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1.
Res Synth Methods ; 2024 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-38772906

RESUMO

BACKGROUND: Traditionally, meta-analysis of time-to-event outcomes reports a single pooled hazard ratio assuming proportional hazards (PH). For health technology assessment evaluations, hazard ratios are frequently extrapolated across a lifetime horizon. However, when treatment effects vary over time, an assumption of PH is not always valid. The Royston-Parmar (RP), piecewise exponential (PE), and fractional polynomial (FP) models can accommodate non-PH and provide plausible extrapolations of survival curves beyond observed data. METHODS: Simulation study to assess and compare the performance of RP, PE, and FP models in a Bayesian framework estimating restricted mean survival time difference (RMSTD) at 50 years from a pairwise meta-analysis with evidence of non-PH. Individual patient data were generated from a mixture Weibull distribution. Twelve scenarios were considered varying the amount of follow-up data, number of trials in a meta-analysis, non-PH interaction coefficient, and prior distributions. Performance was assessed through bias and mean squared error. Models were applied to a metastatic breast cancer example. RESULTS: FP models performed best when the non-PH interaction coefficient was 0.2. RP models performed best in scenarios with complete follow-up data. PE models performed well on average across all scenarios. In the metastatic breast cancer example, RMSTD at 50-years ranged from -14.6 to 8.48 months. CONCLUSIONS: Synthesis of time-to-event outcomes and estimation of RMSTD in the presence of non-PH can be challenging and computationally intensive. Different approaches make different assumptions regarding extrapolation and sensitivity analyses varying key assumptions are essential to check the robustness of conclusions to different assumptions for the underlying survival function.

2.
Stat Med ; 43(1): 184-200, 2024 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-37932874

RESUMO

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay.


Assuntos
Fragilidade , Modelos Estatísticos , Humanos , Doenças Raras/epidemiologia , Simulação por Computador , Software
3.
Brain Behav ; 13(12): e3331, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-37957895

RESUMO

BACKGROUND: Duchenne muscular dystrophy (DMD) is a rare, muscle-degenerative disease predominantly affecting males. Natural history models capture the full disease pathway under current care and combine with estimates of new interventions' effects to assess cost-effectiveness by health technology decision-makers. These models require mortality estimates throughout a patient's lifetime, but rare disease datasets typically contain relatively few patients with short follow-ups. Alternative (published) sources of mortality data may therefore be required. METHODS: The Clinical Practice Research Datalink (CPRD) was evaluated as a source of mortality and natural history data for future economic evaluations of health technologies for DMD and rare diseases in general in the UK population. This retrospective longitudinal cohort study provides flexible parametric estimates of mortality rates and survival probabilities in the current UK DMD population through primary/secondary records in the CPRD since 1990. It also investigates clinically significant milestones such as corticosteroid use, spinal surgery, and cardiomyopathy in these patients. RESULTS: A total of 1121 male patients were included in the study, observed from 0.7 to 48.9 years. Median life expectancy was 25.64 years (95% confidence interval 24.73, 26.47), consistent with previous global estimates. This has improved to 26.47 (25.16, 27.89) years in patients born after 1990. The median ages at corticosteroid initiation, spinal surgery, ventilation, and cardiomyopathy diagnosis were 6.06 years (5.77, 6.29), 14.79 years (14.29, 15.09), 16.97 years (16.50, 18.31), and 15.26 years (14.22, 16.70), respectively. CONCLUSIONS: Estimates of mortality in UK-based DMD patients are age-specific in a uniquely large and nationally representative sample from the CPRD.


Assuntos
Cardiomiopatias , Distrofia Muscular de Duchenne , Humanos , Masculino , Distrofia Muscular de Duchenne/epidemiologia , Distrofia Muscular de Duchenne/terapia , Estudos Retrospectivos , Estudos Longitudinais , Corticosteroides , Cardiomiopatias/complicações , Reino Unido/epidemiologia
4.
BMC Med Res Methodol ; 23(1): 87, 2023 04 10.
Artigo em Inglês | MEDLINE | ID: mdl-37038100

