Nicholas T. Longford

Bio: Nicholas T. Longford is an academic researcher from Imperial College London. The author has contributed to research in topics: Estimator & Small area estimation. The author has an hindex of 26, co-authored 167 publications receiving 21897 citations. Previous affiliations of Nicholas T. Longford include University of Leicester & Massey University.

A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects

Abstract: On decrit un algorithme qui utilise des formules explicites pour l'inverse et le determinant de la matrice de covariance donnee par La Motte (1972) et evite l'inversion des grandes matrices

Efficacy and safety of prostate artery embolization for benign prostatic hyperplasia: an observational study and propensity-matched comparison with transurethral resection of the prostate (the UK-ROPE study).

Abstract: Objectives To assess the efficacy and safety of prostate artery embolization (PAE) for lower urinary tract symptoms (LUTS) secondary to benign prostatic hyperplasia (BPH) and to conduct an indirect comparison of PAE with transurethral resection of the prostate (TURP). Patients and methods As a joint initiative between the British Society of Interventional Radiologists, the British Association of Urological Surgeons and the National Institute for Health and Care Excellence, we conducted the UK Register of Prostate Embolization (UK-ROPE) study, which recruited 305 patients across 17 UK urological/interventional radiology centres, 216 of whom underwent PAE and 89 of whom underwent TURP. The primary outcomes were International Prostate Symptom Score (IPSS) improvement in the PAE group at 12 months post-procedure, and complication data post-PAE. We also aimed to compare IPSS score improvements between the PAE and TURP groups, using non-inferiority analysis on propensity-score-matched patient pairs. The clinical results and urological measurements were performed at clinical sites. IPSS and other questionnaire-based results were mailed by patients directly to the trial unit managing the study. All data were uploaded centrally to the UK-ROPE study database. Results The results showed that PAE was clinically effective, producing a median 10-point IPSS improvement from baseline at 12 months post-procedure. PAE did not appear to be as effective as TURP, which produced a median 15-point IPSS score improvement at 12 months post-procedure. These findings are further supported by the propensity score analysis, in which we formed 65 closely matched pairs of patients who underwent PAE and patients who underwent TURP. In terms of IPSS and quality-of-life (QoL) improvement, there was no evidence of PAE being non-inferior to TURP. Patients in the PAE group had a statistically significant improvement in maximum urinary flow rate and prostate volume reduction at 12 months post-procedure. PAE had a reoperation rate of 5% before 12 months and 15% after 12 months (20% total rate), and a low complication rate. Of 216 patients, one had sepsis, one required a blood transfusion, four had local arterial dissection and four had a groin haematoma. Two patients had non-target embolization that presented as self-limiting penile ulcers. Additional patient-reported outcomes, pain levels and return to normal activities were very encouraging for PAE. Seventy-one percent of PAE cases were performed as outpatient or day cases. In contrast, 80% of TURP cases required at least 1 night of hospital stay, and the majority required 2 nights. Conclusion Our results indicate that PAE provides a clinically and statistically significant improvement in symptoms and QoL, although some of these improvements were greater in the TURP arm. The safety profile and quicker return to normal activities may be seen as highly beneficial by patients considering PAE as an alternative treatment to TURP, with the concomitant advantages of reduced length of hospital stay and need for admission after PAE. PAE is an advanced embolization technique demanding a high level of expertise, and should be performed by experienced interventional radiologists who have been trained and proctored appropriately. The use of cone-beam computed tomography is encouraged to improve operator confidence and minimize non-target embolizations. The place of PAE in the care pathway is between that of drugs and surgery, allowing the clinician to tailor treatment to individual patients' symptoms, requirements and anatomical variation.

Gender differences in the relationship between alcohol consumption and drink problems are largely accounted for by body water.

