Estimating the prevalence of diagnosed Alzheimer disease in England across deprivation groups using electronic health records: a clinical practice research datalink study.

OBJECTIVE: Estimate the prevalence of diagnosed Alzheimer's disease (AD) and early Alzheimer's disease (eAD) overall and stratified by age, sex and deprivation and combinations thereof in England on 1 January 2020. DESIGN: Cross-sectional. SETTING: Primary care electronic health record data, the Clinical Practice Research database linked with secondary care data, Hospital Episode Statistics (HES) and patient-level deprivation data, Index of Multiple Deprivation (IMD). OUTCOME MEASURES: The prevalence per 100 000 of the population and corresponding 95% CIs for both diagnosed AD and eAD overall and stratified by covariates. Sensitivity analyses were conducted to assess the sensitivity of the population definition and look-back period. RESULTS: There were 448 797 patients identified in the Clinical Practice Research Datalink that satisfied the study inclusion criteria and were eligible for HES and IMD linkage. For the main analysis of AD and eAD, 379 763 patients are eligible for inclusion in the denominator. This resulted in an estimated prevalence of diagnosed AD of 378.39 (95% CI, 359.36 to 398.44) per 100 000 and eAD of 292.81 (95% CI, 276.12 to 310.52) per 100 000. Prevalence estimates across main and sensitivity analyses for the entire AD study population were found to vary widely with estimates ranging from 137.48 (95% CI, 127.05 to 148.76) to 796.55 (95% CI, 768.77 to 825.33). There was significant variation in prevalence of diagnosed eAD when assessing the sensitivity with the look-back periods, as low as 120.54 (95% CI, 110.80 to 131.14) per 100 000, and as high as 519.01 (95% CI, 496.64 to 542.37) per 100 000. CONCLUSIONS: The study found relatively consistent patterns of prevalence across both AD and eAD populations. Generally, the prevalence of diagnosed AD increased with age and increased with deprivation for each age category. Women had a higher prevalence than men. More granular levels of stratification reduced patient numbers and increased the uncertainty of point prevalence estimates. Despite this, the study found a relationship between deprivation and prevalence of AD.

Disease severity and mortality in Alzheimer's disease: an analysis using the U.S. National Alzheimer's Coordinating Center Uniform Data Set.

BACKGROUND: Evidence on the relative risk of death across all stages of Alzheimer's disease (AD) is lacking but greatly needed for the evaluation of new interventions. We used data from the Uniform Data Set (UDS) of the National Alzheimer's Coordinating Center (NACC) to assess the expected survival of a person progressing to a particular stage of AD and the relative risk of death for a person in a particular stage of AD compared with cognitively normal (CN) people. METHODS: This was a retrospective observational cohort study of mortality and its determinants in participants with incident mild cognitive impairment (MCI) due to AD or AD dementia compared with CN participants. Overall survival and hazard ratios of all-cause mortality in participants ≥ 50 years of age with clinically assessed or diagnosed MCI due to AD, or mild, moderate, or severe AD dementia, confirmed by Clinical Dementia Rating scores, versus CN participants were estimated, using NACC UDS data. Participants were followed until death, censoring, or until information to determine disease stage was missing. RESULTS: Aged between 50 and 104 years, 12,414 participants met the eligibility criteria for the study. Participants progressing to MCI due to AD or AD dementia survived a median of 3-12 years, with higher mortality observed in more severe stages. Risk of death increased with the severity of AD dementia, with the increase significantly higher at younger ages. Participants with MCI due to AD and CN participants had a similar risk of death after controlling for confounding factors. CONCLUSIONS: Relative all-cause mortality risk increases with AD severity, more so at younger ages. Mortality does not seem to be higher for those remaining in MCI due to AD. Findings might imply potential benefit of lower mortality if preventing or delaying the progression of AD is successful, and importantly, this potential benefit might be greater in relatively younger people. Future research should replicate our study in other samples more representative of the general US population as well as other populations around the world.