RESUMO

BACKGROUND: Multi-state models are used to study several clinically meaningful research questions. Depending on the research question of interest and the information contained in the data, different multi-state structures and modelling choices can be applied. We aim to explore different research questions using a series of multi-state models of increasing complexity when studying repeated prescriptions data, while also evaluating different modelling choices. METHODS: We develop a series of research questions regarding the probability of being under antidepressant medication across time using multi-state models, among Swedish women diagnosed with breast cancer (n = 18,313) and an age-matched population comparison group of cancer-free women (n = 92,454) using a register-based database (Breast Cancer Data Base Sweden 2.0). Research questions were formulated ranging from simple to more composite ones. Depending on the research question, multi-state models were built with structures ranging from simpler ones, like single-event survival analysis and competing risks, up to complex bidirectional and recurrent multi-state structures that take into account the recurring start and stop of medication. We also investigate modelling choices, such as choosing a time-scale for the transition rates and borrowing information across transitions. RESULTS: Each structure has its own utility and answers a specific research question. However, the more complex structures (bidirectional, recurrent) enable accounting for the intermittent nature of prescribed medication data. These structures deliver estimates of the probability of being under medication and total time spent under medication over the follow-up period. Sensitivity analyses over different definitions of the medication cycle and different choices of timescale when modelling the transition intensity rates show that the estimates of total probabilities of being in a medication cycle over follow-up derived from the complex structures are quite stable. CONCLUSIONS: Each research question requires the definition of an appropriate multi-state structure, with more composite ones requiring such an increase in the complexity of the multi-state structure. When a research question is related with an outcome of interest that repeatedly changes over time, such as the medication status based on prescribed medication, the use of novel multi-state models of adequate complexity coupled with sensible modelling choices can successfully address composite, more realistic research questions.


Assuntos
Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/tratamento farmacológico , Recidiva Local de Neoplasia , Antidepressivos/uso terapêutico , Sistema de Registros , Prescrições de Medicamentos
5.
Med Decis Making ; 41(6): 667-684, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-33813933

RESUMO

Economic evaluations conducted alongside randomized controlled trials are a popular vehicle for generating high-quality evidence on the incremental cost-effectiveness of competing health care interventions. Typically, in these studies, resource use (and by extension, economic costs) and clinical (or preference-based health) outcomes data are collected prospectively for trial participants to estimate the joint distribution of incremental costs and incremental benefits associated with the intervention. In this article, we extend the generalized linear mixed-model framework to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects. We provide new wrapper functions to estimate the models in Stata and R by maximum and restricted maximum quasi-likelihood and compare the performance of the new routines with alternative implementations across a range of statistical programming packages. Empirical applications using observed and simulated data from clinical trials suggest that the new methods produce broadly similar results as compared with Stata's merlin and gsem commands and a Bayesian implementation in WinBUGS. We highlight that, although these empirical applications primarily focus on trial-based economic evaluations, the new methods presented can be generalized to other health economic investigations characterized by multivariate hierarchical data structures.


Assuntos
Atenção à Saúde , Teorema de Bayes , Análise Custo-Benefício , Humanos , Modelos Lineares
6.
BMC Med Res Methodol ; 21(1): 16, 2021 01 11.
Artigo em Inglês | MEDLINE | ID: mdl-33430778

RESUMO

BACKGROUND: Multi-state models are being increasingly used to capture complex disease pathways. The convenient formula of the exponential multi-state model can facilitate a quick and accessible understanding of the data. However, assuming time constant transition rates is not always plausible. On the other hand, obtaining predictions from a fitted model with time-dependent transitions can be challenging. One proposed solution is to utilise a general simulation algorithm to calculate predictions from a fitted multi-state model. METHODS: Predictions obtained from an exponential multi-state model were compared to those obtained from two different parametric models and to non-parametric Aalen-Johansen estimates. The first comparative approach fitted a multi-state model with transition-specific distributions, chosen separately based on the Akaike Information Criterion. The second approach was a Royston-Parmar multi-state model with 4 degrees of freedom, which was chosen as a reference model flexible enough to capture complex hazard shapes. All quantities were obtained analytically for the exponential and Aalen-Johansen approaches. The transition rates for the two comparative approaches were also obtained analytically, while all other quantities were obtained from the fitted models via a general simulation algorithm. Metrics investigated were: transition probabilities, attributable mortality (AM), population attributable fraction (PAF) and expected length of stay. This work was performed on previously analysed hospital acquired infection (HAI) data. By definition, a HAI takes three days to develop and therefore selected metrics were also predicted from time 3 (delayed entry). RESULTS: Despite clear deviations from the constant transition rates assumption, the empirical estimates of the transition probabilities were approximated reasonably well by the exponential model. However, functions of the transition probabilities, e.g. AM and PAF, were not well approximated and the comparative models offered considerable improvements for these metrics. They also provided consistent predictions with the empirical estimates in the case of delayed entry time, unlike the exponential model. CONCLUSION: We conclude that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates. The multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data. User-friendly code is provided.


