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IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE DIVISION OF PRIMARY CARE AND POPULATION HEALTH SCIENCES RESEARCH STUDENTSHIPS AND COLLABORATIVE RESEARCH STUDENTSHIPS IN EPIDEMIOLOGY, BIOSTATISTICS AND SOCIAL STATISTICS We are seeking five enthusiastic and highly motivated individuals to join a prestigious and stimulating research environment in the Department of Epidemiology & Public Health, which currently has nine PhD students and research grants totalling ca £5.3 million. The Department's focus includes leading-edge work in epidemiology, spatial statistics and Bayesian statistical methods, medical statistics, statistical computing, clinical trials and meta-analysis. There is considerable potential to add to the list of possible projects below, and candidates are encouraged to submit proposals relevant to the Department's work in discussion with an appropriate supervisor. Four of the available studentships, including a collaborative research studentship, are funded by the Medical Research Council (MRC), and one collaborative research studentship is funded jointly by the Economic & Social Research Council (ESRC) and Ealing Hammersmith & Hounslow Health Authority. ELIGIBILITY: PLEASE CHECK YOUR ELIGIBILITY for these studentships by reading the eligibility rules which may be accessed by clicking on Research Council Studentships eligibility information at: http://www.ic.ac.uk/templates/prospectus_text_3.asp?P=1397 Candidates for Research Studentships who are in any doubt about their eligibility (eg: those with overseas connections, concerned about residence qualifications) should submit IN WRITING the following details to: Higher Degrees Assistant (Postgraduate Medicine), Imperial College School of Medicine, Commonwealth Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, Fax: +44 (0)208 743 6764. DETAILS: (1) Date of Birth, (2) Place of birth, (3) Dates and places of residence (self and parents), (4) Academic qualifications with dates and places, (5) Details of any employment since graduation, (6) Source of first degree funding. APPLICATIONS: Please send a full CV, including details of referees and a covering letter explaining your research interests, to Simon Sheffield at: [log in to unmask] , or by fax to: +44 (0)207 402 2150, or by post to: Simon Sheffield, Departmental Manager, Department of Epidemiology and Public Health, Imperial College School of Medicine, Norfolk Place, London W2 1PG, UK. POSSIBLE PhD PROJECTS, TO COMMENCE 1st OCTOBER 2000 1. Quantifying and explaining variations in health in relation to biological and social risk factors at small-area scale (ESRC Collaborative Research Studentship). For informal discussion, contact Dr Nicky Best (phone: +44 0207 594 3320; email: n.best @ic.ac.uk) 2. Determination of the relationship between maternal haemaglobin, mean corpuscular volume and birthweight and differentiation of anaemia from plasma volume expansion. For informal discussion contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email: [log in to unmask]) 3. Studies of exposure and health effects near landfill sites - a number of projects are possible in this area and candidates should contact Dr Lars Jarup for informal discussion (email: [log in to unmask] ; phone: +44 0207 594 3337). 4. Effect of measurement error in dietary and nutrient variables in the International Population Study of Macro- and Micro-nutrients and Blood Pressure (INTERMAP) For informal discussion, contact Professor Paul Elliott (phone: +44 0207 594 3328, email: [log in to unmask]) 5. Dietary and nutritional factors in relation to blood pressure: the INTERMAP study. For informal discussion, contact Professor Paul Elliott (phone: +44 0207 594 3328, email: [log in to unmask]) 6. Birth and placenta weight as measures of childhood and adult health; a follow-up of 40,000 children born from 1972 = 1987 in an area of Denmark. For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email: [log in to unmask]) 7. Investigations on the influence on birthweight of maternal socioeconomic status, maternal build and response to pregnancy; a study of 400,000 births in the Thames area during a ten-year period. For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email: [log in to unmask]) 8. Space-time modelling of infectious disease surveillance data. For informal discussion, contact Dr Leonhard Knorr-Held (email ([log in to unmask] ) 9. Heterogeneity in couple fertility: the use of frailty models. For informal discussion, contact Dr Mike Joffe (phone: +44 0207 594 3338, email: [log in to unmask]) 10. Lifetime biological and social risk and protective factors in prediction of adult health behaviour. For informal discussion contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask]) 11. Early childhood health as a predictor of adult health. For informal discussion contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask]) 12. Comparison of methods for analysing repeated measurement data and problems due to the presence of missing observations. For informal discussion, contact Dr Rumana Omar (phone: +44 0208 383 1768, email: [log in to unmask]) 13. Evaluation methods of genome mapping. For informal discussion, contact Dr Berthold Lausen (phone: +44 0208 393 3255, email: [log in to unmask]) 14. Impact of dosimetric and other uncertainties on estimation of cancer risks from the Japanese atomic bomb survivors. For informal discussion, contact Dr Mark Little (phone +44 (0)207 594 3379, email - temporarily c/o: [log in to unmask]) PROJECT DESCRIPTIONS 1. Quantifying and explaining variations in health in relation to biological