| 1. |
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| 2. |
- Buatois, Simon, et al.
(author)
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cLRT-Mod : An efficient methodology for pharmacometric model-based analysis of longitudinal phase II dose finding studies under model uncertainty
- 2021
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 40:10, s. 2435-2451
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Journal article (peer-reviewed)abstract
- Within the challenging context of phase II dose-finding trials, longitudinal analyses may increase drug effect detection power compared to an end-of-treatment analysis. This work proposes cLRT-Mod, a pharmacometric adaptation of the MCP-Mod methodology, which allows the use of nonlinear mixed effect models to first detect a dose-response signal and then identify the doses for the confirmatory phase while accounting for model structure uncertainty. The method was evaluated through extensive clinical trial simulations of a hypothetical phase II dose-finding trial using different scenarios and comparing different methods such as MCP-Mod. The results show an increase in power using cLRT with longitudinal data compared to an EOT multiple contrast tests for scenarios with small sample size and weak drug effect while maintaining pre-specifiability of the models prior to data analysis and the nominal type I error. This work shows how model averaging provides better coverage probability of the drug effect in the prediction step, and avoids under-estimation of the size of the confidence interval. Finally, for illustration purpose cLRT-Mod was applied to the analysis of a real phase II dose-finding trial.
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| 3. |
- Ciocanea-Teodorescu, Iuliana, et al.
(author)
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Causal inference in survival analysis under deterministic missingness of confounders in register data
- 2023
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 42:12, s. 1946-1964
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Journal article (peer-reviewed)abstract
- Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.
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| 8. |
- Fonseca-Rodríguez, Osvaldo, et al.
(author)
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Avoiding bias in self-controlled case series studies of coronavirus disease 2019
- 2021
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 40:27, s. 6197-6208
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Journal article (peer-reviewed)abstract
- Many studies, including self-controlled case series (SCCS) studies, are being undertaken to quantify the risks of complications following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19). One such SCCS study, based on all COVID-19 cases arising in Sweden over an 8-month period, has shown that SARS-CoV-2 infection increases the risks of AMI and ischemic stroke. Some features of SARS-CoV-2 infection and COVID-19, present in this study and likely in others, complicate the analysis and may introduce bias. In the present paper we describe these features, and explore the biases they may generate. Motivated by data-based simulations, we propose methods to reduce or remove these biases.
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| 9. |
- Gabriel, Erin E., et al.
(author)
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Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
- 2024
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:3, s. 534-547
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Journal article (peer-reviewed)abstract
- There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.
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| 10. |
- Goetghebeur, Els, et al.
(author)
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Formulating causal questions and principled statistical answers
- 2020
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In: Statistics in Medicine. - : WILEY. - 0277-6715 .- 1097-0258. ; 39:30, s. 4922-4948
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Journal article (peer-reviewed)abstract
- Although review papers on causal inference methods are now available, there is a lack of introductory overviews onwhatthey can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on , where SAS and Stata code for analysis is also provided.
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| 11. |
- Konstantinou, Konstantinos, 1993, et al.
(author)
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Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality
- 2023
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In: Statistics in Medicine. - 0277-6715 .- 1097-0258. ; 42:23, s. 4128-4146
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Journal article (peer-reviewed)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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| 12. |
- Konstantionu, Konstantinos, et al.
(author)
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Spatial modeling of epidermal nerve fiber patterns
- 2021
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:29, s. 6479-6500
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Journal article (peer-reviewed)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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| 13. |
- Kühnel, Line, et al.
(author)
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Simultaneous modeling of Alzheimer's disease progression via multiple cognitive scales
- 2021
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:14, s. 3251-3266
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Journal article (peer-reviewed)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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| 14. |
- Kuronen, M., et al.
(author)
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Point process models for sweat gland activation observed with noise
- 2021
-
In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:8, s. 2055-2072
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Journal article (peer-reviewed)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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| 17. |
- Nyberg, Joakim, et al.
(author)
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Properties of the full random-effect modeling approach with missing covariate data
- 2024
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:5, s. 935-952
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Journal article (peer-reviewed)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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| 18. |
- Olarte Parra, Camila, et al.
(author)
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Trial emulation and survival analysis for disease incidence registers : A case study on the causal effect of pre-emptive kidney transplantation
- 2022
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 41:21, s. 4176-4199
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Journal article (peer-reviewed)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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| 19. |
- Raices Cruz, Ivette, et al.
(author)
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A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
- 2022
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:17, s. 3365-3379
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Journal article (peer-reviewed)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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| 20. |
- Raket, Lars Lau
(author)
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Progression models for repeated measures : Estimating novel treatment effects in progressive diseases
- 2022
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:28, s. 5537-5557
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Journal article (peer-reviewed)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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| 21. |
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| 22. |
- Sheen, Justin K., et al.
(author)
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The required size of cluster randomized trials of nonpharmaceutical interventions in epidemic settings
- 2022
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:13, s. 2466-2482
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Journal article (peer-reviewed)abstract
- To control the SARS-CoV-2 pandemic and future pathogen outbreaks requires an understanding of which nonpharmaceutical interventions are effective at reducing transmission. Observational studies, however, are subject to biases that could erroneously suggest an impact on transmission, even when there is no true effect. Cluster randomized trials permit valid hypothesis tests of the effect of interventions on community transmission. While such trials could be completed in a relatively short period of time, they might require large sample sizes to achieve adequate power. However, the sample sizes required for such tests in outbreak settings are largely undeveloped, leaving unanswered the question of whether these designs are practical. We develop approximate sample size formulae and simulation-based sample size methods for cluster randomized trials in infectious disease outbreaks. We highlight key relationships between characteristics of transmission and the enrolled communities and the required sample sizes, describe settings where trials powered to detect a meaningful true effect size may be feasible, and provide recommendations for investigators in planning such trials. The approximate formulae and simulation banks may be used by investigators to quickly assess the feasibility of a trial, followed by more detailed methods to more precisely size the trial. For example, we show that community-scale trials requiring 220 clusters with 100 tested individuals per cluster are powered to identify interventions that reduce transmission by 40% in one generation interval, using parameters identified for SARS-CoV-2 transmission. For more modest treatment effects, or when transmission is extremely overdispersed, however, much larger sample sizes are required.
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| 23. |
- Signorelli, M., et al.
(author)
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Penalized regression calibration : A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data
- 2021
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 40:27, s. 6178-6196
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Journal article (peer-reviewed)abstract
- Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.
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