Real-World Use of Symptomatic Treatments in Early Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, causing progressive decline of memory, thinking, and behavior, impairing daily functioning. Early AD (eAD) includes mild cognitive impairment (MCI) due to AD and mild AD dementia. OBJECTIVE: The aim of this study was to investigate symptomatic treatment prevalence and treatment patterns in eAD. METHODS: Embase, MEDLINE, and EBM Reviews were searched in November 2021 for observational studies reporting symptomatic treatment patterns in eAD. The range of patients receiving treatment was collated. Risk of bias was assessed using the Joanna Briggs Institute (JBI) prevalence tool. Two independent reviewers screened the records, one performed data extraction and quality assessment while a second checked. RESULTS: Twenty-one studies (prospective and retrospective cohorts, cross-sectional studies, and a survey) were included. Population size ranged from 23 to 2,028. Worldwide, 18 to 35% of patients diagnosed with MCI due to AD received any AChE inhibitor (three studies; n = 631), 7 to 8% memantine (two studies; n = 229), and 9% combination therapy (one study; n = 402). Patients receiving no treatment ranged from 41 to 54% (two studies; n = 733). Worldwide, in mild AD dementia patients, 13 to 89% received any AChE inhibitor (six studies; n = 3,715), 1 to 21% memantine (five studies, n = 3,527), and 0.4 to 39% combination therapy (four studies, n = 3,018). Patients receiving no treatment ranged from 9 to 26% (five studies, n = 4,073). CONCLUSION: Limitations in reporting led to unclear risk of bias. The results reveal a pattern of use of symptomatic treatment in eAD beyond approved labels and highlights the opportunity for new consensus guidelines to inform clinical practice.

Bayesian network meta-analysis methods for combining individual participant data and aggregate data from single arm trials and randomised controlled trials.

BACKGROUND: Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome. METHODS: We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset. RESULTS: Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates. CONCLUSIONS: Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail.

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.

Nonproportional 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.

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.

Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis.

BACKGROUND: Decision makers often need to assess the real-world effectiveness of new drugs prelaunch, when phase II/III randomized controlled trials (RCTs) but no other data are available. OBJECTIVE: To develop a method to predict drug effectiveness prelaunch and to apply it in a case study in rheumatoid arthritis (RA). METHODS: The approach 1) identifies a market-approved treatment ( S) currently used in a target population similar to that of the new drug ( N); 2) quantifies the impact of treatment, prognostic factors, and effect modifiers on clinical outcome; 3) determines the characteristics of patients likely to receive N in routine care; and 4) predicts treatment outcome in simulated patients with these characteristics. Sources of evidence include expert opinion, RCTs, and observational studies. The framework relies on generalized linear models. RESULTS: The case study assessed the effectiveness of tocilizumab (TCZ), a biologic disease-modifying antirheumatic drug (DMARD), combined with conventional DMARDs, compared to conventional DMARDs alone. Rituximab (RTX) combined with conventional DMARDs was identified as treatment S. Individual participant data from 2 RCTs and 2 national registries were analyzed. The model predicted the 6-month changes in the Disease Activity Score 28 (DAS28) accurately: the mean change was -2.101 (standard deviation [SD] = 1.494) in the simulated patients receiving TCZ and conventional DMARDs compared to -1.873 (SD = 1.220) in retrospectively assessed observational data. It was -0.792 (SD = 1.499) in registry patients treated with conventional DMARDs. CONCLUSION: The approach performed well in the RA case study, but further work is required to better define its strengths and limitations.

Systematic Review and Network Meta-analysis of Idiopathic Pulmonary Fibrosis Treatments.

