any inspectors--and therefore exhibiting the inspection behavior--is much greater. Both models presented here, and N-person games in general are equivalent to intrademic group selection models of evolution in structured populations, in which shoals are trait groups and co-operation evolves by between-shoal selection. While the results are cast in terms of predator inspection, the model itself is general and applies to any multi-group scenario where co-operators benefit entire groups at their own expense. The results presented here add to the mounting theoretical and empirical evidence that co-operation is frequently not a pure evolutionarily stable strategy, and that many metapopulations should be polymorphic for both co-operators and defectors.
We aimed to determine the most appropriate candidates for liver transplantation based on their survival outcomes. Two hundred and fourteen patients who were transplanted in the presence of hepatocellular carcinoma (HCC) were analyzed. Patient groups were selected as "good risk" candidates for transplantation by our previously developed artificial network model or by the classic pTNM pathological classification system. The survival of the model-selected candidate groups was then compared to the survival of the candidates chosen as "good risk" by the pTNM classification (i.e. , pTNM stages I + II and pTNM stages I + II + III). Suitability for transplantation was judged by long-term survival rates (i.e., 1-10 years post-transplant). By using the neural network prediction model and the subsequent subgroup case analysis, it was possible to generate those combinations of risk factors which predetermined patient survival through HCC recurrence. By applying the developed neural network model to the transplant candidate pool for patients with HCC, it was possible to select the maximum number of suitable candidates for transplantation while minimizing donor organ loss to recurrent HCC.
In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.
We consider population genetics models where selection acts at a set of unlinked loci. It is known that if the fitness of an individual is multiplicative across loci, then these loci are independent. We consider general selection models, but assume parent-independent mutation at each locus. For such a model, the joint stationary distribution of allele frequencies is proportional to the stationary distribution under neutrality multiplied by a known function of the mean fitness of the population. We further show how knowledge of this stationary distribution enables direct simulation of the genealogy of a sample at a single-locus. For a specific selection model appropriate for complex disease genes, we use simulation to determine what features of the genealogy differ between our general selection model and a multiplicative model.
Two different deterministic and two alternative stochastic (i.e., geostatistical) approaches to modeling the distribution of hydraulic conductivity (K) in a nonuniform (sigma2ln(K)) = 0.29) glacial sand aquifer were used to explore the influence of conceptual model selection on simulations of three-dimensional tracer movement. The deterministic K models employed included a homogeneous effective K and a perfectly stratified 14 layer model. Stochastic K models were constructed using sequential Gaussian simulation and sequential i ndicator simulation conditioned to available K values estimated from measured grain size distributions. Standard simulation software packages MODFLOW, MT3DMS, and MODPATH were used to model three-dimensional ground water flow and transport in a field tracer test, where a pulse of bromide was injected through an array of three fully screened wells and extracted through a single fully screened well approximately 8 m away. Agreement between observed and simulated transport behavior was assessed through direct comparison of breakthrough curves (BTCs) and selected breakthrough metrics at the extraction well and at 26 individual multilevel sample ports distributed irregularly between the injection and extraction wells. Results indicate that conceptual models incorporating formation variability are better able to capture observed breakthrough behavior. Root mean square (RMS) error of the deterministic models bracketed the ensemble mean RMS error of stochastic models for simulated concentration vs. time series, but not for individual BTC characteristic metrics. The spatial variability models evaluated here may be better suited to simulating breakthrough behavior measured in wells screened over large intervals than at arbitrarily distributed observation points within a nonuniform aquifer domain.
For the fifth round of Critical Assessment of Techniques for Protein Structure Prediction (CASP5) all comparative modeling (CM) and fold recognition (FR) target proteins were modeled using a combination of consensus alignment strategy and 3D assessment. A large number and broad variety of prediction targets, with sequence identity between each modeled domain and the related known structure, ranging from 6 to 49%, represented all difficulty levels in comparative modeling and fold recognition. The critical steps in modeling, selection of template(s) and generation of sequence-to-structure alignment, were based on the results of secondary structure prediction and tertiary fold recognition carried out using the Meta Server coupled with the 3D-Jury system. The main idea behind the modeling procedure was to select the most common alignment variants provided by individual servers, as well as to generate several alternatives for questionable regions and to evaluate them in 3D by building corresponding molecular models. Analysis of fold-specific features and sequence conservation patterns for the target family was also widely used at this stage. For both CM and FR targets remote homologs of known structure were clearly recognized by the 3D-Jury system. In the analogous fold recognition subcategory, the correct fold was identified for five out of eight domains. The average alignment accuracy for FR models (48%) was far less than for CM predictions (80%). These finding, coupled with the observation that in the majority of cases the submitted models were not closer to the experimental structure than their best templates, indicate that, especially for difficult targets, there is still ample room for improvement.
