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| 2. |
- Bengtsson, Calle, 1934, et al.
(författare)
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A framework for quantifying net benefits of alternative prognostic models
- 2012
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 31:2, s. 114-130
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Tidskriftsartikel (refereegranskat)abstract
- New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context.We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions.We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with themultistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robustagainst a range of modelling assumptions, including adjusting for competing risks.
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| 4. |
- Burgess, S., et al.
(författare)
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
- 2010
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Ingår i: Statistics in medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 29:12, s. 1298-311
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Tidskriftsartikel (refereegranskat)abstract
- Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.
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| 6. |
- Burman, C. F., et al.
(författare)
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The dual test: Safeguarding p-value combination tests for adaptive designs
- 2010
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 29:7-8, s. 797-807
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Tidskriftsartikel (refereegranskat)abstract
- Many modern adaptive designs apply an analysis where p-values from different stages are weighted together to an overall hypothesis test. One merit of this combination approach is that the design can be made very flexible. However, combination tests violate the sufficiency and conditionality principles. As a consequence, combination tests may lead to absurd conclusions, such as 'proving' a positive effect while the average effect is negative. We explore the possibility of modifying the test so that such illogical conclusions are no longer possible. The dual test requires both the weighted combination test and a nave test, ignoring the adaptations, to be statistically significant. The result is that the flexibility and type I error level control of the combination test are preserved, while the nave test adds a safeguard against unconvincing results. The dual test is, by construction, at least as conservative as the combination test. However, many design changes will not lead to any power loss. A typical situation where the combination approach can be used is two-stage sample size reestimation (SSR). For this case, we give a complete specification of all sample size modifications for which the two tests are equally powerful. We also study the overall power loss for some suggested SSR rules. Rules based on conditional power generally lead to ignorable power loss while a decision analytic approach exhibits clear discrepancies between the two tests.
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| 11. |
- Hess, Wolfgang, et al.
(författare)
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Competing-risks duration models with correlated random effects: an application to dementia patients’ transition histories
- 2014
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Ingår i: Statistics in Medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 33:22, s. 3919-3931
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Tidskriftsartikel (refereegranskat)abstract
- Abstract in Undetermined Multi-state transition models are widely applied tools to analyze individual event histories in the medical or social sciences. In this paper, we propose the use of (discrete-time) competing-risks duration models to analyze multi-transition data. Unlike conventional Markov transition models, these models allow the estimated transition probabilities to depend on the time spent in the current state. Moreover, the models can be readily extended to allow for correlated transition probabilities. A further virtue of these models is that they can be estimated using conventional regression tools for discrete-response data, such as the multinomial logit model. The latter is implemented in many statistical software packages and can be readily applied by empirical researchers. Moreover, model estimation is feasible, even when dealing with very large data sets, and simultaneously allowing for a flexible form of duration dependence and correlation between transition probabilities. We derive the likelihood function for a model with three competing target states and discuss a feasible and readily applicable estimation method. We also present the results from a simulation study, which indicate adequate performance of the proposed approach. In an empirical application, we analyze dementia patients' transition probabilities from the domestic setting, taking into account several, partly duration-dependent covariates. Copyright © 2014 John Wiley & Sons, Ltd.
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| 16. |
- Lindhagen, Lars, et al.
(författare)
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Level-adjusted funnel plots based on predicted marginal expectations : an application to prophylactic antibiotics in gallstone surgery
- 2014
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:21, s. 3655-3675
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Tidskriftsartikel (refereegranskat)abstract
- Funnel plots are widely used to visualize grouped data, for example, in institutional comparison. This paper extends the concept to a multi-level setting, displaying one level at a time, adjusted for the other levels, as well as for covariates at all levels. These level-adjusted funnel plots are based on a Markov chain Monte Carlo fit of a random effects model, translating the estimated model parameters to predicted marginal expectations. Working within the estimation framework, we accommodate outlying institutions using heavy-tailed random effects distributions. We also develop computer-efficient methods to compute predicted probabilities in the case of dichotomous outcome data and various random effect distributions. We apply the method to a data set on prophylactic antibiotics in gallstone surgery.
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| 17. |
- Lisovskaja, Vera, 1984, et al.
