nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Bayes Analysis of Multilevel Health Services Data: A Canadian Neonatal Mortality Study

This article presents a comprehensive analysis of multilevel health services data in order to provide detailed information about important sources of outcome variation. We present a study of neonatal mortality variation among 17 neonatal intensive care units across Canada and briefly introduce the Neonatal Health Services in Canada Project, under which this work was carried out. We illustrate the potential of hierarchical Bayes modeling as a general strategy for systematically evaluating response-covariate associations at each level of the hierarchy, examining cross-level interaction, and attributing unexplained variability. We engage in an extensive discussion on relevant issues that are important for institutional comparison, provider profiling, and quality improvement.
On the Value of derivative evaluations and random walk suppression in Markov Chain Monte Carlo algorithms

Two strategies that can potentially improve Markov Chain Monte Carlo algorithms are to use derivative evaluations of the target density, and to suppress random walk behaviour in the chain. The use of one or both of these strategies has been investigated in a few specific applications, but neither is used routinely. We undertake a broader evaluation of these techniques, with a view to assessing their utility for routine use. In addition to comparing different algorithms, we also compare two different ways in which the algorithms can be applied to a multivariate target distribution. Specifically, the univariate version of an algorithm can be applied repeatedly to one-dimensional conditional distributions, or the multivariate version can be applied directly to the target distribution.
Estimation in Bayesian Disease Mapping

Recent work on Bayesian inference of disease mapping models discusses the advantages of the fully Bayesian (FB) approach over its empirical Bayes (EB) counterpart, suggesting that FB posterior standard deviations of small‐area relative risks are more reflective of the uncertainty associated with the relative risk estimation than counterparts based on EB inference, since the latter fail to account for the variability in the estimation of the hyperparameters. In this article, an EB bootstrap methodology for relative risk inference with accurate parametric EB confidence intervals is developed, illustrated, and contrasted with the hyperprior Bayes. We elucidate the close connection between the EB bootstrap methodology and hyperprior Bayes, present a comparison between FB inference via hybrid Markov chain Monte Carlo and EB inference via penalized quasi‐likelihood, and illustrate the ability of parametric bootstrap procedures to adjust for the undercoverage in the “naive” EB interval estimates. We discuss the important roles that FB and EB methods play in risk inference, map interpretation, and real‐life applications. The work is motivated by a recent analysis of small‐area infant mortality rates in the province of British Columbia in Canada.
Pelvic Bone Asymmetry in 323 Study Participants Receiving Abdominal CT Scans

Retrospective review of all CT scans of pelvis and abdomen performed at our institution in October and November 2000.  To determine the prevalence and extent of radiographic pelvic asymmetry in a population of patients not preselected for having low back pain.  Pelvic asymmetry refers to asymmetric positioning of landmarks on the two sides of the pelvis and may have a structural or functional etiology. Pelvic asymmetry can be associated with the presence of true leg length discrepancy, lead to false diagnosis or inaccurate measurement of leg length discrepancy, or itself be independently associated with back pain. Although the prevalence of pelvic asymmetry has been reported in patients with back pain to be 24–91%, its prevalence in the general population is not known.  A total of 323 consecutive CT scans of the pelvis/abdomen were assessed for pelvic asymmetry by one of three examiners. Pelvic asymmetry was defined as an unequal distance from the iliac crests to the acetabuli bilaterally, measured on the anteroposterior scout view of the CT scan. Measurements made on 30 randomly selected scans by the three examiners were used to assess interrater reliability of the measurement method.  Pelvic asymmetry ranged in magnitude from −11 mm to 7 mm [right pelvis (mm) − left pelvis (mm)]. Pelvic asymmetry was >5 mm in 17 of 323 (5.3%) and >10 mm in 2 of 323 (0.6%) of the subjects; 172 of 323 (53.3%) had a smaller right hemipelvis (mean asymmetry = −3.0 mm). A total of 95 of 323 (29.4%) had a smaller left hemipelvis (mean asymmetry = 2.1 mm). The intraclass correlation coefficient [ICC(2,1)] between the three observers was high (0.91).  Pelvic asymmetry of >5 mm was uncommon, with a prevalence of approximately 5% in the population studied. CT scanography was found to be a practical and reliable method for the assessment of suspected pelvic asymmetry.
A Simple Approach to Fitting Bayesian Survival Models

There has been much recent work on Bayesian approaches to survival analysis, incorporating features such as flexible baseline hazards, time-dependent covariate effects, and random effects. Some of the proposed methods are quite complicated to implement, and we argue that as good or better results can be obtained via simpler methods. In particular, the normal approximation to the log-gamma distribution yields easy and efficient computational methods in the face of simple multivariate normal priors for baseline log-hazards and time-dependent covariate effects. While the basic method applies to piecewise-constant hazards and covariate effects, it is easy to apply importance sampling to consider smoother functions.
An extension of the Dirichlet prior for the analysis of longitudinal multinomial data

