1990 The statistics of genetic linkage studies

Linkage and linkage disequilibrium are key concepts in genetic epidemiology. Two loci or genes on the same chromosome are linked (in linkage) if they are transmitted together from parent to offspring more often than expected under independent inheritance (i.e. recombination fraction of 0.5).

In population genetics, two loci are in linkage disequilibrium (or gametic phase disequilibrium) if they are found together on the same haplotype more often than expected through a random formation of haplotypes from alleles based on their relative frequencies in the population. Linkage disequilibrium (LD) in populations extends over smaller regions of the genome than does linkage in conventional family studies.

Models and statistical methods have been developed and successfully applied for identifying disease susceptibility loci and for genetic mapping based on both traditional linkage studies and linkage disequilibrium studies in diseases such as multiple sclerosis.

References

1. Lu SJ, Day NE, Degos L, Lepage V, Wang PC, Chan SH, Simons M, McKnight B, Easton D, Yi Z, DeThe G. Linkage of a nasopharyngeal carcinoma susceptibility locus to the HLA region. Nature 1990; 346: 470-471.
2. Holmans P, Clayton D. Efficiency of typing unaffected relatives in an affected-sib-pair linkage study with single-locus and multiple tightly linked markers. American Journal of Human Genetics 1995; 57: 1221-1232.
3. Sawcer S, Jones HB, Feakes R, Gray J, Smaldon N, Chataway J, Robertson N, Clayton D, Goodfellow PN, Compston A. A genome screen in multiple sclerosis reveals susceptibility loci on chromosome 6p21 and 17q22. Nature Genetics 1996; 13: 464-468.
4. Sawcer S, Jones HB, Judge D, Visser F, Compston A, Goodfellow PN, Clayton D. Empirical genome-wide significance levels established by whole genome simulations. Genetic Epidemiology 1997; 14: 223-229.
5. Chiano MN, Clayton, DG. Fine genetic mapping using haplotypes and the missing data problem. Annals of Human Genetics 1998; 62: 55-60.
6. Clayton D, Jones HB. Transmission/disequilibrium tests for extended marker haplotypes. American Journal of Human Genetics 1999; 65: 1161-1169.

 

1992 BUGS program: Bayesian inference using Gibbs sampling

The BUGS project exploits overlaps between graphical modelling theory and modern computing to provide a language and software for constructing and analysing realistically complex statistical models. Arbitrarily complex models are built using small generic components, in much the same way as with a child’s construction set (albeit less colourful).

References

1. Thomas A, Spiegelhalter DJ, Gilks WR BUGS: A Program to Perform Bayesian Inference Using Gibbs Sampling. In J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds) Bayesian Statistics 4, pp. 837-842, Oxford University Press, Oxford, UK: 1992.
2. Gilks WR, Thomas A, Spiegelhalter D J. A language and program for complex Bayesian modelling. The Statistician 1994; 43: 169-178.
3. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions. Statistics in Medicine 2009; 28: 3049-3067.
4. LunnDJ, Thomas A, Best N, Spiegelhalter D. WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10 (4): 325-337.
5. Lunn D, Jackson C, Best N, Spiegelhalter DJ, Thomas A. The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman & Hall: 2012.

 

1994 Surgical Performance: league tables, Bristol Inquiry, and risk-adjustment

Based initially on surgical performance, the MRC Biostatistics Unit has played a leading role in informing the practice of performance monitoring in health care.

Significant background methodological work has been combined with high profile involvement in UK inquiries, such as those related to the Bristol Infirmary and Harold Shipman. Close collaboration with the Healthcare Commission (now Care Quality Commission) led to numerous publications and had an important impact on the way hospitals are regulated and monitored. Recently, a unified approach to regulation and monitoring was advocated in a 2012 Royal Statistical Society discussion paper.

References

1. De Leval MR, Francois K, Bull C, Brawn W, Spiegelhalter D, de Leon SY, Williams WG, Drinkwater Jr DC, Laks H. Analysis of a cluster of surgical failures: application to a series of neonatal arterial switch operations. Journal of Thoracic and Cardiovascular Surgery 1994; 107: 914-924.
2. Steiner SH, Cook RJ, Farewell VT, Treasure T. Monitoring surgical performance using risk adjusted cumulative sum charts. Biostatistics 2000; 1: 441-452.
3. Spiegelhalter DJ, Aylin P, Best NG, Evans SJW, Murray GD. Commissioned analysis of surgical performance using routine data: lessons from the Bristol inquiry. Journal of the Royal Statistical Society Series A (Statistics in Society) 2002; 165: 191-221.
4. Spiegelhalter D, Grigg O, Kinsman R, Treasure T. Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery. International Journal of Quality in Health Care 2003; 15: 7-13.
5. Grigg OA, Farewell VT, Spiegelhalter DJ. Use of risk-adjsuted CUSUM and RRSPRT charts for monitoring in medical contexts. Statistical Methods in Medical Research 2003; 12: 147-170.
6. Spiegelhalter DJ, Sherlaw-Jophnson C, Bardsley M, Blunt I, Wood C, Grigg O. Statistical methods for health care regulation: rating, screening and surveillance (with Discussion). Journal of Royal Statistical Society Series A (Statistics in Society) 2012; 175: 1-47.

 

1995 Transmissible disease epidemiology: BSE, vCJD, Hepatitis C virus, swine-flu

The Unit’s engagement in transmissible disease epidemiology has ranged from mad cow disease to swine-flu.