and social risk factors at small-area scale (ESRC Collaborative Research Studentship). For informal discussion, contact Dr Nicky Best (phone: +44 0207 594 3320; email: n.best @ic.ac.uk) It is well established that socio-economic disadvantage has a powerful impact on morbidity and mortality. This project aims to improve our understanding of the reasons underlying these social inequalities in ill-health. The aim will be to develop models to describe the risk profiles of affluent and deprived areas in terms of biological and social characteristics, plus measures of environmental pollution, measured at individual and area level. These will then be combined with small-area models of disease outcomes to attempt to separate and quantify the different sources of variation in disease risk. Health outcomes of interest may include various cancers, particularly breast cancer; hospital admissions for e.g. respiratory illness; and low birthweight. Data on potential risk factors are available from various sources, such as the Health Survey for England, the 1991 UK Census, and national pollution datasets. Other sources of information on e.g. primary care, income, and crime, will be explored in collaboration with Ealing, Hammersmith and Hounslow Health Authority who are the industrial partners for this project. The findings from this project will be fed directly into Local Authority Health Strategies, including Regeneration Strategies and Community Plans, to reduce social exclusion. 2. Determination of the relationship between maternal haemaglobin, mean corpuscular volume and birthweight and differentiation of anaemia from plasma volume expansion. For an informal discussion contact Dr Tina Kold-Jensen (phone: +44 (0)207 594 3335, email: [log in to unmask]) Objectives: To determine the relationship between maternal haemoglobin (Hb), mean corpuscular volume (mcv) and birthweight and differentiate anaemia from plasma volume expansion. Background: Pregnancy produces plasma volume expansion and haemoglobin concentration falls accordingly. Mcv however does not change substantially. A drop in Hb with a large fall in mcv causes anaemia, whereas a drop in Hb with little or no fall in mcv is caused by plasma volume expansion. Severe anaemia (<80g/l) is associated with the birth of small babies (from both pre-term labour and growth restriction) but so is high Hb (>120 g/l). The incidence of low birthweight and of pre-term labour is at its lowest when the haemoglobin concentration is between 95 and 105 g/l but to date there have been no studies that have taken into account changes in mcv. Design/Approaches: The majority of obstetric units in the North West Thames region have used St Mary’s Maternity Information System (SMMIS) since 1988 to record clinical information throughout pregnancy and delivery. This database contains information on birth weight and gestational age and now comprises more than 400,000 pregnancies and births. Full blood counts are recorded on computer at the pathological departments and download from these systems will be obtained and linked to the SMMIS data set. Training: The Ph.D. student will gain experience in analysing large datasets with repeated events (some women have many blood samples drawn) and modelling variables. Also the student will obtain the downloads and link the two databases. 3. Studies of exposure and health effects near landfill sites. For details and informal discussion, contact Dr Lars Jarup (email: [log in to unmask] ; phone: +44 0207 594 3337). 4. Effect of measurement error in dietary and nutrient variables in the International Population Study of Macro- and Micro-nutrients and Blood Pressure (INTERMAP) For informal discussion, contact Professor Paul Elliott (phone: +44 0207 594 3328, email: [log in to unmask]) Measurement error is a key issue in many epidemiological studies. Bayesian modelling offers a flexible way of realistically modelling a large variety of measurement error situations, of combining sources of information on the measurement error process, and of coherently propagating uncertainty of the parameter estimates. Methodological issues connected with correlated measurement errors on several covariates and their consequences, with model choice and model misspecification for the distribution of the unobservable covariates, are topics to be investigated. Applications will inlcude the INTERMAP study of diet and blood pressure (see below). The project will involve implementation of MCMC methods and some computer programming. The INTERMAP study is an international co-operative epidemiological investigation into the relation of dietary macronutrients, micronutrients and other factors with blood pressure. The study comprises more than 4,700 men and women aged 40-59 in 17 population samples in four countries (UK,US, China, Japan). Data collection included four 24-hour dietary recalls, two 24-hour unrine collections (for the measurement of urinary excretion of sodium, potassium, calcium, magnesium and urea -- as a marker of protein intake),extensive information on alcohol intake, questionairre data, and repeated measures of height, weight and blood pressure (8 times in total on four occasions). There was standardised training and extensive quality control in all aspects of the study. The dietary information has been converted into nutrients following, as necessary, extensive updating of the existing nutrient databases for the participating countries. The study database is currently being finalised and the first publications from the study are due to be completed this year. The study offers an ideal opportunity to investigate the extent and effect of measurement error in dietary, nutrient and other variables based on a highly standardised and well characterised nutrient dataset, and will give an excellent opportunity to apply latest hierarchical statistical methods to investigate this problem. 