BACKGROUND: The antifibrotics pirfenidone and nintedanib are both approved for the treatment of idiopathic pulmonary fibrosis (IPF) by regulatory agencies and are recommended by health technology assessment bodies. Other treatments such as N-acetylcysteine are used in clinical practice but have not received regulatory approval. No head-to-head trials have been conducted to directly compare the efficacy of these therapies in IPF. OBJECTIVE: To compare the efficacy of treatments for IPF. METHODS: A systematic review was conducted up to April 2015. Phase II/III randomized controlled trials in adults with IPF were eligible. A Bayesian network meta-analysis (NMA) was used to compare pirfenidone, nintedanib, and N-acetylcysteine with respect to forced vital capacity (FVC) and mortality. RESULTS: Nine studies were included in the NMA. For change from baseline in FVC, the NMA indicated that pirfenidone and nintedanib were more effective than placebo after 1 year (pirfenidone vs. placebo: difference = 0.12 liter (L), 95% credible interval [CrI] = 0.03-0.21 L; nintedanib vs. placebo: difference = 0.11 L, 95% CrI = 0.00-0.22 L). There was no evidence that N-acetylcysteine had an effect on FVC compared with placebo (N-acetylcysteine vs. placebo: difference = 0.01 L, 95% CrI = -0.15-0.17 L). Patients treated with pirfenidone also had a lower risk of experiencing a decline in percent predicted FVC of ≥ 10% over 1 year (odds ratio [OR]: 0.58, 95% CrI = 0.40-0.88), whereas there was no conclusive evidence of a difference between nintedanib and placebo (OR: 0.65, 95% CrI = 0.42-1.02). The NMA indicated that pirfenidone reduced all-cause mortality relative to placebo over 1 year (hazard ratio [HR]: 0.52, 95% CrI = 0.28-0.92). There was no evidence of a difference in all-cause mortality between nintedanib and placebo (HR: 0.70, 95% CrI = 0.32-1.55), or N-acetylcysteine and placebo (HR: 2.00, 95% CrI=0.46-8.62). CONCLUSIONS: Our primary analysis of the available evidence indicates that over 1 year, pirfenidone and nintedanib are effective at reducing lung-function decline, and pirfenidone may reduce the odds of experiencing a decline in percent predicted FVC of ≥10% compared with placebo in the first year of treatment. The results of our analysis also suggest that pirfenidone improves survival. DISCLOSURES: Fleetwood is an employee of Quantics Consulting. McCool and Glanville are employees of York Health Economics Consortium (YHEC). Quantics and YHEC received funding from F. Hoffmann-La Roche for conducting the systematic review and network meta-analysis reported in this paper. Edwards, Gsteiger, and Daigl are employees of F. Hoffmann-La Roche. Fisher was employed by InterMune UK, a wholly owned Roche subsidiary, until July 2015. He is currently employed by FIECON, which has received funding from F. Hoffmann-La Roche for consulting services. The systematic review and network meta-analysis reported in this paper were conducted by Fleetwood (Quantics Consulting) and McCool and Glanville (YHEC), funded by F. Hoffmann-La Roche. The original network analysis was funded by InterMune. Study concept and design were contributed by Edwards, Gsteiger, and Daigl, along with Fleetwood, McCool, and Glanville. Fleetwood, McCool, and Glanville collected the data, with assistance from Edwards, Gsteiger, and Daigl. Data interpretation was performed by Fleetwood and Fisher, with assistance from the other authors. The manuscript was written by Fleetwood, McCool, and Glanville, with assistance from Edwards, Daigl, and Fisher, and revised by all the authors.

Effects of Renal Impairment on Steady-State Plasma Concentrations of Rivastigmine: A Population Pharmacokinetic Analysis of Capsule and Patch Formulations in Patients with Alzheimer's Disease.