Abstract: It is widely reported that women drink less and have a lower prevalence of drink problems than men, but the gender differences in the relationship between level of drinking and drink problems have rarely been investigated quantitatively. This paper reports results from the Medical Research Council National Survey of Health and Development (the 1946 British Cohort) when the subjects were 43 years old. Using 7-day recall for alcohol consumption and CAGE scores of 2, 3 or 4 for drink problems, it was found that the prevalence of drink problems increased with level of alcohol consumption. Women were more likely than men to report drink problems at the same level of alcohol consumption. However, this gender difference was largely accounted for by individual differences in weight of body water. Beer accounted for the excess of men's drinking over women's and the proportion of alcohol consumed as beer was inversely related to drink problems. Eighty per cent of women and 52% of men who had drink problems in the past year reported drinking less than an average of 3 U (women) or 4 U (men) a day in the past week. As drinking levels in women begin to approach those in men, rates of drink problems in women are likely to overtake those in men because of women's greater physiological sensitivity to the effects of alcohol.

Factor Analysis for Clustered Observations.

Abstract: Classical factor analysis assumes a random sample of vectors of observations. For clustered vectors of observations, such as data for students from colleges, or individuals within households, it may be necessary to consider different within-group and between-group factor structures. Such a two-level model for factor analysis is defined, and formulas for a scoring algorithm for estimation with this model are derived. A simple noniterative method based on a decomposition of the total sums of squares and crossproducts is discussed. This method provides a suitable starting solution for the iterative algorithm, but it is also a very good approximation to the maximum likelihood solution. Extensions for higher levels of nesting are indicated. With judicious application of quasi-Newton methods, the amount of computation involved in the scoring algorithm is moderate even for complex problems; in particular, no inversion of matrices with large dimensions is involved. The methods are illustrated on two examples.

The Benefits of Being Present: Mindfulness and Its Role in Psychological Well-Being

Abstract: Mindfulness is an attribute of consciousness long believed to promote well-being. This research provides a theoretical and empirical examination of the role of mindfulness in psychological well-being. The development and psychometric properties of the dispositional Mindful Attention Awareness Scale (MAAS) are described. Correlational, quasi-experimental, and laboratory studies then show that the MAAS measures a unique quality of consciousness that is related to a variety of well-being constructs, that differentiates mindfulness practitioners from others, and that is associated with enhanced selfawareness. An experience-sampling study shows that both dispositional and state mindfulness predict self-regulated behavior and positive emotional states. Finally, a clinical intervention study with cancer patients demonstrates that increases in mindfulness over time relate to declines in mood disturbance and stress. Many philosophical, spiritual, and psychological traditions emphasize the importance of the quality of consciousness for the maintenance and enhancement of well-being (Wilber, 2000). Despite this, it is easy to overlook the importance of consciousness in human well-being because almost everyone exercises its primary capacities, that is, attention and awareness. Indeed, the relation between qualities of consciousness and well-being has received little empirical attention. One attribute of consciousness that has been much-discussed in relation to well-being is mindfulness. The concept of mindfulness has roots in Buddhist and other contemplative traditions where conscious attention and awareness are actively cultivated. It is most commonly defined as the state of being attentive to and aware of what is taking place in the present. For example, Nyanaponika Thera (1972) called mindfulness “the clear and single-minded awareness of what actually happens to us and in us at the successive moments of perception” (p. 5). Hanh (1976) similarly defined mindfulness as “keeping one’s consciousness alive to the present reality” (p. 11). Recent research has shown that the enhancement of mindfulness through training facilitates a variety of well-being outcomes (e.g., Kabat-Zinn, 1990). To date, however, there has been little work examining this attribute as a naturally occurring characteristic. Recognizing that most everyone has the capacity to attend and to be aware, we nonetheless assume (a) that individuals differ in their propensity or willingness to be aware and to sustain attention to what is occurring in the present and (b) that this mindful capacity varies within persons, because it can be sharpened or dulled by a variety of factors. The intent of the present research is to reliably identify these inter- and intrapersonal variations in mindfulness, establish their relations to other relevant psychological constructs, and demonstrate their importance to a variety of forms of psychological well-being.