Studies producing longitudinal multinomial data arise in several subject areas. This article suggests a Bayesian approach to the analysis of such data. Rather than infusing a latent model structure, we develop a prior distribution for the multinomial parameters which reflects the longitudinal nature of the observations. This distribution is constructed by modifying the prior that posits independent Dirichlet distributions for the multinomial parameters across time. Posterior analysis, which is implemented using Monte Carlo methods, can then be used to assess the temporal behaviour of the multinomial parameters underlying the observed data. We test this methodology on simulated data, opinion polling data, and data from a study concerning the development of moral reasoning.
Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments

Examples of Mismeasurement The Mismeasurement Phenomenon What is Ahead? THE IMPACT OF MISMEASURED CONTINUOUS VARIABLES The Archetypical Scenario More General Impact Multiplicative Measurement Error Multiple Mismeasured Predictors What about Variability and Small Samples? Logistic Regression Beyond Nondifferential and Unbiased Measurement Error  The Linear Model Case More General Impact Inferences on Odds-Ratios Logistic Regression Differential Misclassification Polychotomous Variables  Viral Dynamics Logistic Regression I: Smoking and Bladder Cancer Logistic Regression II: Framingham Heart Study Issues in Specifying the Exposure Model More Flexible Exposure Models Retrospective Analysis Comparison with Non-Bayesian Approaches  TOPICS Dichotomization of Mismeasured Continuous Variables Mismeasurement Bias and Model Misspecification Bias Identifiability in Mismeasurement Models Further Remarks APPENDIX: BAYES-MCMC INFERENCE Bayes Theorem Point and Interval Estimates Markov Chain Monte Carlo Prior Selection MCMC and Unobserved Structure REFERENCES
Comparing the Effects of Continuous and Discrete Covariate Mismeasurement, with Emphasis on the Dichotomization of Mismeasured Predictors

It is well known that imprecision in the measurement of predictor variables typically leads to bias in estimated regression coefficients. We compare the bias induced by measurement error in a continuous predictor with that induced by misclassification of a binary predictor in the contexts of linear and logistic regression. To make the comparison fair, we consider misclassification probabilities for a binary predictor that correspond to dichotomizing an imprecise continuous predictor in lieu of its precise counterpart. On this basis, nondifferential binary misclassification is seen to yield more bias than nondifferential continuous measurement error. However, it is known that differential misclassification results if a binary predictor is actually formed by dichotomizing a continuous predictor subject to nondifferential measurement error. When the postulated model linking the response and precise continuous predictor is correct, this differential misclassification is found to yield less bias than continuous measurement error, in contrast with nondifferential misclassification, i.e., dichotomization reduces the bias due to mismeasurement. This finding, however, is sensitive to the form of the underlying relationship between the response and the continuous predictor. In particular, we give a scenario where dichotomization involves a trade‐off between model fit and misclassification bias. We also examine how the bias depends on the choice of threshold in the dichotomization process and on the correlation between the imprecise predictor and a second precise predictor.
On the simultaneous effects of model misspecification and errors in variables

Model misspecification and noisy covariate measurements are two common sources of inference bias. There is considerable literature on the consequences of each problem in isolation. In this paper, however, the author investigates their combined effects. He shows that in the context of linear models, the large‐sample error in estimating the regression function may be partitioned in two terms quantifying the impact of these sources of bias. This decomposition reveals trade‐offs between the two biases in question in a number of scenarios. After presenting a finite‐sample version of the decomposition, the author studies the relative impacts of model misspecification, covariate imprecision, and sampling variability, with reference to the detectability of the model misspecification via diagnostic plots.
A Bayesian approach to case-control studies with errors in covariables.

We develop Bayesian methodology for the analysis of case-control data with covariate imprecision. The pretense that the distribution of the imprecisely measured covariate is discrete on a heuristically chosen support set leads to a method which is reasonably simple to implement, and can be applied to different study designs. The methodological development emphasizes the interplay between retrospective and prospective analysis. We illustrate the method on simulated data, and on data from a cancer study where smoking history is the imprecisely measured covariate.
On measuring sensitivity to parametric model misspecification

In settings where parametric inference is inconsistent under model misspecification, the discrepancy between correct and misspecified inferences is compared with the discrepancy between correct and misspecified models. To make the comparison tractable, large sample and small misspecification approximations are employed. The ratio of the approximate discrepancy between inferences to the approximate discrepancy between models is regarded as a relative measure of sensitivity to model misspecification. The maximum ratio over a family of correct distributions is determined as a measure of worst case sensitivity. As well, the distribution producing this maximum can be examined, to see how a particular combination of a parametric family and estimand is susceptible to model misspecifications.
Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities

