I am a biostatistician with primary research interests in developing and applying statistical methods for population-based cancer survival analysis, particularly the estimation and modeling of relative survival. Current research, in collaboration with Professor Paul Lambert (University of Leicester), focusses on the development and application of methods for presenting patient survival that are relevant for patients, clinicians, and the general public. I also have general interests in epidemiology, particularly cancer epidemiology, and methods for register-based research.
- 1990, BMath, University of Newcastle, Australia.
- 1992, BMath (honours class 1), Statistics, University of Newcastle, Australia.
- 1997, PhD, Department of Statistics, University of Newcastle, Australia.
- 1998, Postdoc (Cancer Epidemiology), Karolinska Institutet.
- 2002, Docent (Biostatistics), Karolinska Institutet.
Research in the area of cancer patient survival
Patient survival is the most important single measure of cancer patient care (the diagnosis and treatment of cancer) and together with incidence and mortality is one of the key measures of cancer control. The optimal method for monitoring and evaluating the effectiveness of cancer patient care is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries (Dickman and Adami, 2006). It is standard in population-based studies to use relative survival as the measure of cancer patient survival. Relative survival is the ratio of observed (all-cause) to expected survival proportion and provides a measure of excess mortality associated with diagnosis of cancer. Excess mortality is the difference between the observed (all-cause) mortality and the mortality that would have been expected if the patients were not diagnosed with cancer. It has the advantage that cause of death information is not required and that it captures mortality both directly due to the cancer as well as indirectly due to the cancer (e.g., increased risk of non-cancer mortality caused by the treatment). In cancer clinical trials it is standard to estimate cause-specific survival but this is less frequently used in population-based studies since information on cause of death is not as accurate as it is in clinical trials.
I have contributed to several developments in methodology for estimating and modelling relative survival. Our 2004 paper on modelling relative survival has been cited over 400 times and the model proposed therein is one of the most commonly applied models for relative survival. In recent years I have collaborated closely with Prof. Paul Lambert from the University of Leicester; we have developed and applied cure models for relative survival and methods for estimating the probability of death due to cancer in the presence of competing risks. We have investigated the assumptions underlying relative survival and evaluated approaches to predicting cancer patient survival. We have also developed methods for partitioning excess mortality that have been applied to estimating treatment-related mortality among patients with Hodgkin lymphoma. We have developed freely-available user-friendly software (primarily Stata but also SAS) to implement our methods and hold courses each year (http://cansurv.net/) to train cancer researchers working in the area. I collaborate closely with clinicians; all of my methods are developed to address relevant clinical questions. We typically publish details of the new methods in statistical methods journals and applications of the methods in clinical journals.
Estimating the proportion cured of cancer: some practical advice for users.
Cancer Epidemiol 2013 Dec;37(6):836-42
Temporal trends in mortality from diseases of the circulatory system after treatment for Hodgkin lymphoma: a population-based cohort study in Sweden (1973 to 2006).
J. Clin. Oncol. 2013 Apr;31(11):1435-41
Stage at diagnosis and mortality in women with pregnancy-associated breast cancer (PABC).
Breast Cancer Res. Treat. 2013 May;139(1):183-92
Estimating the loss in expectation of life due to cancer using flexible parametric survival models.
Stat Med 2013 Dec;32(30):5286-300
Estimating net survival in population-based cancer studies.
Int. J. Cancer 2013 Jul;133(2):519-21
How can we make cancer survival statistics more useful for patients and clinicians: an illustration using localized prostate cancer in Sweden.
Cancer Causes Control 2013 Mar;24(3):505-15
Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models.
BMC Med Res Methodol 2012 Jun;12():86
Predicting the survival of cancer patients recently diagnosed in Sweden and an evaluation of predictions published in 2004.
Acta Oncol 2012 Jan;51(1):17-27
Success story of targeted therapy in chronic myeloid leukemia: a population-based study of patients diagnosed in Sweden from 1973 to 2008.
J. Clin. Oncol. 2011 Jun;29(18):2514-20
Estimating the crude probability of death due to cancer and other causes using relative survival models.
Stat Med 2010 Mar;29(7-8):885-95
Temporal trends in the proportion cured among adults diagnosed with acute myeloid leukaemia in Sweden 1973-2001, a population-based study.
Br. J. Haematol. 2010 Mar;148(6):918-24
Temporal trends in the proportion cured for cancer of the colon and rectum: a population-based study using data from the Finnish Cancer Registry.
Int. J. Cancer 2007 Nov;121(9):2052-9
Interpreting trends in cancer patient survival.
J. Intern. Med. 2006 Aug;260(2):103-17
Cancer patient survival in Sweden at the beginning of the third millennium--predictions using period analysis.
Cancer Causes Control 2004 Nov;15(9):967-76
Regression models for relative survival.
Stat Med 2004 Jan;23(1):51-64
Natural history of early, localized prostate cancer.
JAMA 2004 Jun;291(22):2713-9
Research in cancer epidemiology (other than patient survival)
My primary training is in statistics and my primary research interests are in the development and application of statistical methods for cancer patient survival. However, I also have a strong interest in cancer epidemiology in general and consider myself as much a cancer epidemiologist as a biostatistician.
Serum calcium and the risk of prostate cancer.
Cancer Causes Control 2009 Sep;20(7):1205-14
Hip fractures in men with prostate cancer treated with orchiectomy.
J. Urol. 2004 Dec;172(6 Pt 1):2208-12
Thyroid cancer risk after thyroid examination with 131I: a population-based cohort study in Sweden.
Int. J. Cancer 2003 Sep;106(4):580-7
Incidence and survival of Swedish patients with differentiated thyroid cancer.
Int. J. Cancer 2003 Sep;106(4):569-73
Quality of life after radical prostatectomy or watchful waiting.
N. Engl. J. Med. 2002 Sep;347(11):790-6
Research in perinatal and reproductive epidemiology
Maternal smoking and infant mortality: does quitting smoking reduce the risk of infant death?
Epidemiology 2009 Jul;20(4):590-7
Previous preterm and small-for-gestational-age births and the subsequent risk of stillbirth.
N. Engl. J. Med. 2004 Feb;350(8):777-85
Maternal hemoglobin concentration during pregnancy and risk of stillbirth.
Time of birth and risk of intrapartum and early neonatal death.
Epidemiology 2003 Mar;14(2):218-22
I am an active and enthusiastic teacher. I particularly enjoy teaching courses in the analysis of cancer patient survival to cancer researchers since I learn so much from discussions with the participants. Details of my teaching can be found on my personal web page.
From 2005-2007 I was director of postgraduate studies and member of the working group of the postgraduate program in epidemiology and continue to teach postgraduate courses at KI.
Together with Paul Lambert, I teach a 1-week course on statistical methods for population-based cancer survival analysis each June in Veneto, Italy. Dates for 2014 are June 16-21.
From 2006-2010 I was responsible for coordinating all undergraduate education at MEB (grundutbildningsansvarig) and heavily involved in teaching in the KI medical program, where I was chair of chair the group that had an overall responsibility for coordinating scientific development within the program including specific responsibility for 5 weeks coursework integrated throughout the program and a 20 week project.
Current supervision of PhD students