Assuntos
Hospitais , Modelos Estatísticos , Humanos , Cadeias de Markov , Probabilidade , Análise de Sobrevida
7.
J Thromb Haemost ; 18(6): 1348-1356, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32073229

RESUMO

BACKGROUND: Venous thromboembolism (VTE) is a frequent complication of cancer. Elevated D-dimer is associated with an increased risk of cancer-associated VTE. Whether changes in D-dimer over time harbor additional prognostic information that may be exploited clinically for dynamic prediction of VTE is unclear. OBJECTIVES: To explore the potential role of longitudinal D-dimer trajectories for personalized prediction of cancer-associated VTE. PATIENTS/METHODS: A total of 167 patients with active malignancy were prospectively enrolled (gastrointestinal: n = 59 [35%], lung: n = 56 [34%], brain: n = 50 [30%], others: n = 2 [1%]; metastatic disease: n = 74 [44%]). D-dimer (median = 0.8 µg/mL [25th-75th percentile: 0.4-2.0]) was measured at baseline and during 602 monthly follow-up visits. Joint models of longitudinal and time-to-event data were implemented to quantify the association between D-dimer trajectories and prospective risk of VTE. RESULTS: VTE occurred in 20 patients (250-day VTE risk = 12.1%, 95% confidence interval [CI], 7.8-18.5). D-dimer increased by 34%/month (0.47 µg/mL/month, 95% CI, 0.22-0.72, P < .0001) in patients who developed VTE, but remained constant in patients who did not develop VTE (change/month = -0.06 µg/mL, 95% CI, -0.15 to 0.02, P = .121). In joint modeling, a doubling of the D-dimer trajectory was associated with a 2.8-fold increase in the risk of VTE (hazard ratio = 2.78, 95% CI, 1.69-4.58, P < .0001). This finding was independent of established VTE risk factors. Highly personalized, dynamic predictions of VTE conditional on individual patients' D-dimer trajectories could be obtained. CONCLUSIONS: D-dimer increases before the onset of cancer-associated VTE, but remains constant over time in patients without VTE. This study represents proof-of-concept that longitudinal trajectories of D-Dimer may advance the personalized assessment of VTE risk in the oncologic setting.


Assuntos
Neoplasias , Tromboembolia Venosa , Biomarcadores , Produtos de Degradação da Fibrina e do Fibrinogênio , Humanos , Neoplasias/complicações , Neoplasias/diagnóstico , Estudos Prospectivos , Fatores de Risco , Fatores de Tempo , Tromboembolia Venosa/diagnóstico , Tromboembolia Venosa/epidemiologia
8.
Stat Neerl ; 74(1): 5-23, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31894164

RESUMO

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

9.
Stat Med ; 38(23): 4477-4502, 2019 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-31328285

RESUMO

Survival models incorporating random effects to account for unmeasured heterogeneity are being increasingly used in biostatistical and applied research. Specifically, unmeasured covariates whose lack of inclusion in the model would lead to biased, inefficient results are commonly modeled by including a subject-specific (or cluster-specific) frailty term that follows a given distribution (eg, gamma or lognormal). Despite that, in the context of parametric frailty models, little is known about the impact of misspecifying the baseline hazard or the frailty distribution or both. Therefore, our aim is to quantify the impact of such misspecification in a wide variety of clinically plausible scenarios via Monte Carlo simulation, using open-source software readily available to applied researchers. We generate clustered survival data assuming various baseline hazard functions, including mixture distributions with turning points, and assess the impact of sample size, variance of the frailty, baseline hazard function, and frailty distribution. Models compared include standard parametric distributions and more flexible spline-based approaches; we also included semiparametric Cox models. The resulting bias can be clinically relevant. In conclusion, we highlight the importance of fitting models that are flexible enough and the importance of assessing model fit. We illustrate our conclusions with two applications using data on diabetic retinopathy and bladder cancer. Our results show the importance of assessing model fit with respect to the baseline hazard function and the distribution of the frailty: misspecifying the former leads to biased relative and absolute risk estimates, whereas misspecifying the latter affects absolute risk estimates and measures of heterogeneity.