(författare)
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On the choice of doses for phase III clinical trials
- 2013
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 32:10, s. 1661-1676
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Tidskriftsartikel (refereegranskat)abstract
- Many potential new medicines fail in phase III clinical trials, because of either insufficient efficacy or intolerability. Such failures may be caused by the absence of an effect and also if a suboptimal dose is being tested. It is thus important to consider how to optimise the choice of dose or doses that continue into the confirmatory phase. For many indications, it is common to test one single active dose in phase III. However, phase IIB dose-finding trials are relatively small and often lack the ability of precisely estimating the dose–response curves for efficacy and tolerability. Because of this uncertainty in dose response, it is reasonable to consider bringing more than one dose into phase III. Using simple but illustrative models, we find the optimal doses and compare the probability of success, for fixed total sample sizes, when one or two active doses are included in phase III.
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| 18. |
- Persson, Emma, 1981-, et al.
(författare)
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Estimating a marginal causal odds ratio in a case-control design : analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus
- 2013
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 32:14, s. 2500-2512
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of marginal causal effects from case-control data has two complications: (i) confounding due to the fact that the exposure under study is not randomized, and (ii) bias from the case-control sampling scheme. In this paper, we study estimators of the marginal causal odds ratio, addressing these issues for matched and unmatched case-control designs when utilizing the knowledge of the known prevalence of being a case. The estimators are implemented in simulations where their finite sample properties are studied and approximations of their variances are derived with the delta method. Also, we illustrate the methods by analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus using data from the Swedish Childhood Diabetes Register, a nationwide population-based incidence register.
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| 19. |
- Ravva, Patanjali, et al.
(författare)
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A linearization approach for the model-based analysis of combined aggregate and individual patient data
- 2014
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:9, s. 1460-1476
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Tidskriftsartikel (refereegranskat)abstract
- The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data.
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| 22. |
- Salim, Agus, et al.
(författare)
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Analysis of incidence and prognosis from 'extreme' case-control designs
- 2014
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Ingår i: Statistics in Medicine. - : Wiley-Blackwell. - 0277-6715 .- 1097-0258. ; 33:30, s. 5388-5398
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Tidskriftsartikel (refereegranskat)abstract
- The significant investment in measuring biomarkers has prompted investigators to improve cost-efficiency by sub-sampling in non-standard study designs. For example, investigators studying prognosis may assume that any differences in biomarkers are likely to be most apparent in an extreme sample of the earliest deaths and the longest-surviving controls. Simple logistic regression analysis of such data does not exploit the information available in the survival time, and statistical methods that model the sampling scheme may be more efficient. We derive likelihood equations that reflect the complex sampling scheme in unmatched and matched extreme' case-control designs. We investigated the performance and power of the method in simulation experiments, with a range of underlying hazard ratios and study sizes. Our proposed method resulted in hazard ratio estimates close to those obtained from the full cohort. The standard error estimates also performed well when compared with the empirical variance. In an application to a study investigating markers for lethal prostate cancer, an extreme case-control sample of lethal cases and the longest-surviving controls provided estimates of the effect of Gleason score in close agreement with analysis of all the data. By using the information in the sampling design, our method enables efficient and valid estimation of the underlying hazard ratio from a study design that is intuitive and easily implemented.
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| 25. |
- Sjölander, Arvid, et al.
(författare)
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Between-within models for survival analysis.
- 2013
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Ingår i: Statistics in Medicine. - Hoboken, USA : Wiley-Blackwell. - 0277-6715 .- 1097-0258. ; 32:18, s. 3067-3076
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Tidskriftsartikel (refereegranskat)abstract
- A popular way to control for confounding in observational studies is to identify clusters of individuals (e.g., twin pairs), such that a large set of potential confounders are constant (shared) within each cluster. By studying the exposure-outcome association within clusters, we are in effect controlling for the whole set of shared confounders. An increasingly popular analysis tool is the between-within (BW) model, which decomposes the exposure-outcome association into a 'within-cluster effect' and a 'between-cluster effect'. BW models are relatively common for nonsurvival outcomes and have been studied in the theoretical literature. Although it is straightforward to use BW models for survival outcomes, this has rarely been carried out in practice, and such models have not been studied in the theoretical literature. In this paper, we propose a gamma BW model for survival outcomes. We compare the properties of this model with the more standard stratified Cox regression model and use the proposed model to analyze data from a twin study of obesity and mortality. We find the following: (i) the gamma BW model often produces a more powerful test of the 'within-cluster effect' than stratified Cox regression; and (ii) the gamma BW model is robust against model misspecification, although there are situations where it could give biased estimates.
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