| 51. |
- Hansson, Sven Ove
(författare)
-
Replacing the no-effect level (NOEL) with bounded effect levels (OBEL and LEBEL)
- 2002
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 21:20, s. 3071-3078
-
Tidskriftsartikel (refereegranskat)abstract
- From experiments or epidemiological studies designed to search for a particular toxic effect, it is in general possible to determine an upper bound for that effect. This observed bounded effect level (OBEL) is defined for both positive and negative experiments. It is non-zero even for negative experiments, and it is inversely related to the size of the exposed group. The OBEL can be used to determine the linearly extrapolated bounded effect level (LEBEL) for various effect doses. Contrary to no-observed-effect' levels (NOELs), LEBEL values are designed to protect against type 11 (false negative) errors. It is proposed that LEBEL values replace NOELs as a tool for decision-making.
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| 52. |
- Hess, Wolfgang, et al.
(författare)
-
Competing-risks duration models with correlated random effects: an application to dementia patients’ transition histories
- 2014
-
Ingår i: Statistics in Medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 33:22, s. 3919-3931
-
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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| 53. |
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| 54. |
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| 55. |
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| 56. |
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| 57. |
- Konstantinou, Konstantinos, 1993, et al.
(författare)
-
Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
- 2023
-
Ingår i: Statistics in Medicine. - 0277-6715 .- 1097-0258. ; 42:23, s. 4128-4146
-
Tidskriftsartikel (refereegranskat)abstract
- Diabetic neuropathy is a disorder characterized by impaired nerve function and reduction of the number of epidermal nerve fibers per epidermal surface. Additionally, as neuropathy related nerve fiber loss and regrowth progresses over time, the two-dimensional spatial arrangement of the nerves becomes more clustered. These observations suggest that with development of neuropathy, the spatial pattern of diminished skin innervation is defined by a thinning process which remains incompletely characterized. We regard samples obtained from healthy controls and subjects suffering from diabetic neuropathy as realisations of planar point processes consisting of nerve entry points and nerve endings, and propose point process models based on spatial thinning to describe the change as neuropathy advances. Initially, the hypothesis that the nerve removal occurs completely at random is tested using independent random thinning of healthy patterns. Then, a dependent parametric thinning model that favors the removal of isolated nerve trees is proposed. Approximate Bayesian computation is used to infer the distribution of the model parameters, and the goodness-of-fit of the models is evaluated using both non-spatial and spatial summary statistics. Our findings suggest that the nerve mortality process changes as neuropathy advances.
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| 58. |
- Konstantionu, Konstantinos, et al.
(författare)
-
Spatial modeling of epidermal nerve fiber patterns
- 2021
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:29, s. 6479-6500
-
Tidskriftsartikel (refereegranskat)abstract
- Peripheral neuropathy is a condition associated with poor nerve functionality. Epidermal nerve fiber (ENF) counts per epidermal surface are dramatically reduced and the two-dimensional (2D) spatial structure of ENFs tends to become more clustered as neuropathy progresses. Therefore, studying the spatial structure of ENFs is essential to fully understand the mechanisms that guide those morphological changes. In this article, we compare ENF patterns of healthy controls and subjects suffering from mild diabetic neuropathy by using suction skin blister specimens obtained from the right foot. Previous analysis of these data has focused on the analysis and modeling of the spatial ENF patterns consisting of the points where the nerves enter the epidermis, base points, and the points where the nerve fibers terminate, end points, projected on a 2D plane, regarding the patterns as realizations of spatial point processes. Here, we include the first branching points, the points where the nerve trees branch for the first time, and model the three-dimensional (3D) patterns consisting of these three types of points. To analyze the patterns, spatial summary statistics are used and a new epidermal active territory that measures the volume in the epidermis that is covered by the individual nerve fibers is constructed. We developed a model for both the 2D and the 3D patterns including the branching points. Also, possible competitive behavior between individual nerves is examined. Our results indicate that changes in the ENFs spatial structure can more easily be detected in the later parts of the ENFs.
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| 59. |
- Kühnel, Line, et al.
(författare)
-
Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales
- 2021
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:14, s. 3251-3266
-
Tidskriftsartikel (refereegranskat)abstract
- Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale—sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease.