In many ways, adjustment for publication bias in meta-analysis parallels adjustment for ascertainment bias in genetic studies. We investigate a previously published simulation-based method for dealing with complex ascertainment bias and show that it can be modified for use in meta-analysis when publication bias is suspected. The method involves simulating sets of pseudo-data under the assumed model using guesses for the unknown parameters. The pseudo-data are subjected to the same selection criteria as are believed to have operated on the original data. A conditional likelihood is then used to estimate the adjusted values of the unknown parameters. This method is used to re-analyse a published meta-analysis of the effect of the MTHFR gene on homocysteine levels. Simulation studies show that the pseudo-data method is unbiased; they give an indication of the number of pseudo-data values required and suggest that a two-stage adjustment produces less variable estimates. This method can be thought of as an example of the selection model approach to publication bias correction. As the selection mechanism must be assumed, it is important to investigate the sensitivity of any conclusions to this assumption.
Mixed treatment comparison (MTC) meta-analysis is a generalization of standard pairwise meta-analysis for A vs B trials, to data structures that include, for example, A vs B, B vs C, and A vs C trials. There are two roles for MTC: one is to strengthen inference concerning the relative efficacy of two treatments, by including both 'direct' and 'indirect' comparisons. The other is to facilitate simultaneous inference regarding all treatments, in order for example to select the best treatment. In this paper, we present a range of Bayesian hierarchical models using the Markov chain Monte Carlo software WinBUGS. These are multivariate random effects models that allow for variation in true treatment effects across trials. We consider models where the between-trials variance is homogeneous across treatment comparisons as well as heterogeneous variance models. We also compare models with fixed (unconstrained) baseline study effects with models with random baselines drawn from a common distribution. These models are applied to an illustrative data set and posterior parameter distributions are compared. We discuss model critique and model selection, illustrating the role of Bayesian deviance analysis, and node-based model criticism. The assumptions underlying the MTC models and their parameterization are also discussed.
Studies on patients and large animal models suggest the importance of atrial fibrosis in the development of atrial fibrillation (AF). To investigate whether increased fibrosis is sufficient to produce a substrate for AF, we have studied cardiac electrophysiology (EP) and inducibility of atrial arrhythmias in MHC-TGFcys33ser transgenic mice (Tx), which have increased fibrosis in the atrium but not in the ventricles. In anesthetized mice, wild-type (Wt) and Tx did not show significant differences in surface ECG parameters. With transesophageal atrial pacing, no significant differences were observed in EP parameters, except for a significant decrease in corrected sinus node recovery time in Tx mice. Burst pacing induced AF in 14 of 29 Tx mice, whereas AF was not induced in Wt littermates (P<0.01). In Langendorff perfused hearts, atrial conduction was studied using a 16-electrode array. Epicardial conduction velocity was significantly decreased in the Tx RA compared with the Wt RA. In the Tx LA, conduction velocity was not significantly different from Wt, but conduction was more heterogeneous. Action potential characteristics recorded with intracellular microelectrodes did not reveal differences between Wt and Tx mice in either atrium. Thus, in this transgenic mouse model, selective atrial fibrosis is sufficient to increase AF inducibility.
Unidirectional incompatibility selection is examined as an alternate mechanism of natural selection to cytoplasmic male sterility (CMS) for generating cytonuclear disequilibria. Differences in the dynamics and equilibrium behavior of cytonuclear disequilibria between these two cytonuclear selection models may allow for statistical tests of CMS vs. unidirectional incompatibility between mating cytotypes. Unlike CMS without migration, unidirectional incompatibility causes the cytoplasmic allele frequency to change over time rather than remain constant, and the nuclear allele frequencies hitchhike on the cytoplasmic frequencies. The decay of disequilibria is also distinctive in the absence of migration. Furthermore, in comparing both models with migration it is seen that the opportunity for internal equilibrium can be two or three times higher in a unidirectional incompatibility vs. CMS model. An example is presented that shows how unidirectional incompatibility can be statistically eliminated as a possible mechanism of cytonuclear selection.