Consider case‐control analysis with a dichotomous exposure variable that is subject to misclassification. If the classification probabilities are known, then methods are available to adjust odds‐ratio estimates in light of the misclassification. We study the realistic scenario where reasonable guesses, but not exact values, are available for the classification probabilities. If the analysis proceeds by simply treating the guesses as exact, then even small discrepancies between the guesses and the actual probabilities can seriously degrade odds‐ratio estimates. We show that this problem is mitigated by a Bayes analysis that incorporates uncertainty about the classification probabilities as prior information.
Local Robustness in Bayesian Analysis

Whereas a global approach to prior robustness focusses on the range of inferences arising from a range of priors, the local approach is concerned with derivatives of posterior quantities with respect to the prior. The local approach has several advantages. First, in contrast to the linearization algorithm used for global robustness, an iterative scheme is not required. Second, it is typically straightforward to obtain a local analysis from Markov chain Monte Carlo posterior output. Third, local sensitivity analysis can be implemented in situations where the linearization algorithm for global analysis is not applicable because the posterior quantity of interest is not a ratio-linear functional of the distribution being perturbed. On the downside, however, it is hard to assess the accuracy of a local sensitivity analysis viewed as an approximation to a global analysis. In particular, summaries of local sensitivity often have nonsensical asymptotic behaviour, raising thorny questions about their calibration. In this article the local approach is broadly defined, ranging from differentiation of functions (e.g., assessing sensitivity to hyperparameters) to differentiation of functionals (e.g., assessing sensitivity to the prior distribution as a whole). In this article we review some of the basic formulations for local assessment of prior influence. We then discuss the use of local analysis to study sensitivity in contexts broader than merely prior uncertainty. Finally, we try to summarize the merits and demerits of local analysis as a robustness tool.
Bayesian Regression Modeling with Interactions and Smooth Effects

There have been many recent suggestions as to how to build and estimate flexible Bayesian regression models, using constructs such as trees, neural networks, and Gaussian processes. Although there is much to commend these methods, their implementation and interpretation can be daunting for practitioners. This article presents a spline-based methodology for flexible Bayesian regression that is quite simple in terms of computation and interpretation. Smooth bivariate interactions are modeled in an economical and apparently novel way, and prior distributions that penalize complexity are used. Predictions can be based on either model selection or model averaging. Taking computation, interpretation, and predictive performance into account, the method is seen to perform well when applied to simulated and real data.
Flexible Bayesian Modelling for Survival Data

The analysis of failure time data often involves two strong assumptions. The proportional hazards assumption postulates that hazard rates corresponding to different levels of explanatory variables are proportional. The additive effects assumption specifies that the effect associated with a particular explanatory variable does not depend on the levels of other explanatory variables. A hierarchical Bayes model is presented, under which both assumptions are relaxed. In particular, time-dependent covariate effects are explicitly modelled, and the additivity of effects is relaxed through the use of a modified neural network structure. The hierarchical nature of the model is useful in that it parsimoniously penalizes violations of the two assumptions, with the strength of the penalty being determined by the data.
Large hierarchical Bayesian analysis of multivariate survival data.

Failure times that are grouped according to shared environments arise commonly in statistical practice. That is, multiple responses may be observed for each of many units. For instance, the units might be patients or centers in a clinical trial setting. Bayesian hierarchical models are appropriate for data analysis in this context. At the first stage of the model, survival times can be modelled via the Cox partial likelihood, using a justification due to Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221). Thus, questionable parametric assumptions are avoided. Conventional wisdom dictates that it is comparatively safe to make parametric assumptions at subsequent stages. Thus, unit-specific parameters are modelled parametrically. The posterior distribution of parameters given observed data is examined using Markov chain Monte Carlo methods. Specifically, the hybrid Monte Carlo method, as described by Neal (1993a,  1993b, Probabilistic inference using Markov chain Monte Carlo methods), is utilized.
Model influence functions based on mixtures

Influence functions are considered as diagnostics for model departures in parametric Bayesian inference. A baseline model density is expressed as a mixture; then the mixing distribution is perturbed. This is designed to engender perturbations which are plausible a priori. The influence of perturbations is measured for both Bayes estimates and their associated posterior expected losses. To assess the plausibility of perturbations a posteriori, an additional influence function is constructed for the Bayes factor comparing the perturbed and baseline models. The effect of perturbation on various estimands is incorporated in the analysis. 
 