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| 60. |
- Kuronen, M., et al.
(författare)
-
Point process models for sweat gland activation observed with noise
- 2021
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:8, s. 2055-2072
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors.
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| 61. |
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| 62. |
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| 63. |
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| 64. |
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| 65. |
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| 66. |
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| 67. |
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| 68. |
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| 69. |
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| 70. |
- Lindhagen, Lars, et al.
(författare)
-
Level-adjusted funnel plots based on predicted marginal expectations : an application to prophylactic antibiotics in gallstone surgery
- 2014
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:21, s. 3655-3675
-
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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| 71. |
- Lindmark, Anita, et al.
(författare)
-
Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
- 2018
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 37:10, s. 1744-1762
-
Tidskriftsartikel (refereegranskat)abstract
- To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assumptions about unconfoundedness are required. Since these assumptions cannot be tested using the observed data, a mediation analysis should always be accompanied by a sensitivity analysis of the resulting estimates. In this article, we propose a sensitivity analysis method for parametric estimation of direct and indirect effects when the exposure, mediator, and outcome are all binary. The sensitivity parameters consist of the correlations between the error terms of the exposure, mediator, and outcome models. These correlations are incorporated into the estimation of the model parameters and identification sets are then obtained for the direct and indirect effects for a range of plausible correlation values. We take the sampling variability into account through the construction of uncertainty intervals. The proposed method is able to assess sensitivity to both mediator‐outcome confounding and confounding involving the exposure. To illustrate the method, we apply it to a mediation study based on the data from the Swedish Stroke Register (Riksstroke). An R package that implements the proposed method is available.
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| 72. |
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| 73. |
- Lisovskaja, Vera, 1984, et al.
(författare)
-
On the choice of doses for phase III clinical trials
- 2013
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 32:10, s. 1661-1676
-
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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| 74. |
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| 75. |
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| 76. |
- Magnúsdóttir, Bergrún Tinna, et al.
(författare)
-
Simultaneous estimation of parameters in the bivariate Emax model
- 2015
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 34:28, s. 3714-3723
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
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| 77. |
- Mandal, Saumen, et al.
(författare)
-
Stein-type shrinkage estimators in gamma regression model with application to prostate cancer data
- 2019
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 38:22, s. 4310-4322
-
Tidskriftsartikel (refereegranskat)abstract
- Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data.
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| 78. |
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| 79. |
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| 80. |
- Norén, G. Niklas, et al.
(författare)
-
A statistical methodology for drug–drug interaction surveillance
- 2008
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 27:16, s. 3057-3070
-
Tidskriftsartikel (refereegranskat)abstract
- Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.
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| 81. |
- Nyberg, Joakim, et al.
(författare)
-
Properties of the full random-effect modeling approach with missing covariate data
- 2024
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:5, s. 935-952
-
Tidskriftsartikel (refereegranskat)abstract
- During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.
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| 82. |
- Olarte Parra, Camila, et al.
(författare)
-
Trial emulation and survival analysis for disease incidence registers : A case study on the causal effect of pre-emptive kidney transplantation
- 2022
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 41:21, s. 4176-4199
-
Tidskriftsartikel (refereegranskat)abstract
- When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.
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| 83. |
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| 84. |
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| 85. |
- Persson, Emma, 1981-, et al.
(författare)
-
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
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 32:14, s. 2500-2512
-
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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| 86. |
- Persson, Emma, et al.
(författare)
-
Estimating marginal causal effects in a secondary analysis of case-control data
- 2017
-
Ingår i: Statistics in Medicine. - Hoboken : Wiley-Blackwell. - 0277-6715 .- 1097-0258. ; 36:15, s. 2404-2419
-
Tidskriftsartikel (refereegranskat)abstract
- When an initial case-control study is performed, data can be used in a secondary analysis to evaluate the effect of the case-defining event on later outcomes. In this paper, we study the example in which the role of the event is changed from a response variable to a treatment of interest. If the aim is to estimate marginal effects, such as average effects in the population, the sampling scheme needs to be adjusted for. We study estimators of the average effect of the treatment in a secondary analysis of matched and unmatched case-control data where the probability of being a case is known. For a general class of estimators, we show the components of the bias resulting from ignoring the sampling scheme and demonstrate a design-weighted matching estimator of the average causal effect. In simulations, the finite sample properties of the design-weighted matching estimator are studied. Using a Swedish diabetes incidence register with a matched case-control design, we study the effect of childhood onset diabetes on the use of antidepressant medication as an adult.