Multilevel Statistical Models

Abstract: Contents Dedication Preface Acknowledgements Notation A general classification notation and diagram Glossary Chapter 1 An introduction to multilevel models 1.1 Hierarchically structured data 1.2 School effectiveness 1.3 Sample survey methods 1.4 Repeated measures data 1.5 Event history and survival models 1.6 Discrete response data 1.7 Multivariate models 1.8 Nonlinear models 1.9 Measurement errors 1.10 Cross classifications and multiple membership structures. 1.11 Factor analysis and structural equation models 1.12 Levels of aggregation and ecological fallacies 1.13 Causality 1.14 The latent normal transformation and missing data 1.15 Other texts 1.16 A caveat Chapter 2 The 2-level model 2.1 Introduction 2.2 The 2-level model 2.3 Parameter estimation 2.4 Maximum likelihood estimation using Iterative Generalised Least Squares (IGLS) 2.5 Marginal models and Generalized Estimating Equations (GEE) 2.6 Residuals 2.7 The adequacy of Ordinary Least Squares estimates. 2.8 A 2-level example using longitudinal educational achievement data 2.9 General model diagnostics 2.10 Higher level explanatory variables and compositional effects 2.11 Transforming to normality 2.12 Hypothesis testing and confidence intervals 2.13 Bayesian estimation using Markov Chain Monte Carlo (MCMC) 2.14 Data augmentation Appendix 2.1 The general structure and maximum likelihood estimation for a multilevel model Appendix 2.2 Multilevel residuals estimation Appendix 2.3 Estimation using profile and extended likelihood Appendix 2.4 The EM algorithm Appendix 2.5 MCMC sampling Chapter 3. Three level models and more complex hierarchical structures. 3.1 Complex variance structures 3.2 A 3-level complex variation model example. 3.3 Parameter Constraints 3.4 Weighting units 3.5 Robust (Sandwich) Estimators and Jacknifing 3.6 The bootstrap 3.7 Aggregate level analyses 3.8 Meta analysis 3.9 Design issues Chapter 4. Multilevel Models for discrete response data 4.1 Generalised linear models 4.2 Proportions as responses 4.3 Examples 4.4 Models for multiple response categories 4.5 Models for counts 4.6 Mixed discrete - continuous response models 4.7 A latent normal model for binary responses 4.8 Partitioning variation in discrete response models Appendix 4.1. Generalised linear model estimation Appendix 4.2 Maximum likelihood estimation for generalised linear models Appendix 4.3 MCMC estimation for generalised linear models Appendix 4.4. Bootstrap estimation for generalised linear models Chapter 5. Models for repeated measures data 5.1 Repeated measures data 5.2 A 2-level repeated measures model 5.3 A polynomial model example for adolescent growth and the prediction of adult height 5.4 Modelling an autocorrelation structure at level 1. 5.5 A growth model with autocorrelated residuals 5.6 Multivariate repeated measures models 5.7 Scaling across time 5.8 Cross-over designs 5.9 Missing data 5.10 Longitudinal discrete response data Chapter 6. Multivariate multilevel data 6.1 Introduction 6.2 The basic 2-level multivariate model 6.3 Rotation Designs 6.4 A rotation design example using Science test scores 6.5 Informative response selection: subject choice in examinations 6.6 Multivariate structures at higher levels and future predictions 6.7 Multivariate responses at several levels 6.8 Principal Components analysis Appendix 6.1 MCMC algorithm for a multivariate normal response model with constraints Chapter 7. Latent normal models for multivariate data 7.1 The normal multilevel multivariate model 7.2 Sampling binary responses 7.3 Sampling ordered categorical responses 7.4 Sampling unordered categorical responses 7.5 Sampling count data 7.6 Sampling continuous non-normal data 7.7 Sampling the level 1 and level 2 covariance matrices 7.8 Model fit 7.9 Partially ordered data 7.10 Hybrid normal/ordered variables 7.11 Discussion Chapter 8. Multilevel factor analysis, structural equation