| 1. |
- Abrahamsson, L, et al.
(författare)
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A statistical model of breast cancer tumour growth with estimation of screening sensitivity as a function of mammographic density
- 2016
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 25:4, s. 1620-1637
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Tidskriftsartikel (refereegranskat)abstract
- Understanding screening sensitivity and tumour progression is important for designing and evaluating screening programmes for breast cancer. Several approaches for estimating tumour growth rates have been described, some of which simultaneously estimate (mammography) screening sensitivity. None of the continuous tumour growth modelling approaches has incorporated mammographic density, although it is known to have a profound influence on mammographic screening sensitivity. We describe a new approach for estimating breast cancer tumour growth which builds on recently described continuous tumour growth models and estimates mammographic screening sensitivity as a function of tumour size and mammographic density.
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| 2. |
- 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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| 3. |
- Albajes-Eizagirre, A, et al.
(författare)
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Meta-analysis of non-statistically significant unreported effects
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:12, s. 3741-3754
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Tidskriftsartikel (refereegranskat)abstract
- Published studies in Medicine (and virtually any other discipline) sometimes report that a difference or correlation did not reach statistical significance but do not report its effect size or any statistic from which the latter may be derived. Unfortunately, meta-analysts should not exclude these studies because their exclusion would bias the meta-analytic outcome, but also they cannot be included as null effect sizes because this strategy is also associated to bias. To overcome this problem, we have developed MetaNSUE, a novel method based on multiple imputations of the censored information. We also provide an R package and an easy-to-use Graphical User Interface for non-R meta-analysts.
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| 4. |
- Andersson, Eva M., 1968, et al.
(författare)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2008
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 17:4, s. 421-438
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Tidskriftsartikel (refereegranskat)abstract
- We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain,), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
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| 5. |
- Bottai, M
(författare)
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A regression method for modelling geometric rates
- 2017
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 26:6, s. 2700-2707
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Tidskriftsartikel (refereegranskat)abstract
- The occurrence of an event of interest over time is often summarized by the incidence rate, defined as the average number of events per person-time. This type of rate applies to events that may occur repeatedly over time on any given subject, such as infections, and Poisson regression represents a natural regression method for modelling the effect of covariates on it. However, for events that can occur only once, such as death, the geometric rate may be a better summary measure. The geometric rate has long been utilized in demography for studying the growth of populations and in finance to compute compound interest on capital. This type of rate, however, is virtually unknown to medical research. This may be partly a consequence of the lack of a regression method for it. This paper describes a regression method for modelling the effect of covariates on the geometric rate. The described method is based on applying quantile regression to a transform of the time-to-event variable. The proposed method is used to analyze mortality in a randomized clinical trial and in an observational epidemiological study.
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| 6. |
- 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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| 7. |
- 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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| 8. |
- Crippa, A, et al.
(författare)
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One-stage dose-response meta-analysis for aggregated data
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:5, s. 1579-1596
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Tidskriftsartikel (refereegranskat)abstract
- The standard two-stage approach for estimating non-linear dose–response curves based on aggregated data typically excludes those studies with less than three exposure groups. We develop the one-stage method as a linear mixed model and present the main aspects of the methodology, including model specification, estimation, testing, prediction, goodness-of-fit, model comparison, and quantification of between-studies heterogeneity. Using both fictitious and real data from a published meta-analysis, we illustrated the main features of the proposed methodology and compared it to a traditional two-stage analysis. In a one-stage approach, the pooled curve and estimates of the between-studies heterogeneity are based on the whole set of studies without any exclusion. Thus, even complex curves (splines, spike at zero exposure) defined by several parameters can be estimated. We showed how the one-stage method may facilitate several applications, in particular quantification of heterogeneity over the exposure range, prediction of marginal and conditional curves, and comparison of alternative models. The one-stage method for meta-analysis of non-linear curves is implemented in the dosresmeta R package. It is particularly suited for dose–response meta-analyses of aggregated where the complexity of the research question is better addressed by including all the studies.
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| 9. |
- Dahlqwist, E, et al.
(författare)
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Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data
- 2019
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Ingår i: Statistical methods in medical research. - : SAGE Publications. - 1477-0334 .- 0962-2802. ; 28:2, s. 462-485
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Tidskriftsartikel (refereegranskat)abstract
- The between-within frailty model has been proposed as a viable analysis tool for clustered survival time outcomes. Previous research has shown that this model gives consistent estimates of the exposure–outcome hazard ratio in the presence of unmeasured cluster-constant confounding, which the ordinary frailty model does not, and that estimates obtained from the between-within frailty model are often more efficient than estimates obtained from the stratified Cox proportional hazards model. In this paper, we derive novel estimation techniques for regression standardization with between-within frailty models. We also show how between-within frailty models can be used to estimate the attributable fraction function, which is a generalization of the attributable fraction for survival time outcomes. We illustrate the proposed methods by analyzing a large cohort on preterm birth and attention deficit hyperactivity disorder. To facilitate use of the proposed methods, we provide R code for all analyses.
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| 10. |
- 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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