INTRODUCTION: The glomerular filtration rate (GFR), a measure of renal function, decreases by approximately 10 mL/min every 10 years after the age of 40 years, which could lead to the accumulation of drugs and/or renal toxicity. Pharmacokinetic studies of drugs excreted both renally and non-renally are desirable in patients with impaired renal function, defined by parameters including estimated GFR (eGFR) and creatinine clearance (CLCR). OBJECTIVE: We describe here a population pharmacokinetic analysis of the possible effects of renal impairment on steady-state plasma concentrations of rivastigmine and its metabolite NAP226-90 after rivastigmine patch (5 cm2 [4.6 mg/24 h], 10 cm2 [9.5 mg/24 h], 15 cm2 [13.3 mg/24 h], and 20 cm2 [17.4 mg/24 h]) and capsule (1.5, 3, 4.5, and 6 mg/12 h) treatment in patients with Alzheimer's disease. METHODS: The data used to conduct the current pharmacokinetic analysis were obtained from the pivotal phase III, 24-week, multicenter, randomized, double-blind, placebo- and active-controlled, parallel-group study (IDEAL). One blood sample was collected from each patient at steady-state to measure plasma concentrations of rivastigmine and NAP226-90 using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. The steady-state plasma concentrations of rivastigmine and NAP226-90 were plotted against CLCR and eGFR data, and boxplots were constructed after stratification by renal function. RESULTS: The two groups (mild/no renal impairment vs. moderate/severe/end-stage renal impairment) showed comparable demographic covariates for all patch sizes and capsule doses. No correlation was observed between CLCR or eGFR and plasma concentrations of rivastigmine or NAP226-90. Boxplots of concentrations of rivastigmine or NAP226-90 for each dose largely overlapped for patch and capsule. Additionally, model-based estimates of plasma concentrations adjusted for body weight yielded similar results. CONCLUSION: The results of this study show that renal function does not affect rivastigmine or NAP226-90 steady-state plasma concentrations, and no dose adjustment in patients with renal impairment is required. CLINICALTRIALS.GOV: NCT00099242.

GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi-state models, discrete event simulation models, physiology-based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real-world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

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.

Assessing switchability for biosimilar products: modelling approaches applied to children's growth.

The present paper describes two statistical modelling approaches that have been developed to demonstrate switchability from the original recombinant human growth hormone (rhGH) formulation (Genotropin(®) ) to a biosimilar product (Omnitrope(®) ) in children suffering from growth hormone deficiency. Demonstrating switchability between rhGH products is challenging because the process of growth varies with the age of the child and across children. The first modelling approach aims at predicting individual height measured at several time-points after switching to the biosimilar. The second modelling approach provides an estimate of the deviation from the overall growth rate after switching to the biosimilar, which can be regarded as an estimate of switchability. The results after applying these approaches to data from a randomized clinical trial are presented. The accuracy and precision of the predictions made using the first approach and the small deviation from switchability estimated with the second approach provide sufficient evidence to conclude that switching from Genotropin(®) to Omnitrope(®) has a very small effect on growth, which is neither statistically significant nor clinically relevant.

Robust meta-analytic-predictive priors in clinical trials with historical control information.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Anti-interleukin-17A monoclonal antibody secukinumab in treatment of ankylosing spondylitis: a randomised, double-blind, placebo-controlled trial.

BACKGROUND: Ankylosing spondylitis is a chronic immune-mediated inflammatory disease characterised by spinal inflammation, progressive spinal rigidity, and peripheral arthritis. Interleukin 17 (IL-17) is thought to be a key inflammatory cytokine in the development of ankylosing spondylitis, the prototypical form of spondyloarthritis. We assessed the efficacy and safety of the anti-IL-17A monoclonal antibody secukinumab in treating patients with active ankylosing spondylitis. METHODS: We did a randomised double-blind proof-of-concept study at eight centres in Europe (four in Germany, two in the Netherlands, and two in the UK). Patients aged 18-65 years were randomly assigned (in a 4:1 ratio) to either intravenous secukinumab (2×10 mg/kg) or placebo, given 3 weeks apart. Randomisation was done with a computer-generated block randomisation list without a stratification process. The primary efficacy endpoint was the percentage of patients with a 20% response according to the Assessment of SpondyloArthritis international Society criteria for improvement (ASAS20) at week 6 (Bayesian analysis). Safety was assessed up to week 28. This study is registered with ClinicalTrials.gov, number NCT00809159. FINDINGS: 37 patients with moderate-to-severe ankylosing spondylitis were screened, and 30 were randomly assigned to receive either intravenous secukinumab (n=24) or placebo (n=6). The final efficacy analysis included 23 patients receiving secukinumab and six patients receiving placebo, and the safety analysis included all 30 patients. At week 6, ASAS20 response estimates were 59% on secukinumab versus 24% on placebo (99·8% probability that secukinumab is superior to placebo). One serious adverse event (subcutaneous abscess caused by Staphylococcus aureus) occurred in the secukinumab-treated group. INTERPRETATION: Secukinumab rapidly reduced clinical or biological signs of active ankylosing spondylitis and was well tolerated. It is the first targeted therapy that we know of that is an alternative to tumour necrosis factor inhibition to reach its primary endpoint in a phase 2 trial. FUNDING: Novartis.