and mixture models 8.1 A 2-stage 2-level factor model 8.2 A general multilevel factor model 8.3 MCMC estimation for the factor model 8.4 Structural equation models 8.5 Discrete response multilevel structural equation models 8.6 More complex hierarchical latent variable models 8.7 Multilevel mixture models Chapter 9. Nonlinear multilevel models 9.1 Introduction 9.2 Nonlinear functions of linear components 9.3 Estimating population means 9.4 Nonlinear functions for variances and covariances 9.5 Examples of nonlinear growth and nonlinear level 1 variance Appendix 9.1 Nonlinear model estimation Chapter 10. Multilevel modelling in sample surveys 10.1 Sample survey structures 10.2 Population structures 10.3 Small area estimation Chapter 11 Multilevel event history and survival models 11.1 Introduction 11.2 Censoring 11.3 Hazard and survival funtions 11.4 Parametric proportional hazard models 11.5 The semiparametric Cox model 11.6 Tied observations 11.7 Repeated events proportional hazard models 11.8 Example using birth interval data 11.9 Log duration models 11.10 Examples with birth interval data and children s activity episodes 11.11 The grouped discrete time hazards model 11.12 Discrete time latent normal event history models Chapter 12. Cross classified data structures 12.1 Random cross classifications 12.2 A basic cross classified model 12.3 Examination results for a cross classification of schools 12.4 Interactions in cross classifications 12.5 Cross classifications with one unit per cell 12.6 Multivariate cross classified models 12.7 A general notation for cross classifications 12.8 MCMC estimation in cross classified models Appendix 12.1 IGLS Estimation for cross classified data. Chapter 13 Multiple membership models 13.1 Multiple membership structures 13.2 Notation and classifications for multiple membership structures 13.3 An example of salmonella infection 13.4 A repeated measures multiple membership model 13.5 Individuals as higher level units 13.5.1 Example of research grant awards 13.6 Spatial models 13.7 Missing identification models Appendix 13.1 MCMC estimation for multiple membership models. Chapter 14 Measurement errors in multilevel models 14.1 A basic measurement error model 14.2 Moment based estimators 14.3 A 2-level example with measurement error at both levels. 14.4 Multivariate responses 14.5 Nonlinear models 14.6 Measurement errors for discrete explanatory variables 14.7 MCMC estimation for measurement error models Appendix 14.1 Measurement error estimation 14.2 MCMC estimation for measurement error models Chapter 15. Smoothing models for multilevel data. 15.1 Introduction 15.2. Smoothing estimators 15.3 Smoothing splines 15.4 Semi parametric smoothing models 15.5 Multilevel smoothing models 15.6 General multilevel semi-parametric smoothing models 15.7 Generalised linear models 15.8 An example Fixed Random 15.9 Conclusions Chapter 16. Missing data, partially observed data and multiple imputation 16.1 Creating a completed data set 16.2 Joint modelling for missing data 16.3 A two level model with responses of different types at both levels. 16.4 Multiple imputation 16.5 A simulation example of multiple imputation for missing data 16.6 Longitudinal data with attrition 16.7 Partially known data values 16.8 Conclusions Chapter 17 Multilevel models with correlated random effects 17.1 Non-independence of level 2 residuals 17.2 MCMC estimation for non-independent level 2 residuals 17.3 Adaptive proposal distributions in MCMC estimation 17.4 MCMC estimation for non-independent level 1 residuals 17.5 Modelling the level 1 variance as a function of explanatory variables with random effects 17.6 Discrete responses with correlated random effects 17.7 Calculating the DIC statistic 17.8 A growth data set 17.9 Conclusions Chapter 18. Software for multilevel modelling References Author index Subject index

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.