Of note is the quantification of the UK’s 1980-96 dietary exposure to the Bovine Spongiform Encelophalopathy (BSE) epidemic in cattle and the implications for clinical and subclinical vCJD (Gore, 1995; Gore, 1996; Cooper and Bird, 2002; Cooper and Bird, 2003; Bird, 2003); the quantification and projections of Hepatitis C Virus (HCV) burden in Scotland and England (Hutchinson et al, 2005; Sweeting et al, 2007, De Angelis et al, 2009); and, more recently, the estimation of the evolution and severity of the H1N1 pandemic (Presanis et al, 2011; Birrell et al, 2011 ).

References

1. Gore SM. More than happenstance: Creutzfeldt-Jakob disease in farmers and young adults. British Medical Journal 1995; 311: 1416-1418.
2. Gore, SM (1996). Bovine Creutzfeldt-Jakob disease? (Editorial). British Medical Journal 1996; 312: 791-793.
3. Cooper JD, Bird SM. Predicting incidence of vCJD from UK dietary exposure for the 1940-69 and post-1969 birth cohorts. International Journal of Epidemiology 2003; 32: 784 – 791.
4. Bird SM. European Union’s rapid TSE testing in adult cattle and sheep: implementation and results in 2001 and 2002. Statistical Methods in Medical Research 2003; 12: 261 – 278.
5. Hutchinson SJ, Bird SM, Goldberg DJ. Modelling the current and future disease burden of hepatitis C among injection drug users in Scotland. Hepatology 2005; 42: 711-723.
6. Sweeting MJ, De Angelis D, Ramsay ME, Brant L, Harris HE. The burden of hepatitis C in England and Wales. Journal of Viral Hepatitis 2007; 14: 570-576.
7. De Angelis D, Sweeting MJ, Ades AE, Hickman M, Hope V, Ramsay M. An evidence synthesis approach to estimating Hepatitis C prevalence in England and Wales. Statistical Methods in Medical Research 2009; 18: 381-395.
8. McDonald SA, Hutchinson SJ, Bird SM, Mills PR, Dillon J, Bloor M, Robertson C, Donaghy M, Hayes P, Graham L, Goldberg DJ. A population-based record linkage study of mortality in hepatitis C diagnosed persons with and without HIV co-infection in Scotland. Statistical Methods in Medical Research 2009; 18: 271 – 283.
9. Presanis A, Pebody RG, Paterson BJ, Tom BM, Birrell PJ, Charlett A, Lipsitch M, De Angelis D. Changes in severity of pandemic (H1N1) influenza in England: a Bayesian evidence synthesis. BMJ 2011; 343: d5408 doi: 10.1136/bmj.d5408.
10. Birrell P, Ketsetzis G, Gay N, Cooper B, Zhang X, Presanis A, Harris R, Charlett A, HPA flu team, De Angelis D. Unmasking the pandemic: a Bayesian reconstruction of influenza A/H1N1pdm dynamics in London. PNAS 2011; 108(45): 18238-18243. doi: 10.1073/pnas.1103002108.

 

1996 Gilks, Richardson and Spiegelhalter: Markov Chain Monte Carlo Methods in Practice.

Markov Chain Monte Carlo Methods in Practice was a crucial early introduction to the powerful computational tools, including adaptive rejection sampling, provided by Monte Carlo Markov chains (MCMC) algorithms. These algorithms have enabled ubiquitous use of Bayesian modelling in practice.

A seminal bestseller, Markov Chain Monte Carlo Methods in Practice covers the basic principles of MCMC theory in a pedagogical manner, and presented examples of the implementation of hierarchical models in a wide range of applications and thereby illustrated the flexibility and power of these approaches.

References

1. Gilks WR, Richardson S, Spiegelhalter DJ (Editors). Markov Chain Monte Carlo Methods in Practice. Chapman & Hall/CRC Press LLC, Florida: 1996.
2. Gilks WR, Best NG, Tan KKC. Adaptive rejection Metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society Series C (Applied Statistics) 1995; 44: 455-472.

 

1998 Database linkage: high risk of drugs-related death soon after prison-release

Our Biostatistical Initiative in support of AIDS/HIV studies in Scotland (MRC-BIAS) used confidential database linkage to investigate how incarceration in Edinburgh Prison affected the morbidity and mortality of Edinburgh’s male HIV infected injectors. We found an 8-fold higher risk of overdose death in the first fortnight after release from Edinburgh Prison in 1983-94 than at comparable other times at liberty – which Bird and Hutchinson corroborated for males aged 15-35 years on release from Scottish prisons in 1996-99.

High risk of drugs-related death soon after prison-release has since been recognised as an international phenomenon which the N-ALIVE Trial’s randomized evaluation of naloxone-on-release for prisoners with a history of heroin injection now seeks to mitigate.

Database linkage studies have also been instrumental in revealing that, for younger injectors (under 35 years of age), being female is associated with a substantially lower drugs-related death-rate than for male injectors; and, in Scotland, enabled the late sequelae of Hepatitis C virus to be quantified.

Historically, Hill and Galloway (Lancet 1949; i: 299-301) used National Health Insurance records to quantify the risk of congenital defects in relation to maternal rubella.

References

1. Seaman SR, Brettle RP, Gore SM. Mortality from overdose among injecting drug users recently released from prison: Database linkage study. British Medical Journal 1998; 316: 426-428.
2. Bird SM, Hutchinson SJ. Male drugs-related deaths in the fortnight after release from prison: Scotland, 1996-99. Addiction 2003; 98: 185-190.
3. Hutchinson SJ, Bird SM, Goldberg DJ. Modelling the current and future disease burden of hepatitis C among injection drug users in Scotland. Hepatology 2005; 42: 711-723.
4. King R, Bird SM, Brooks SP, Hutchinson SJ, Hay G. Prior information in behavioural capture-recapture methods: demographic influences on drug injectors’ propensity to be listed in data sources and their drugs-related mortality. American Journal of Epidemiology 2005; 162: 1-10.

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