Assuntos
Modelos Estatísticos , Análise de Sobrevida , Retinopatia Diabética/mortalidade , Retinopatia Diabética/terapia , Humanos , Método de Monte Carlo , Modelos de Riscos Proporcionais , Ensaios Clínicos Controlados Aleatórios como Assunto , Tamanho da Amostra , Neoplasias da Bexiga Urinária/tratamento farmacológico , Neoplasias da Bexiga Urinária/mortalidade
10.
Stat Med ; 38(11): 2074-2102, 2019 05 20.
Artigo em Inglês | MEDLINE | ID: mdl-30652356

RESUMO

Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some "truth" (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analyzed, and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting, and presentation. In particular, this tutorial provides a structured approach for planning and reporting simulation studies, which involves defining aims, data-generating mechanisms, estimands, methods, and performance measures ("ADEMP"); coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their estimation; guidance on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine, which included at least one simulation study and identify areas for improvement.


Assuntos
Simulação por Computador , Modelos Estatísticos , Viés , Bioestatística/métodos , Guias como Assunto , Método de Monte Carlo , Projetos de Pesquisa
11.
Stat Methods Med Res ; 27(3): 765-784, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-27114326

RESUMO

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.


Assuntos
Bioestatística/métodos , Protocolos de Ensaio Clínico como Assunto , Estudos Cross-Over , Ensaios Clínicos Controlados Aleatórios como Assunto/métodos , Simulação por Computador , Interpretação Estatística de Dados , Seguimentos , Humanos , Estimativa de Kaplan-Meier , Modelos Estatísticos , Modelos de Riscos Proporcionais , Ensaios Clínicos Controlados Aleatórios como Assunto/estatística & dados numéricos , Tamanho da Amostra , Análise de Sobrevida
12.
Am J Epidemiol ; 187(4): 828-836, 2018 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-29020167

RESUMO

Expected or reference mortality rates are commonly used in the calculation of measures such as relative survival in population-based cancer survival studies and standardized mortality ratios. These expected rates are usually presented according to age, sex, and calendar year. In certain situations, stratification of expected rates by other factors is required to avoid potential bias if interest lies in quantifying measures according to such factors as, for example, socioeconomic status. If data are not available on a population level, information from a control population could be used to adjust expected rates. We have presented two approaches for adjusting expected mortality rates using information from a control population: a Poisson generalized linear model and a flexible parametric survival model. We used a control group from BCBaSe-a register-based, matched breast cancer cohort in Sweden with diagnoses between 1992 and 2012-to illustrate the two methods using socioeconomic status as a risk factor of interest. Results showed that Poisson and flexible parametric survival approaches estimate similar adjusted mortality rates according to socioeconomic status. Additional uncertainty involved in the methods to estimate stratified, expected mortality rates described in this study can be accounted for using a parametric bootstrap, but this might make little difference if using a large control population.


Assuntos
Neoplasias da Mama/mortalidade , Projetos de Pesquisa Epidemiológica , Classe Social , Adulto , Idoso , Idoso de 80 Anos ou mais , Interpretação Estatística de Dados , Feminino , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Fatores de Risco , Análise de Sobrevida , Suécia , Incerteza
13.
Stat Med ; 36(29): 4719-4742, 2017 Dec 20.
Artigo em Inglês | MEDLINE | ID: mdl-28872690

RESUMO

Multistate models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this article, we concentrate on parametric multistate models, both Markov and semi-Markov, and develop a flexible framework where each transition can be specified by a variety of parametric models including exponential, Weibull, Gompertz, Royston-Parmar proportional hazards models or log-logistic, log-normal, generalised gamma accelerated failure time models, possibly sharing parameters across transitions. We also extend the framework to allow time-dependent effects. We then use an efficient and generalisable simulation method to calculate transition probabilities from any fitted multistate model, and show how it facilitates the simple calculation of clinically useful measures, such as expected length of stay in each state, and differences and ratios of proportion within each state as a function of time, for specific covariate patterns. We illustrate our methods using a dataset of patients with primary breast cancer. User-friendly Stata software is provided.


