| 1. |
- Abrahamsson, L, et al.
(författare)
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Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies
- 2020
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 29:2, s. 374-395
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Tidskriftsartikel (refereegranskat)abstract
- Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2–3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias.
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| 2. |
- Bottai, M, et al.
(författare)
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Modeling the probability of occurrence of events
- 2021
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 30:8, s. 1976-1987
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces the event-probability function, a measure of occurrence of an event of interest over time, defined as the instantaneous probability of an event at a given time point conditional on having survived until that point. Unlike the hazard function, the event-probability function is a proper probability. This paper describes properties and interpretation of the event-probability function, presents its connection with other popular functions, such as the hazard and survival functions, proposes practical flexible proportional-odds models for estimating conditional event-probabilities given covariates with possibly censored and truncated observations, discusses the theoretical and computational aspects of parameter estimation, and applies the proposed models for assessing mortality in patients with metastatic renal carcinoma from a randomized clinical trial.
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| 3. |
- Bottai, M, et al.
(författare)
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Nonlinear parametric quantile models
- 2020
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 29:12, s. 3757-3769
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Tidskriftsartikel (refereegranskat)abstract
- Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper presents the general case of nonlinear parametric quantile models. These can be nonlinear with respect to the parameters, the covariates, or both. Some important features and asymptotic properties of the proposed estimator are described, and its finite-sample behavior is assessed in a simulation study. Nonlinear parametric quantile models are applied to estimate extreme quantiles of longitudinal measures of respiratory mechanics in asthmatic children from an epidemiological study and to evaluate a dose–response relationship in a toxicological laboratory experiment.
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| 4. |
- Dahlqwist, E, et al.
(författare)
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Using instrumental variables to estimate the attributable fraction
- 2020
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 29:8, s. 2063-2073
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Tidskriftsartikel (refereegranskat)abstract
- In order to design efficient interventions aimed to improve public health, policy makers need to be provided with reliable information of the health burden of different risk factors. For this purpose, we are interested in the proportion of cases that could be prevented had some harmful exposure been eliminated from the population, i.e. the attributable fraction. The attributable fraction is a causal measure; thus, to estimate the attributable fraction from observational data, we have to make appropriate adjustment for confounding. However, some confounders may be unobserved, or even unknown to the investigator. A possible solution to this problem is to use instrumental variable analysis. In this work, we present how the attributable fraction can be estimated with instrumental variable methods based on the two-stage estimator or the G-estimator. One situation when the problem of unmeasuredconfounding may be particularly severe is when assessing the effect of low educational qualifications on coronary heart disease. By using Mendelian randomization, a special case of instrumental variable analysis, it has been claimed that low educational qualifications is a causal risk factor for coronary heart disease. We use Mendelian randomization to estimate the causal risk ratio and causal odds ratio of low educational qualifications as a risk factor for coronary heart disease with data from the UK Biobank. We compare the two-stage and G-estimator as well as the attributable fraction based on the two estimators. The plausibility of drawing causal conclusion in this analysis is thoroughly discussed and alternative genetic instrumental variables are tested.
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| 5. |
- Freeman, SC, et al.
(författare)
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Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network
- 2022
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 31:5, s. 839-861
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Tidskriftsartikel (refereegranskat)abstract
- Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed. Objectives To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches. Methods The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models. Results All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches. Conclusion The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.
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| 6. |
- Gasparini, A, et al.
(författare)
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A natural history and copula-based joint model for regional and distant breast cancer metastasis
- 2022
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 31:12, s. 2415-2430
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Tidskriftsartikel (refereegranskat)abstract
- The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding – potentially latent – disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.
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| 7. |
- Gasparini, A, et al.
(författare)
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Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data
- 2022
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 31:5, s. 862-881
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Tidskriftsartikel (refereegranskat)abstract
- We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, still, with enough data it should be possible to detect strong misspecifications. Furthermore, we fit our model to observational data from an extension of a case-control study of post-menopausal breast cancer in Sweden, providing model-based estimates of the probability of being free from detected distant metastasis as a function of tumour size, mode of detection (of the primary tumour), and screening history. For women with screen-detected cancer and two previous negative screens, the probabilities of being free from detected distant metastases 5 years after detection and removal of the primary tumour are 0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We also study the probability of having latent/dormant metastases at detection of the primary tumour, estimating that 33% of patients in our study had such metastases.
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| 8. |
- Hamza, T, et al.
(författare)
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A Bayesian dose-response meta-analysis model: A simulations study and application
- 2021
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 30:5, s. 1358-1372
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Tidskriftsartikel (refereegranskat)abstract
- Dose–response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose–response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose–response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose–response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose–response meta-analysis model in a simulation study. We found that the Bayesian dose–response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log–log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose–response curve increases between zero dose and 30–40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.
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| 9. |
- Hamza, T, et al.
(författare)
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A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines
- 2022
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; , s. 9622802211070256-
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Tidskriftsartikel (refereegranskat)abstract
- Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose–effect relationship using restricted cubic splines. We extend existing models into a dose–effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose–effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose–effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose–effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice.
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| 10. |
- Ning, Yilin, et al.
(författare)
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Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes
- 2020
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 29:2, s. 437-454
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Tidskriftsartikel (refereegranskat)abstract
- The rank-ordered logit (rologit) model was recently introduced as a robust approach for analysing continuous outcomes, with the linear exposure effect estimated by scaling the rank-based log-odds estimate. Here we extend the application of the rologit model to continuous outcomes with ties and ordinal outcomes treated as imperfectly-observed continuous outcomes. By identifying the functional relationship between survival times and continuous outcomes, we explicitly establish the equivalence between the rologit and Cox models to justify the use of the Breslow, Efron and perturbation methods in the analysis of continuous outcomes with ties. Using simulation, we found all three methods perform well with few ties. Although an increasing extent of ties increased the bias of the log-odds and linear effect estimates and resulted in reduced power, which was somewhat worse when the model was mis-specified, the perturbation method maintained a type I error around 5%, while the Efron method became conservative with heavy ties but outperformed Breslow. In general, the perturbation method had the highest power, followed by the Efron and then the Breslow method. We applied our approach to three real-life datasets, demonstrating a seamless analytical workflow that uses stratification for confounder adjustment in studies of continuous and ordinal outcomes.
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