5. Dietary and nutritional factors in relation to blood pressure: the INTERMAP study. For informal discussion, contact Professor Paul Elliott (phone: +44 0207 594 3328, email: [log in to unmask]) The study offers an excellent opportunity to investigate the associations of micro and macro-nutrient intake with blood pressure within the context of a large epidemiological study, with excellent data on both blood pressure and dietary exposures. A range of potential projects are available depending on the experience and interests of the candidate. The INTERMAP study is an international co-operative epidemiological investigation into the relation of dietary macronutrients, micronutrients and other factors with blood pressure. The study comprises more than 4,700 men and women aged 40-59 in 17 population samples in four countries (UK,US, China, Japan). Data collection included four 24-hour dietary recalls, two 24-hour unrine collections (for the measurement of urinary excretion of sodium, potassium, calcium, magnesium and urea -- as a marker of protein intake),extensive information on alcohol intake, questionairre data, and repeated measures of height, weight and blood pressure (8 times in total on four occasions). There was standardised training and extensive quality control in all aspects of the study. The dietary information has been converted into nutrients following, as necessary, extensive updating of the existing nutrient databases for the participating countries. The study database is currently being finalised and the first publications from the study are due to be completed this year. 6. Birth and placenta weight as measures of childhood and adult health; a follow-up of 40,000 children born from 1972 = 1987 in an area of Denmark. For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email: [log in to unmask]) At the Department of Gynaecology and Obstetrics at Odense University Hospital in Denmark all pregnant women who attended prophylactic antenatal care- between 1972 and 1987 (N=40,666) were routinely interviewed by a secretary. Some women delivered more than once during the study period (28,629 different women). At the first antenatal visit at the 20th weeks’ of gestation, reproductive, medical, educational, occupational, and lifestyle histories were recorded by a secretary. At delivery a midwife recorded birth weight and length, placenta weight, diameter and thickness, funniculus length and number of vessels. The children are now between 13 and 27 years old and it is possible to link them to Danish central register of all hospital submissions (Discharge diagnosis), The Cancer Register and the Death Register. It is also possible to look at siblings with same intrauterine environment. 7. Investigations on the influence on birthweight of maternal socioeconomic status, maternal build and response to pregnancy; a study of 400,000 births in the Thames area during a ten-year period. For informal discussion, contact Dr Tina Kold-Jensen (phone: +44 0207 594 3335, email: [log in to unmask]) The purpose of this study is, by the use of almost half a million births, to investigate the influence on birthweight of maternal socio-economic status (Carstairs index), maternal build (height, weight, body mass index), and response to pregnancy (as assessed by the fall in haemoglobin concentration, changes in blood pressure and pre-eclampsia during pregnancy). These variables are, however, not operating independently and therefore a model taking into account the combined effect of these factors will be developed. This will make it possible to predict the birthweight of a baby based on the maternal characteristics and may give rice to useful recommendations about ideal body build, blood pressure and haemoglobin levels for successful pregnancy. 8. Space-time modelling of infectious disease surveillance data. For an informal discussion, contact Dr Leonhard Knorr-Held (email ([log in to unmask] ) This goal of this project is to develop statistical methodology for the analysis of infectious disease data in space and time. Such data typically arises as so-called surveillance data. Classical methods for infectious disease incidence mostly analyse small groups such as households, and focus on estimating transmission parameters of the underlying epidemic model. A different aim is to describe and predict spatio-temporal patterns of diseases over a large region. Recent approaches for time-space variation of non-infectious diseases will be modified to properly describe and predict outbreaks and decline of various infectious diseases such as Influenza, Measles, Mumps or Meningitis. Space-time interactions as well as seasonal patterns are inherent features of such data and have to be incorporated in the model. Furthermore, adjustments have to be made to appreciate the fact that individuals, already being infected, become immune and are no longer susceptible to the disease. Typically the models will have to incorporate the numbers of infected cases at time t-1 as additional covariates with possibly non-linear effects on the rate of infection at time t. For each area, this might also involve the cases in neighboring areas with different weight structures to be considered. Finally, there is need to account for sampling variability and additional unstructured heterogeneity. Throughout, Markov chain Monte Carlo methods will be used as the current state-of-the-art inference technique for such complex models. 