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| 87. |
- Raices Cruz, Ivette, et al.
(författare)
-
A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
- 2022
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:17, s. 3365-3379
-
Tidskriftsartikel (refereegranskat)abstract
- Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.
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| 88. |
- Raket, Lars Lau
(författare)
-
Progression models for repeated measures : Estimating novel treatment effects in progressive diseases
- 2022
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:28, s. 5537-5557
-
Tidskriftsartikel (refereegranskat)abstract
- Mixed models for repeated measures (MMRMs) are ubiquitous when analyzing outcomes of clinical trials. However, the linearity of the fixed-effect structure in these models largely restrict their use to estimating treatment effects that are defined as linear combinations of effects on the outcome scale. In some situations, alternative quantifications of treatment effects may be more appropriate. In progressive diseases, for example, one may want to estimate if a drug has cumulative effects resulting in increasing efficacy over time or whether it slows the time progression of disease. This article introduces a class of nonlinear mixed-effects models called progression models for repeated measures (PMRMs) that, based on a continuous-time extension of the categorical-time parametrization of MMRMs, enables estimation of novel types of treatment effects, including measures of slowing or delay of the time progression of disease. Compared to conventional estimates of treatment effects where the unit matches that of the outcome scale (eg, 2 points benefit on a cognitive scale), the time-based treatment effects can offer better interpretability and clinical meaningfulness (eg, 6 months delay in progression of cognitive decline). The PMRM class includes conventionally used MMRMs and related models for longitudinal data analysis, as well as variants of previously proposed disease progression models as special cases. The potential of the PMRM framework is illustrated using both simulated and historical data from clinical trials in Alzheimer's disease with different types of artificially simulated treatment effects. Compared to conventional models it is shown that PMRMs can offer substantially increased power to detect disease-modifying treatment effects where the benefit is increasing with treatment duration.
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| 89. |
- 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
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 33:9, s. 1460-1476
-
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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| 90. |
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| 91. |
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| 92. |
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| 93. |
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| 94. |
- Rompaye, Bart Van, et al.
(författare)
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Evaluating hospital performance based on excess cause-specific incidence
- 2015
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 34:8, s. 1334-1350
-
Tidskriftsartikel (refereegranskat)abstract
- Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry.
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| 95. |
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| 96. |
- Rowley, M., et al.
(författare)
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A latent class model for competing risks
- 2017
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 36:13, s. 2100-2119
-
Tidskriftsartikel (refereegranskat)abstract
- Survival data analysis becomes complex when the proportional hazards assumption is violated at population level or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, on the basis of assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent classmodels as special cases. Simulated data confirm that our approach can map a cohort's substructure and remove heterogeneity-induced informative censoring effects. Application to data from the Uppsala Longitudinal Study of Adult Men cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the Swedish Apolipoprotein Mortality Risk Study.
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| 97. |
- Ryeznik, Yevgen, et al.
(författare)
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A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios
- 2018
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 37:21, s. 3056-3077
-
Tidskriftsartikel (refereegranskat)abstract
- Randomization designs for multiarm clinical trials are increasingly used in practice, especially in phase II dose-ranging studies. Many new methods have been proposed in the literature; however, there is lack of systematic, head-to-head comparison of the competing designs. In this paper, we systematically investigate statistical properties of various restricted randomization procedures for multiarm trials with fixed and possibly unequal allocation ratios. The design operating characteristics include measures of allocation balance, randomness of treatment assignments, variations in the allocation ratio, and statistical characteristics such as type I error rate and power. The results from the current paper should help clinical investigators select an appropriate randomization procedure for their clinical trial. We also provide a web-based R shiny application that can be used to reproduce all results in this paper and run simulations under additional user-defined experimental scenarios.
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| 98. |
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| 99. |
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| 100. |
- Salim, Agus, et al.
(författare)
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Analysis of incidence and prognosis from 'extreme' case-control designs
- 2014
-
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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