 
 
Les fonctions d'influence sont considerees en tant que diagnostic de l'ecart par rapport a un modele en inference bayesienne parametrique. Nous exprimons une densite de reference comme un melange; puis la distribution de melange est perturbtee dans le but de generer des perturbations plausibles a priori. L'influence des perturbations est mesuree pour les estimations bayesiennes ainsi que pour leurs pertes moyennes a posteriori. Nous construisons une fonction d'influence additionnelle pour le facteur de Bayes comparant les modeles perturbes et ceux de reference, cela dans le but d'evaluer la plausibilite des perturbations a posteriori. Nous incorporons dans l'analyse l'effet de la perturbation sur differents estimateurs.
Local Sensitivity of Inferences to Prior Marginals

The sensitivity of posterior expectations to perturbations of prior marginals is investigated, using a local method. This approach is particularly well suited to high-dimensional parameter spaces. Qualitative and quantitative results are obtained regarding the propagation of sensitivity in multiparameter settings. More specifically, a posterior expectation is treated as a functional of the prior marginal. The derivative of this functional is evaluated at the nominal prior marginal, yielding a measure of sensitivity. This is computationally more feasible than evaluating the range of a posterior quantity over a class of priors; hence sensitivity can be assessed in high-dimensional problems. The local technique is used to assess the influence of distributional assumptions at the various stages of a hierarchical model.
The effect of mixing‐distribution misspecification in conjugate mixture models

Parametric mixture models are commonly used in the analysis of clustered data. Parametric families are specified for the conditional distribution of the response variable given a cluster-specific effect, and for the marginal distribution of the cluster-specific effects. This latter distribution is referred to as the mixing distribution. If the form of the mixing distribution is misspecified, then Bayesian and maximum-likelihood estimators of parameters associated with either distribution may be inconsistent. The magnitude of the asymptotic bias is investigated, using an approximation based on infinitesimal contamination of the mixing distribution. The approximation is useful when there is a closed-form expression for the marginal distribution of the response under the assumed mixing distribution, but not under the true mixing distribution. Typically this occurs when the assumed mixing distribution is conjugate, meaning that the conditional distribution of the cluster-specific parameter given the response variable belongs to the same parametric family as the mixing distribution. 
 
 
 
Les modeles de melange parametriques sont frequemment utilises au cours de l'analyse de donnees en grappe. Nous specifions des familles paramketriques pour la distribution conditionnelle de la variable de reponse etant donne un effet specifique a la grappe, et pour la distribution marginale des effets specifiques a la grappe. Nous qualifions cette derniere distribution de distribution de melange. Si la forme de la distribution de melange est mal specifiee, c'est alors que les estimateurs Bayesiens et maximum de vraisemblance des parametres associes a l'une des distribution sont peut etre inconsistants. Nous eetudions la magnitude du biais asymptotique a l'aide d'une approximation fondee sur la contamination infinitesimale de la distribution de melange. L'approximation s'avere utile lorsqu'il y a une expression en forme fermee de la distribution marginale de la reponse d'apres la distribution de melange presumee, mais non d'apres la vraie distribution de melange. Cela arrive habituellement lorsque la distribution de melange presumee est conjuguee, c'est a dire lorsque la distribution conditionnelle de parametre specifique a la grappe, etant donnee la variable de reponse, appartient a la meme famille parametrique que la distribution de melange.
Robustness considerations in Bayesian analysis

All statistical analyses demand uncertain inputs or assumptions. This is especially true of Bayesian analyses. In addition to the usual concerns about the agreement of the data and model, a Bayesian must contemplate the effect of an uncertain prior specification. The degree to which inferences are robust to changes in the prior is of primary interest. This article discusses some robust techniques that have been suggested in the literature. One goal is to make apparent the relevance of some of these techniques to biostatistical work.
Local sensitivity of posterior expectations

We investigate the degree to which posterior expectations are sensitive to prior distributions, using a local method based on functional differentiation. Invariance considerations suggest a family of norms which can be used to measure perturbations to the prior. The sensitivity measure is seen to depend heavily on the choice of norm; asymptotic results suggest which norm will yield the most useful results in practice. We find that to maintain asymptotically sensible behaviour, it is necessary to reduce the richness of the class of prior perturbations as the dimension of the parameter space increases. Jeffreys' prior is characterized as the prior to which inference is least sensitive.
Local sensitivity diagnostics for Bayesian inference

We investigate diagnostics for quantifying the effect of small changes to the prior distribution over a k-dimensional parameter space. We show that several previously suggested diagnostics, such as the norm of the Frechet derivative, diverge at rate n k/2 if the base prior is an interior point in the class of priors, under the density ratio topology. Diagnostics based on Φ-divergences exhibit similar asymptotic behavior. We show that better asymptotic behavior can be obtained by suitably restricting the classes of priors. We also extend the diagnostics to see how various marginals of the prior affect various marginals of the posterior.