9. Heterogeneity in couple fertility: the use of frailty models. For informal discussion, contact Dr Mike Joffe (phone +44 0207 594 3338, email: [log in to unmask]) The fertility of a variety of populations has been characterised, using as a measure the time taken by a couple to conceive (Time To Pregnancy, TTP), and a remarkably large degree of between-couple heterogeneity has been found. On evolutionary grounds, it is difficult to explain why a substantial proportion of the population, with no obvious disease or other health problem, have low per-cycle probability of conception. Furthermore, the semen quality of men is remarkably poor when compared with that of other mammalian species. It is unclear whether these phenomena are linked, and if so, whether they are of recent historical origin, and/or whether there are major differences between different populations e.g. according to genetic or nutritional differences. The possibility of a fall in the sperm count, which appears to have occurred in certain places (e.g. Paris, Gent), needs to be seen in this context. This project will explore between- and within-couple fertility in a large survey, the National Child Development Study, which is representative of the population born in Britain in 1958. Respondents who were interviewed at age 33 provided values of TTP for almost all non-accidental pregnancies (91 percent of female and 84 percent of male respondents, N=3132 and 2576, respectively). These data will be analysed using frailty models, which introduce a couple-specific element (a random effect) to acknowledge between-couple differences. The distribution of these random effects may then depend on characteristics of the couple. The effect of these characteristics on relative fertility will be explored and quantified, and implications for models of fertility in the population will be assessed. 10. Lifetime biological and social risk and protective factors in prediction of adult health behaviour. For informal discussion, contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask]) Aims are: To explore the effects of childhood social standing considering biological modifying factors, and own health behaviour in adolescence, on nutritional factors (e.g. obesity), physical fitness, smoking and drinking up to age 30 years and to study variations by populations/population groups. To explain the association of health behaviour and lifetime biological and social risk and protective factors with the self-assessed health, health related quality of life and with inequalities in health until the age of thirty, by population groups (gender, social class, marital, employment status, country). We will use the opportunity offered by social differences between countries to compare influence of social factors on health and health behaviour. Populations and data: The study consist of the two national datasets, one from Finland and one from Britain, representing the sme generation and have similar ages of follow-up (pregnancy, birth, at 1 or 2 years , teenage years 13-16 years and 31-33 years). The Finnish study (Cohort 1966) consists of 12231 births in 1966. Data collection started in the 24th gestational week on social background, health and pregnancy and delivery and in the British Cohort for (n=17733) data has been collected from medical records, by questionnaires and interviews. In the questionnaires of 31 years follow-up in 1997-8 for Cohort 1966 the comparability with existing British database was taken into account. Data has also been collected from various registers and the existing datafiles comprise thousands of variables. Outcomes at age 30: nutritional status (weight, height, waist-hip measurement, body mass index), drinking, smoking, physical fitness and self-assessed health, health related quality of life measure, social status at 30 (education, income, household facilities, marital status, employment). Explanatory variables at various ages: maternal smoking, parental education and social standing at birth and at 14, parental health behaviour at subject’s age of 14, subject’s smoking and drinking at teenage and body size. Subject’s health at birth, at 1 and 14. We will apply the cumulative social class measure that takes into account the class of the parents and the subject’s occupational history, to present occupational (social) class in predicting the differences in current health and health behaviour. 11. Early childhood health as a predictor of adult health. For informal discussion, contact Dr Marjo-Riitta Jarvelin (phone: +44 0207 594 3345, email: [log in to unmask]) Aim: To study the association between early childhood health and well-being and adult health (/adolescent health). The key question is that are the people in lower socioeconomic groups less healthy than the people in higher socioeconomic groups because they experienced more health problems in childhood? What is the contribution of health selelction? Populations and data: The study consist of the two datasets, from northern Finland, one for 1966 (n=12231) and one for 1985-86 (n=9479). Data collection started in the 24th gestational week on social background, health and pregnancy and delivery. Data has been later collected from medical records, by questionnaires, interviews and clinical examinations. Data has also been collected from various registers and the existing data files comprise thousands of variables. Outcomes at age 30: self-rated health, both hospital and non-hospital treated diseases. Explanatory and confounding variables at various ages: health variables since birth (to some extent prenatally) and early variables (metal disorders, neurological disabilities, other long term diseases, living conditions, health behaviour. 