Kopec JA, Sayre EC, Flanagan WM, Fines P, Cibere J, Rahman MM, Bansback NJ, Anis AH, Jordan JM, Sobolev B, Aghajanian J, Kang W, Greidanus NV, Garbuz DS, Hawker GA, Badley EM

OBJECTIVES: The purpose of the study was to develop a population-based simulation model of osteoarthritis (OA) in Canada that can be used to quantify the future health and economic burden of OA under a range of scenarios for changes in the OA risk factors and treatments. In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies. DESIGN: We used the Population Health Model (POHEM) platform to develop a stochastic continuous-time microsimulation model of physician-diagnosed OA. Incidence rates were calibrated to agree with administrative data for the province of British Columbia, Canada. The effect of obesity on OA incidence and the impact of OA on health-related quality of life (HRQL) were modeled using Canadian national surveys. RESULTS: Incidence rates of OA in the model increase approximately linearly with age in both sexes between the ages of 50 and 80 and plateau in the very old. In those aged 50+, the rates are substantially higher in women. At baseline, the prevalence of OA is 11.5%, 13.6% in women and 9.3% in men. The OA hazard ratios for obesity are 2.0 in women and 1.7 in men. The effect of OA diagnosis on HRQL, as measured by the Health Utilities Index (HUI3), is to reduce it by 0.10 in women and 0.14 in men. CONCLUSIONS: We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

Osteoarthritis Cartilage. 2009 Oct 23

Nixon RM, O’Hagan A, Oakley J, Madan J, Stevens JW, Bansback N, Brennan A

The development of a new drug is a major undertaking and it is important to consider carefully the key decisions in the development process. Decisions are made in the presence of uncertainty and outcomes such as the probability of successful drug registration depend on the clinical development programmme.The Rheumatoid Arthritis Drug Development Model was developed to support key decisions for drugs in development for the treatment of rheumatoid arthritis. It is configured to simulate Phase 2b and 3 trials based on the efficacy of new drugs at the end of Phase 2a, evidence about the efficacy of existing treatments, and expert opinion regarding key safety criteria.The model evaluates the performance of different development programmes with respect to the duration of disease of the target population, Phase 2b and 3 sample sizes, the dose(s) of the experimental treatment, the choice of comparator, the duration of the Phase 2b clinical trial, the primary efficacy outcome and decision criteria for successfully passing Phases 2b and 3. It uses Bayesian clinical trial simulation to calculate the probability of successful drug registration based on the uncertainty about parameters of interest, thereby providing a more realistic assessment of the likely outcomes of individual trials and sequences of trials for the purpose of decision making.In this case study, the results show that, depending on the trial design, the new treatment has assurances of successful drug registration in the range 0.044-0.142 for an ACR20 outcome and 0.057-0.213 for an ACR50 outcome. Copyright (c) 2009 John Wiley & Sons, Ltd.

Pharm Stat. 2009 Apr 1

Nixon RM, Bansback N, Stevens JW, Brennan A, Madan J

A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration. Copyright 2008 John Wiley & Sons, Ltd.

Pharm Stat. 2009 Apr-Jun;8(2):150-62

Bansback N, Young A, Brennan A, Dixey J

OBJECTIVE: To construct a prognostic algorithm to predict 5-year functional outcome in rheumatoid arthritis (RA), based on the Health Assessment Questionnaire (HAQ). METHODS: Data from all patients with 5-year followup (n = 985) were used from an inception cohort, the Early Rheumatoid Arthritis Study (ERAS). Possibly relevant prognostic factors considered in the initial stage of the model-building process were standard clinical, radiological, and laboratory features measured at baseline and at 1 year. Multivariate analysis was performed using logistic regression, and the predictive performance of the model was tested using measures of discrimination and calibration. RESULTS: Bootstrap resampling identified 6 variables that consistently predicted severe functional outcome. Functional grade III/IV (odds ratio 6.7) and HAQ at 1 year (odds ratio 2.4) were the most important. Other variables included socioeconomic status, hemoglobin, and radiographic and disease activity scores. Estimates of the regression coefficients and performance were corrected for over-fitting. Reasonably large values for the c-index (0.82) and the Nagelkerke R(2) (0.39) indicate that the set of prognostic factors explains the variation in outcome to a degree that implies good prediction for individual patients. CONCLUSION: The algorithm identifies patients in the first year of RA who are likely to have poor function by 5 years and who could potentially benefit from aggressive drug therapy. A nomogram is produced for simple application of the model in clinical practice. While further external validation is necessary, this model could allow clinicians to target aggressive therapy earlier in a patient’s disease course.

J Rheumatol. 2006 Aug;33(8):1503-10

Wired has an interesting post on how Sergey Brin, founder of google, is looking to data mining a new, fast approach to identifying associations with Parkinson’s. They point to a study they have done with 23andme, a company I have been interested in a previous study.

Here is the essential comparison. The rest of the post is here.

Traditional Model
1. Hypothesis: An early study suggests that patients with Gaucher’s disease (caused by a mutation to the GBA gene) might be at increased risk of Parkinson’s.
2. Studies: Researchers conduct further studies, with varying statistical significance.
3. Data aggregation: Sixteen centers pool information on more than 5,500 Parkinson’s patients.
4. Analysis: A statistician crunches the numbers.
5. Writing: A paper is drafted and approved by 64 authors.
6. Submission: The paper is submitted to The New England Journal of Medicine. Peer review ensues.
7. Acceptance: NEJM accepts the paper.
8. Publication: The paper notes that people with Parkinson’s are 5.4 times more likely to carry the GBA mutation.
Total time elapsed: 6 years

Parkinson’s Genetics initiative
1. Tool Construction: Survey designers build the questionnaire that patients will use to report symptoms.
2. Recruitment: The community is announced, with a goal of recruiting 10,000 subjects with Parkinson’s.
3. Data aggregation: Community members get their DNA analyzed. They also fill out surveys.
4. Analysis: Reacting to the NEJM paper, 23andMe researchers run a database query based on 3,200 subjects. The results are returned in 20 minutes.
5. Presentation: The results are reported at a Royal Society of Medicine meeting in London: People with GBA are 5 times more likely to have Parkinson’s, which is squarely in line with the NEJM paper. The finding will possibly be published at a later date.
Total time elapsed: 8 months