Assuntos
Cadeias de Markov , Medição de Risco/métodos , Análise de Sobrevida , Neoplasias da Mama/mortalidade , Neoplasias da Mama/cirurgia , Simulação por Computador , Feminino , Humanos , Tempo de Internação , Modelos Estatísticos , Modelos de Riscos Proporcionais , Fatores de Risco , Fatores de Tempo
14.
Eur Heart J Qual Care Clin Outcomes ; 2(2): 125-140, 2016 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-27042338

RESUMO

AIMS: To examine long-term healthcare utilization and costs of patients with stable coronary artery disease (SCAD). METHODS AND RESULTS: Linked cohort study of 94 966 patients with SCAD in England, 1 January 2001 to 31 March 2010, identified from primary care, secondary care, disease, and death registries. Resource use and costs, and cost predictors by time and 5-year cardiovascular disease (CVD) risk profile were estimated using generalized linear models. Coronary heart disease hospitalizations were 20.5% in the first year and 66% in the year following a non-fatal (myocardial infarction, ischaemic or haemorrhagic stroke) event. Mean healthcare costs were £3133 per patient in the first year and £10 377 in the year following a non-fatal event. First-year predictors of cost included sex (mean cost £549 lower in females), SCAD diagnosis (non-ST-elevation myocardial infarction cost £656 more than stable angina), and co-morbidities (heart failure cost £657 more per patient). Compared with lower risk patients (5-year CVD risk 3.5%), those of higher risk (5-year CVD risk 44.2%) had higher 5-year costs (£23 393 vs. £9335) and lower lifetime costs (£43 020 vs. £116 888). CONCLUSION: Patients with SCAD incur substantial healthcare utilization and costs, which varies and may be predicted by 5-year CVD risk profile. Higher risk patients have higher initial but lower lifetime costs than lower risk patients as a result of shorter life expectancy. Improved cardiovascular survivorship among an ageing CVD population is likely to require stratified care in anticipation of the burgeoning demand.

15.
Heart ; 102(10): 755-62, 2016 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-26864674

RESUMO

OBJECTIVES: To use electronic health records (EHR) to predict lifetime costs and health outcomes of patients with stable coronary artery disease (stable-CAD) stratified by their risk of future cardiovascular events, and to evaluate the cost-effectiveness of treatments targeted at these populations. METHODS: The analysis was based on 94 966 patients with stable-CAD in England between 2001 and 2010, identified in four prospectively collected, linked EHR sources. Markov modelling was used to estimate lifetime costs and quality-adjusted life years (QALYs) stratified by baseline cardiovascular risk. RESULTS: For the lowest risk tenth of patients with stable-CAD, predicted discounted remaining lifetime healthcare costs and QALYs were £62 210 (95% CI £33 724 to £90 043) and 12.0 (95% CI 11.5 to 12.5) years, respectively. For the highest risk tenth of the population, the equivalent costs and QALYs were £35 549 (95% CI £31 679 to £39 615) and 2.9 (95% CI 2.6 to 3.1) years, respectively. A new treatment with a hazard reduction of 20% for myocardial infarction, stroke and cardiovascular disease death and no side-effects would be cost-effective if priced below £72 per year for the lowest risk patients and £646 per year for the highest risk patients. CONCLUSIONS: Existing EHRs may be used to estimate lifetime healthcare costs and outcomes of patients with stable-CAD. The stable-CAD model developed in this study lends itself to informing decisions about commissioning, pricing and reimbursement. At current prices, to be cost-effective some established as well as future stable-CAD treatments may require stratification by patient risk.


Assuntos
Doença da Artéria Coronariana/economia , Doença da Artéria Coronariana/terapia , Registros Eletrônicos de Saúde/economia , Custos de Cuidados de Saúde , Avaliação de Processos em Cuidados de Saúde/economia , Idoso , Idoso de 80 Anos ou mais , Doença da Artéria Coronariana/diagnóstico , Doença da Artéria Coronariana/mortalidade , Análise Custo-Benefício , Mineração de Dados , Bases de Dados Factuais , Técnicas de Apoio para a Decisão , Progressão da Doença , Inglaterra , Feminino , Humanos , Masculino , Cadeias de Markov , Pessoa de Meia-Idade , Modelos Econômicos , Anos de Vida Ajustados por Qualidade de Vida , Fatores de Risco , Fatores de Tempo , Resultado do Tratamento
16.
Stat Med ; 35(7): 1193-209, 2016 Mar 30.
Artigo em Inglês | MEDLINE | ID: mdl-26514596