12. Comparison of methods for analysing repeated measurement data and problems due to the presence of missing observations. For informal discussion, contact Dr Rumana Omar (phone: +44 0208 393 3255, email: [log in to unmask]) A variety of methods are available for the analysis of repeated measurement data such as the use of summary statistics, hierarchical random effects models and marginal models. We have recently compared various methods for repeated measurement analysis for the case of continuous outcomes from a clinical trial. Some methods are relatively simple to use, but the other more complex methods have greater flexibility. Classical multilevel models impose more restrictive distributional assumptions compared with Bayesian and marginal models. Issues in statistical analysis tend to be more complicated for discrete outcome data. Furthermore, observations may be missing at intermittent times and the presence of missing observations can make the analysis more problematic. If in particular the reason for the missingness of an observation is related to the outcome variable being investigated, it complicates both the analysis and the interpretation of the data. The various methods available for the analysis of repeated measurement data differ in their sensitivity to missing data. Some methods require ‘missing completely at random’ (MCAR) assumption, whereas others require ‘missing at random assumption’ (MAR). In practice it is possible that data are not missing at random (NMAR). This project has two primary objectives. It will examine how the various methods available for the analysis of repeated measurement data compare in terms of flexibility, ease of application, interpretation and underlying assumptions for both continuous and discrete outcome data. A number of data sets both from clinical trials and longitudinal observational studies will be used for this purpose. The project will also explore methods for investigating the patterns of missingness in the data, that is whether the missing data mechanism is MAR, MCAR or NMAR, and propose strategies to deal with missingness in the analysis. 13. Evaluation methods of genome mapping. For informal discussion, contact Dr Berthold Lausen (phone: +44 0208 393 3255, email: [log in to unmask]) The construction of a genetic or a physical map is a basic goal of all genome projects. The underlying mathematical/computational challenge can be seen as a variant of the travelling salesman problem. The aim is to find the order (of visits) of the genetic marker loci which minimises the sum of the distances. Consequently, optimal solutions are not feasible for relative large numbers marker loci. The PhD-project aims to improve the statistical theory and to address several important data analytic issues. The project will be collaborative with Tim Aitman of the Molecular Medicine Group, MRC Clinical Sciences Centre, Hammersmith campus). It will focus on radiation hybrid (RH) mapping. One RH example is the data set of the Insulin Resistance Team (headed by Dr Tim Aitman) (Al-Majali, K.M., et al., 1999, A high-resolution radiation hybrid map of the proximal region of rat chromosome 4, Mammalian Genome 10, 471-476). For example important data analytic issues of ongoing research projects are to model the measurement error caused by possible PCR failure or weak positive results or to estimate and model jointly linkage groups of the RH data and information of genetic maps. The development of evaluation methods of constructed and published genome maps is an important issue of genome and post genome projects. Recently bootstrap methods are suggested to provide a measure of stability. The PhD-project will address the data analytic issues and will develop and analyse bootstrap evaluation by means of Monte-Carlo simulation and mathematical considerations. The project is an excellent opportunity to collaborate in an important and fast developing research area. Moreover, the project provides research experience in highly important application fields of statistical genetics and bioinformatics. 14. Impact of dosimetric and other uncertainties on estimation of cancer risks from the Japanese atomic bomb survivors. For informal discussion, contact Dr Mark Little (phone +44 (0)207 594 3379, email - temporarily c/o: [log in to unmask]) Objectives: Development of improved methods for analysis of random and systematic dosimetric uncertainties in the Japanese atomic bomb survivor Life Span Study (LSS) data, and consideration of uncertainties in projecting radiation-induced cancer risks to low doses, to the end of life, and across populations to enable a full assessment to be made of uncertainties in low dose-rate cancer risk. Methods and data: Bayesian Markov Chain Monte Carlo models will be fitted to the latest LSS cancer incidence and mortality data. Various dose measurement error models will be investigated. No standard statistical software can be used for implementing these algorithms and hence special computer programs will be written. A variety of complex disease models (hybrid absolute/relative risk, mechanistic multistage) will be investigated, and in particular a variety of different dose-responses (linear, quadratic, exponential) will be assessed. There will be interactions with researchers in the University of Munich, INSERM U.170 (Paris), the National Radiological Protection Board and RIVM (Bilthoven) as part of a collaborative EC contract. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%