Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process. In a first step, a stem cell is transformed via a series of mutations into an intermediate cell having a growth advantage. Under favorable conditions, such a cell will give rise to a clone of initiated cells. Eventually, further alterations may transform a cell out of this clone into a malignant tumor cell. A mechanistic model of this process is given by the widely used two-stage clonal expansion model (TSCE). In this thesis, we take up a generalization of the TSCE, and study, how to introduce the concept of population heterogeneity into the model. We use mixture modeling, which allows to describe frailty in a biologically meaningful way. In a first part, we focus on theoretical properties of the extended model. Especially identifiability is discussed extensively. In a second part, we fit the model to human cancer incidence data. We analyze a situation, in which maximum likelihood estimation fails, and describe alternatives for statistical inference. The applications show that good fits are achieved only when the mixing distribution separates the population clearly into a large, virtually immune group, and into a small, high risk group

Heterogeneity in District-Level Transmission of Ebola Virus Disease during the 2013-2015 Epidemic in West Africa.

The Ebola virus disease (EVD) epidemic in West Africa in 2013-2015 spread heterogeneously across the three hardest-hit countries Guinea, Liberia and Sierra Leone and the estimation of national transmission of EVD provides little information about local dynamics. To investigate district-level transmissibility of EVD, we applied a statistical modelling approach to estimate the basic reproduction number (R0) for each affected district and each country using weekly incident case numbers. We estimated growth rates during the early exponential phase of the outbreak using exponential regression of the case counts on the first eight weeks since onset. To take into account the heterogeneity between and within countries, we fitted a mixed effects model and calculated R0 based on the predicted individual growth rates and the reported serial interval distribution. At district level, R0 ranged from 0.36 (Dubréka) to 1.72 (Beyla) in Guinea, from 0.53 (Maryland) to 3.37 (Margibi) in Liberia and from 1.14 (Koinadugu) to 2.73 (Western Rural) in Sierra Leone. At national level, we estimated an R0 of 0.97 (95% CI 0.77-1.18) for Guinea, 1.26 (95% CI 0.98-1.55) for Liberia and 1.66 (95% CI 1.32-2.00) for Sierra Leone. Socio-demographic variables related to urbanisation such as high population density and high wealth index were found positively associated with R0 suggesting that the consequences of fast urban growth in West Africa may have contributed to the increased spread of EVD

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain

Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune

Oral abstracts 1: SpondyloarthropathiesO1. Detecting axial spondyloarthritis amongst primary care back pain referrals

Background: Inflammatory back pain (IBP) is an early feature of ankylosing spondylitis (AS) and its detection offers the prospect of early diagnosis of AS. However, since back pain is very common but only a very small minority of back pain sufferers have ASpA or AS, screening of back pain sufferers for AS is problematic. In early disease radiographs are often normal so that fulfilment of diagnostic criteria for AS is impossible though a diagnosis of axial SpA can be made if MRI evidence of sacroiliitis is present. This pilot study was designed to indicate whether a cost-effective pick up rate for ASpA/early AS could be achieved by identifying adults with IBP stratified on the basis of age. Methods: Patients aged between 18 and 45 years who were referred to a hospital physiotherapy service with back pain of more than 3 months duration were assessed for IBP. All were asked to complete a questionnaire based on the Berlin IBP criteria. Those who fulfilled IBP criteria were also asked to complete a second short questionnaire enquiring about SpA comorbidities, to have a blood test for HLA-B27 and CRP level and to undergo an MRI scan of the sacroiliac joints. This was a limited scan, using STIR, diffusion-weighted, T1 and T2 sequences of the sacroiliac joints to minimize time in the scanner and cost. The study was funded by a research grant from Abbott Laboratories Ltd. Results: 50 sequential patients agreed to participate in the study and completed the IBP questionnaire. Of these 27 (54%) fulfilled criteria for IBP. Of these, 2 patients reported a history of an SpA comorbidity - 1 psoriasis; 1 ulcerative colitis - and 3 reported a family history of an SpA comorbidity - 2 psoriasis; 1 Crohn's disease. 4 were HLA-B27 positive, though results were not available for 7. Two patients had marginally raised CRP levels (6, 10 -NR ≤ 5). 19 agreed to undergo MRI scanning of the sacroiliac joints and lumbar spine; 4 scans were abnormal, showing evidence of bilateral sacroiliitis on STIR sequences. In all cases the changes met ASAS criteria but were limited. Of these 4 patients 3 were HLA-B27 positive but none gave a personal or family history of an SpA-associated comorbidity and all had normal CRP levels. Conclusions: This was a pilot study yielding only limited conclusions. However, it is clear that: Screening of patients referred for physiotherapy for IBP is straightforward, inexpensive and quick. It appears that IBP is more prevalent in young adults than overall population data suggest so that targeting this population may be efficient. IBP questionnaires could be administered routinely during a physiotherapy assessment. HLA-B27 testing in this group of patients with IBP is a suitable screening tool. The sacroiliac joint changes identified were mild and their prognostic significance is not yet clear so that the value of early screening needs further evaluation. Disclosure statement: C.H. received research funding for this study from Abbott. A.K. received research funding for this study, and speaker and consultancy fees, from Abbott. All other authors have declared no conflicts of interes