RESUMO

A now common goal in medical research is to investigate the inter-relationships between a repeatedly measured biomarker, measured with error, and the time to an event of interest. This form of question can be tackled with a joint longitudinal-survival model, with the most common approach combining a longitudinal mixed effects model with a proportional hazards survival model, where the models are linked through shared random effects. In this article, we look at incorporating delayed entry (left truncation), which has received relatively little attention. The extension to delayed entry requires a second set of numerical integration, beyond that required in a standard joint model. We therefore implement two sets of fully adaptive Gauss-Hermite quadrature with nested Gauss-Kronrod quadrature (to allow time-dependent association structures), conducted simultaneously, to evaluate the likelihood. We evaluate fully adaptive quadrature compared with previously proposed non-adaptive quadrature through a simulation study, showing substantial improvements, both in terms of minimising bias and reducing computation time. We further investigate, through simulation, the consequences of misspecifying the longitudinal trajectory and its impact on estimates of association. Our scenarios showed the current value association structure to be very robust, compared with the rate of change that we found to be highly sensitive showing that assuming a simpler trend when the truth is more complex can lead to substantial bias. With emphasis on flexible parametric approaches, we generalise previous models by proposing the use of polynomials or splines to capture the longitudinal trend and restricted cubic splines to model the baseline log hazard function. The methods are illustrated on a dataset of breast cancer patients, modelling mammographic density jointly with survival, where we show how to incorporate density measurements prior to the at-risk period, to make use of all the available information. User-friendly Stata software is provided.


Assuntos
Modelos Estatísticos , Análise de Sobrevida , Bioestatística , Densidade da Mama , Neoplasias da Mama/mortalidade , Simulação por Computador , Feminino , Humanos , Funções Verossimilhança , Estudos Longitudinais , Modelos de Riscos Proporcionais
17.
Health Technol Assess ; 19(10): 1-668, vii-viii, 2015 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-25634033

RESUMO

BACKGROUND: Total hip replacement (THR) involves the replacement of a damaged hip joint with an artificial hip prosthesis. Resurfacing arthroplasty (RS) involves replacement of the joint surface of the femoral head with a metal surface covering. OBJECTIVES: To undertake clinical effectiveness and cost-effectiveness analysis of different types of THR and RS for the treatment of pain and disability in people with end-stage arthritis of the hip, in particular to compare the clinical effectiveness and cost-effectiveness of (1) different types of primary THR and RS for people in whom both procedures are suitable and (2) different types of primary THR for people who are not suitable for hip RS. DATA SOURCES: Electronic databases including MEDLINE, EMBASE, The Cochrane Library, Current Controlled Trials and UK Clinical Research Network (UKCRN) Portfolio Database were searched in December 2012, with searches limited to publications from 2008 and sample sizes of ≥ 100 participants. Reference lists and websites of manufacturers and professional organisations were also screened. REVIEW METHODS: Systematic reviews of the literature were undertaken to appraise the clinical effectiveness and cost-effectiveness of different types of THR and RS for people with end-stage arthritis of the hip. Included randomised controlled trials (RCTs) and systematic reviews were data extracted and risk of bias and methodological quality were independently assessed by two reviewers using the Cochrane Collaboration risk of bias tool and the Assessment of Multiple Systematic Reviews (AMSTAR) tool. A Markov multistate model was developed for the economic evaluation of the technologies. Sensitivity analyses stratified by sex and controlled for age were carried out to assess the robustness of the results. RESULTS: A total of 2469 records were screened of which 37 were included, representing 16 RCTs and eight systematic reviews. The mean post-THR Harris Hip Score measured at different follow-up times (from 6 months to 10 years) did not differ between THR groups, including between cross-linked polyethylene and traditional polyethylene cup liners (pooled mean difference 2.29, 95% confidence interval -0.88 to 5.45). Five systematic reviews reported evidence on different types of THR (cemented vs. cementless cup fixation and implant articulation materials) but these reviews were inconclusive. Eleven cost-effectiveness studies were included; four provided relevant cost and utility data for the model. Thirty registry studies were included, with no studies reporting better implant survival for RS than for all types of THR. For all analyses, mean costs for RS were higher than those for THR and mean quality-adjusted life-years (QALYs) were lower. The incremental cost-effectiveness ratio for RS was dominated by THR, that is, THR was cheaper and more effective than RS (for a lifetime horizon in the base-case analysis, the incremental cost of RS was £11,284 and the incremental QALYs were -0.0879). For all age and sex groups RS remained clearly dominated by THR. Cost-effectiveness acceptability curves showed that, for all patients, THR was almost 100% cost-effective at any willingness-to-pay level. There were age and sex differences in the populations with different types of THR and variations in revision rates (from 1.6% to 3.5% at 9 years). For the base-case analysis, for all age and sex groups and a lifetime horizon, mean costs for category E (cemented components with a polyethylene-on-ceramic articulation) were slightly lower and mean QALYs for category E were slightly higher than those for all other THR categories in both deterministic and probabilistic analyses. Hence, category E dominated the other four categories. Sensitivity analysis using an age- and sex-adjusted log-normal model demonstrated that, over a lifetime horizon and at a willingness-to-pay threshold of £20,000 per QALY, categories A and E were equally likely (50%) to be cost-effective. LIMITATIONS: A large proportion of the included studies were inconclusive because of poor reporting, missing data, inconsistent results and/or great uncertainty in the treatment effect estimates. This warrants cautious interpretation of the findings. The evidence on complications was scarce, which may be because of the absence or rarity of these events or because of under-reporting. The poor reporting meant that it was not possible to explore contextual factors that might have influenced study results and also reduced the applicability of the findings to routine clinical practice in the UK. The scope of the review was limited to evidence published in English in 2008 or later, which could be interpreted as a weakness; however, systematic reviews would provide summary evidence for studies published before 2008. CONCLUSIONS: Compared with THR, revision rates for RS were higher, mean costs for RS were higher and mean QALYs gained were lower; RS was dominated by THR. Similar results were obtained in the deterministic and probabilistic analyses and for all age and sex groups THR was almost 100% cost-effective at any willingness-to-pay level. Revision rates for all types of THR were low. Category A THR (cemented components with a polyethylene-on-metal articulation) was more cost-effective for older age groups. However, across all age-sex groups combined, the mean cost for category E THR (cemented components with a polyethylene-on-ceramic articulation) was slightly lower and the mean QALYs gained were slightly higher. Category E therefore dominated the other four categories. Certain types of THR appeared to confer some benefit, including larger femoral head sizes, use of a cemented cup, use of a cross-linked polyethylene cup liner and a ceramic-on-ceramic as opposed to a metal-on-polyethylene articulation. Further RCTs with long-term follow-up are needed. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013003924. FUNDING: The National Institute for Health Research Health Technology Assessment programme.