Cyclooxygenase-2 (COX-2) is over expressed in a variety of premalignant and malignant conditions. It may contribute to carcinogenesis by modulating xenobiotic metabolism, apoptosis, immune surveillance, and angiogenesis. Selective COX-2 inhibitors suppress the formation of tumors in experimental models. Selective COX-2 inhibitors also suppress the growth and metastases of established tumors and enhance the anticancer activity of both radiotherapy and chemotherapy in experimental animals. This review aims at discussing evidence that inhibition of COX-2 represents a promising strategy to treat, prevent or possibly prevent human malignancies. Importantly, selective COX-2 inhibitors do not inhibit platelet function and cause fewer gastrointestinal side effects (peptic ulcer disease) than traditional nonsteroidal anti-inflammatory drugs (NSAIDS). More clinical trials are warranted to define the role of selective COX-2 inhibitors in the prevention and treatment of cancer along with their assessment of toxicity.
The purpose of this retrospective study was to examine in a naturalistic setting the effect of abciximab versus tirofiban on hospital length of stay for patients undergoing percutaneous coronary intervention (PCI). Retrospective data were obtained from HCIASach's Clinical Pathways Database on 5,560 PCI patients who were administered either abciximab or tirofiban. Multivariate analysis was used to control for a wide range of factors (GPIIb/IIIa selection, patient demographics, insurance provider, health conditions, admission information, and hospital characteristics) that may influence hospital length of stay. Estimation was conducted via a two-stage sample selection model. After controlling for high-risk indications and sources of selection bias, results indicate that receipt of abciximab was associated with significantly shorter lengths of hospital stays compared to tirofiban (1.01 fewer days; p < 0.001). In a subgroup analysis of patients having an acute myocardial infarction (AMI; n = 2,593), receipt of abciximab was also found to be associated with significantly shorter hospital stays compared to tirofiban (0.60 fewer days; p < 0.001). Results of this study indicate that patients who are administered abciximab versus tirofiban have significantly shorter hospital stays. This reduction in length of stay may imply potential cost offsets for PCI patients who receive abciximab.
Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis-specification, leading to incorrect inference about the activation-maps, SPM[t] and SPM[F]; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under-utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre-specified, usually over-simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin-Watson statistic (DW-map) and the coefficient of multiple determination (R(2)-map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis-specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub-optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227-238, 2003.
A numerical method for fitting linear compartmental models to data is presented which is similar to the method proposed by Jennrich and Bright (R. I. Jennrich and P. B. Bright, Technometrics 18, 385 (1976] and Feldman (H. A. Feldman. Amer. J. Physiol. 233, R1 (1977] but avoids some of the numerical difficulties. The method is direct and does not use numerical integration thereby avoiding time consuming and expensive calculations. Emphasis is on statistical procedures for model selection. In addition to the usual F tests for comparing two models, Akaike's Information Criterion (AIC) is introduced for choosing from among several models. The combined use AIC with an F test, chi-square test, or confidence intervals on estimated parameters gives a practical method to obtain a balance between underfitting and overfitting. Application to models of plasma protein kinetics illustrates the method.
We developed and analyzed the mathematical model of the intrinsic pathway based on the current biochemical data on the kinetics of blood coagulation individual stages. The model includes eight differential equations describing the spatio-temporal dynamics of activation of factors XI, IX, X, II, I, VIII, V, and protein C. The assembly of tenase and prothrombinase complexes is considered as a function of calcium concentration. The spatial dynamics of coagulation was analyzed for the one-dimensional case. We examined the formation of active factors, their spreading, and growth of the clot from the site of injury in the direction perpendicular to the vessel wall, into the blood thickness. We assumed that the site of injury (in the model one boundary of the space segment under examination) becomes a source of the continuous influx of factor XIa. In the first part, we described the model, selected the parameters, etc. In the second part, we compared the model with experimental data obtained in the homogeneous system and analyzed the spatial dynamics of the clot growth.