BHPR research: qualitative1. Complex reasoning determines patients' perception of outcome following foot surgery in rheumatoid arhtritis

Background: Foot surgery is common in patients with RA but research into surgical outcomes is limited and conceptually flawed as current outcome measures lack face validity: to date no one has asked patients what is important to them. This study aimed to determine which factors are important to patients when evaluating the success of foot surgery in RA Methods: Semi structured interviews of RA patients who had undergone foot surgery were conducted and transcribed verbatim. Thematic analysis of interviews was conducted to explore issues that were important to patients. Results: 11 RA patients (9 ♂, mean age 59, dis dur = 22yrs, mean of 3 yrs post op) with mixed experiences of foot surgery were interviewed. Patients interpreted outcome in respect to a multitude of factors, frequently positive change in one aspect contrasted with negative opinions about another. Overall, four major themes emerged. Function: Functional ability & participation in valued activities were very important to patients. Walking ability was a key concern but patients interpreted levels of activity in light of other aspects of their disease, reflecting on change in functional ability more than overall level. Positive feelings of improved mobility were often moderated by negative self perception ("I mean, I still walk like a waddling duck”). Appearance: Appearance was important to almost all patients but perhaps the most complex theme of all. Physical appearance, foot shape, and footwear were closely interlinked, yet patients saw these as distinct separate concepts. Patients need to legitimize these feelings was clear and they frequently entered into a defensive repertoire ("it's not cosmetic surgery; it's something that's more important than that, you know?”). Clinician opinion: Surgeons' post operative evaluation of the procedure was very influential. The impact of this appraisal continued to affect patients' lasting impression irrespective of how the outcome compared to their initial goals ("when he'd done it ... he said that hasn't worked as good as he'd wanted to ... but the pain has gone”). Pain: Whilst pain was important to almost all patients, it appeared to be less important than the other themes. Pain was predominately raised when it influenced other themes, such as function; many still felt the need to legitimize their foot pain in order for health professionals to take it seriously ("in the end I went to my GP because it had happened a few times and I went to an orthopaedic surgeon who was quite dismissive of it, it was like what are you complaining about”). Conclusions: Patients interpret the outcome of foot surgery using a multitude of interrelated factors, particularly functional ability, appearance and surgeons' appraisal of the procedure. While pain was often noted, this appeared less important than other factors in the overall outcome of the surgery. Future research into foot surgery should incorporate the complexity of how patients determine their outcome Disclosure statement: All authors have declared no conflicts of interes

Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity.

Objectives Gini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time. Methods We used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999-2001; Natsal-3: 2010-2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients. Results Gini coefficients for CT and MG were 0.33 (95% CI [0.18-0.49]) and 0.16 (95% CI [0.02-0.36]), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15 to 0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient. Conclusions Gini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies

Bioequivalence tests based on individual estimates using non-compartmental or model-based analyses: evaluation of estimates of sample means and type I error for different designs.

International audiencePURPOSE: The main objective of this work is to compare the standard bioequivalence tests based on individual estimates of the area under the curve and the maximal concentration obtained by non-compartmental analysis (NCA) to those based on individual empirical Bayes estimates (EBE) obtained by nonlinear mixed effects models. METHODS: We evaluate by simulation the precision of sample means estimates and the type I error of bioequivalence tests for both approaches. Crossover trials are simulated under H ( 0 ) using different numbers of subjects (N) and of samples per subject (n). We simulate concentration-time profiles with different variability settings for the between-subject and within-subject variabilities and for the variance of the residual error. RESULTS: Bioequivalence tests based on NCA show satisfactory properties with low and high variabilities, except when the residual error is high, which leads to a very poor type I error, or when n is small, which leads to biased estimates. Tests based on EBE lead to an increase of the type I error, when the shrinkage is above 20%, which occurs notably when NCA fails. CONCLUSIONS: For small n or data with high residual error, tests based on a global data analysis should be considered instead of those based on individual estimates

Non-proportional hazards in network meta-analysis: efficient strategies for model building and analysis

Objectives: To develop efficient approaches for fitting network meta-analysis (NMA) models with time-varying hazard ratios (such as fractional polynomials and piecewise constant models) to allow practitioners to investigate a broad range of models rapidly and to achieve a more robust and comprehensive model selection strategy. Methods: We reformulated the fractional polynomial and piecewise constant NMA models using analysis of variance–like parameterization. With this approach, both models are expressed as generalized linear models (GLMs) with time-varying covariates. Such models can be fitted efficiently with standard frequentist techniques. We applied our approach to the example data from the study by Jansen et al, in which fractional polynomial NMA models were introduced. Results: Fitting frequentist fixed-effect NMAs for a large initial set of candidate models took less than 1 second with standard GLM routines. This allowed for model selection from a large range of hazard ratio structures by comparing a set of criteria including Akaike information criterion/Bayesian information criterion, visual inspection of goodness-of-fit, and long-term extrapolations. The “best” models were then refitted in a Bayesian framework. Estimates agreed very closely. Conclusions: NMA models with time-varying hazard ratios can be explored efficiently with a stepwise approach. A frequentist fixed-effect framework enables rapid exploration of different models. The best model can then be assessed further in a Bayesian framework to capture and propagate uncertainty for decision-making