Assuntos
Artroplastia de Quadril/economia , Artroplastia de Quadril/métodos , Osteoartrite do Quadril/cirurgia , Manejo da Dor/economia , Manejo da Dor/métodos , Fatores Etários , Análise Custo-Benefício , Humanos , Modelos Econômicos , Anos de Vida Ajustados por Qualidade de Vida , Ensaios Clínicos Controlados Aleatórios como Assunto , Fatores Sexuais , Avaliação da Tecnologia Biomédica
18.
Med Decis Making ; 34(3): 387-402, 2014 04.
Artigo em Inglês | MEDLINE | ID: mdl-24449433

RESUMO

BACKGROUND: Treatment switching commonly occurs in clinical trials of novel interventions in the advanced or metastatic cancer setting. However, methods to adjust for switching have been used inconsistently and potentially inappropriately in health technology assessments (HTAs). OBJECTIVE: We present recommendations on the use of methods to adjust survival estimates in the presence of treatment switching in the context of economic evaluations. METHODS: We provide background on the treatment switching issue and summarize methods used to adjust for it in HTAs. We discuss the assumptions and limitations associated with adjustment methods and draw on results of a simulation study to make recommendations on their use. RESULTS: We demonstrate that methods used to adjust for treatment switching have important limitations and often produce bias in realistic scenarios. We present an analysis framework that aims to increase the probability that suitable adjustment methods can be identified on a case-by-case basis. We recommend that the characteristics of clinical trials, and the treatment switching mechanism observed within them, should be considered alongside the key assumptions of the adjustment methods. Key assumptions include the "no unmeasured confounders" assumption associated with the inverse probability of censoring weights (IPCW) method and the "common treatment effect" assumption associated with the rank preserving structural failure time model (RPSFTM). CONCLUSIONS: The limitations associated with switching adjustment methods such as the RPSFTM and IPCW mean that they are appropriate in different scenarios. In some scenarios, both methods may be prone to bias; "2-stage" methods should be considered, and intention-to-treat analyses may sometimes produce the least bias. The data requirements of adjustment methods also have important implications for clinical trialists.


Assuntos
Tecnologia Biomédica , Ensaios Clínicos Controlados Aleatórios como Assunto , Análise de Sobrevida , Custos e Análise de Custo
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