Using historical control information for the design and analysis of clinical trials with overdispersed count data.

Results from clinical trials are never interpreted in isolation. Previous studies in a similar setting provide valuable information for designing a new trial. For the analysis, however, the use of trial-external information is challenging and therefore controversial, although it seems attractive from an ethical or efficiency perspective. Here, we consider the formal use of historical control data on lesion counts in a multiple sclerosis trial. The approach to incorporating historical data is Bayesian, in that historical information is captured in a prior that accounts for between-trial variability and hence leads to discounting of historical data. We extend the meta-analytic-predictive approach, a random-effects meta-analysis of historical data combined with the prediction of the parameter in the new trial, from normal to overdispersed count data of individual-patient or aggregate-trial format. We discuss the prior derivation for the lesion mean count in the control group of the new trial for two populations. For the general population (without baseline enrichment), with 1936 control patients from nine historical trials, between-trial variability was moderate to substantial, leading to a prior effective sample size of about 45 control patients. For the more homogenous population (with enrichment), with 412 control patients from five historical trials, the prior effective sample size was approximately 63 patients. Although these numbers are small relative to the historical data, they are fairly typical in settings where between-trial heterogeneity is moderate. For phase II, reducing the number of control patients by 45 or by 63 may be an attractive option in many multiple sclerosis trials.

Model-based analyses of bioequivalence crossover trials using the stochastic approximation expectation maximisation algorithm.

In this work, we develop a bioequivalence analysis using nonlinear mixed effects models (NLMEM) that mimics the standard noncompartmental analysis (NCA). We estimate NLMEM parameters, including between-subject and within-subject variability and treatment, period and sequence effects. We explain how to perform a Wald test on a secondary parameter, and we propose an extension of the likelihood ratio test for bioequivalence. We compare these NLMEM-based bioequivalence tests with standard NCA-based tests. We evaluate by simulation the NCA and NLMEM estimates and the type I error of the bioequivalence tests. For NLMEM, we use the stochastic approximation expectation maximisation (SAEM) algorithm implemented in monolix. We simulate crossover trials under H(0) using different numbers of subjects and of samples per subject. We simulate with different settings for between-subject and within-subject variability and for the residual error variance. The simulation study illustrates the accuracy of NLMEM-based geometric means estimated with the SAEM algorithm, whereas the NCA estimates are biased for sparse design. NCA-based bioequivalence tests show good type I error except for high variability. For a rich design, type I errors of NLMEM-based bioequivalence tests (Wald test and likelihood ratio test) do not differ from the nominal level of 5%. Type I errors are inflated for sparse design. We apply the bioequivalence Wald test based on NCA and NLMEM estimates to a three-way crossover trial, showing that Omnitrope®; (Sandoz GmbH, Kundl, Austria) powder and solution are bioequivalent to Genotropin®; (Pfizer Pharma GmbH, Karlsruhe, Germany). NLMEM-based bioequivalence tests are an alternative to standard NCA-based tests. However, caution is needed for small sample size and highly variable drug.

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.

PURPOSE: 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.

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.