4. Modelling Survival Data in Medical Research

The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques.

Bayesian Approaches to Clinical Trials and Health-Care Evaluation: Spiegelhalter/Clinical Trials and Health-Care Evaluation

The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum’s Three-Parameter models have been implemented, whereas for polytomous data Semejima’s Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

/pdf/ltm-an-r-package-for-latent-variable-modeling-and-item-2ncqk3b6zo.pdf

ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses

Risk adjustment for measuring health care outcomes

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

/pdf/generalized-multilevel-structural-equation-modeling-1wlutp7neh.pdf

Generalized multilevel structural equation modeling

Based on a longtime course for master's level students at the London School of Economics and Politics, where the authors are based, this text concentrates on the multivariate methods so useful to social science problems involving correlational rather than causal relationships. Chapters with application examples and further readings cover data preliminaries, cluster analysis, multidimensional scaling, correspondence analysis, principal components analysis, factor analysis, and latent variable methods. While mathematical demands are minimal, these methods require use of a computer software package; an auxiliary website supplies data sets and code for use with SPSS.

The Analysis and Interpretation of Multivariate Data for Social Scientists

League tables and their limitations: Statistical issues in comparisons of institutional performance - Discussion

We show how to score binary responses to seven items to give a measure of approval for abortion, using a latent variable approach. Three models are suggested to cope with item non-response. The methods are exemplified with the use of a selection of data from the 1986 British Social Attitudes Panel Survey. This paper will use simple latent variable modelling of binary response to try to extract a common factor from expressed attitudes to abortion. The common factor is then used to score the response patterns. We shall also fit models. to allow for item non-response, both in a neutral fashion, and with an attempted recovery of information from the pattern of non-response. The impetus for this work came from the wish to produce a scoring for use in multilevel modelling work (see Wiggins et al., 1990).

Scoring attitudes to abortion

Summary. The paper focuses on the hospital and community health service component of the current formula for the target allocation of annual ‘weighted capitation’ funding of England's 304 primary care trusts—a formula that responds to political pressure to tackle continuing health inequalities. The history of allocation formulae for the National Health Service is briefly reviewed, before exposing the composition and provenance of the hospital and community health service component. Under the heading ‘Logical and statistical deficiencies’ we consider various frameworks, model misspecification, allowance for age, ins and outs of supply, unmet need and wrong signs, and replicability and robustness—questions that are relevant to trust in the current formula based on the regression of age-standardized current utilization on a multiplicity of socioeconomic variables. We conclude that it might be better to put future resources into developing direct, rather than proxy, measurement of health needs.

How not to fund hospital and community health services in England

A 1977 ANTIQUITY paper included a study of Neutron Activation Analyses of Ubaid pottery from Mesopotamia and the Arabian Gulf (Oates et al. 1977). A re-examination finds that the interpretation of the statistical analysis was mistaken and shows that the data do not prove the existence of ‘seafaring merchants of Ur’ in the Ubaid period.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0003598X00047463

Pottery and p-values: ‘Seafaring merchants of Ur?’ re-examined

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