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- Karlsson, Kristin E., et al.
(författare)
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Modeling Disease Progression in Acute Stroke Using Clinical Assessment Scales
- 2010
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 12:4, s. 683-691
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Tidskriftsartikel (refereegranskat)abstract
- This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.
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| 3. |
- Kjellsson, Maria C., et al.
(författare)
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Comparison of proportional odds and differential odds models for mixed-effects analysis of categorical data
- 2008
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 35:5, s. 483-501
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Tidskriftsartikel (refereegranskat)abstract
- In this work a model for analyzing categorical data is presented; the differential odds model. Unlike the commonly used proportional odds model, this model does not assume that a covariate affects all categories equally on the log odds scale. The differential odds model was compared to the proportional odds model, by assessing statistical significance and improvement of predictive performance when applying the differential odds model to data previously analyzed using the proportional odds model. Three clinical studies; 3-category T-cell receptor density data, 5-category diarrhea data and 6-category sedation data, were re-analyzed with the differential odds model. As expected, no improvements were seen with T-cell receptor density and diarrhea data. However, for the more complex measurement sedation, the differential odds model provided both statistical improvements and improvements in simulation properties. The estimated actual critical value was for all data lower than the nominal value, using the number of added parameters as the degree of freedom, i.e. the differential odds model is statistically indicated to a less extent than expected. The differential odds model had the desired property of not being indicated when not necessary, but it may provide improvements when the data does not represent a categorization of continuous data.
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- Nunes, Luís, et al.
(författare)
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Prognostic genome and transcriptome signatures in colorectal cancers
- 2024
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Ingår i: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687.
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Tidskriftsartikel (refereegranskat)abstract
- Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.
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- Zingmark, Per-Henrik, et al.
(författare)
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Modelling a spontaneously reported side effect by use of a Markov mixed-effects model.
- 2005
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 32:2, s. 261-281
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Tidskriftsartikel (refereegranskat)abstract
- Aims: To present a method for analyzing side-effect data where change in severity is spontaneouslyreported during the experiment. Methods: A clinical study in 12 healthy volunteers aimed toinvestigate the concentration-response characteristics of a CNS-specific side-effect was conducted.After an open session where the subjects experienced the side-effect and where the individualpharmacokinetic parameters were evaluated they were randomized to a sequence of three differentinfusion rates of the drug in a double-blinded crossover way. The infusion rates were individualizedto achieve the same target concentration in all subjects and different drug input rates wereselected to mimic absorption profiles from different formulations. The occurrence of the specificside-effect and any subsequent change in severity was self-reported by the subjects. Severity wasrecorded as 0 = no side-effect, 1 = mild side-effect and 2 = moderate or severe side-effect.Results: The side-effect data were analyzed using a mixed-effects model for ordered categoricaldata with and without Markov elements. The former model estimated the probability of having acertain side-effect score conditioned on the preceding observation and drug exposure. The observednumbers of transitions between scores were from 0 ->1: 24, from 0 ->2: 11, from 1 ->2: 23, from2 ->1: 1, from 2 ->0: 32 and from 1 ->0: 2. The side-effect model consisted of an effect-compartmentmodel with a tolerance compartment. The predictive performance of the Markov model wasinvestigated by a posterior predictive check (PPC), where 100 datasets were simulated from thefinal model. Average number of the different transitions from the PPC was from 0 ->1: 26, from0 ->2: 11, from 1 ->2: 25, from 2 ->1: 1, from 2 ->0: 35 and from 1 ->0: 1. A similar PPCfor the model without Markov elements was at considerable disparity with the data. Conclusion:This approach of incorporating Markov elements in an analysis of spontaneously reported categoricalside-effect data could adequately predict the observed side-effect time course and could beconsidered in analyses of categorical data where dependence between observations is an issue.
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| 10. |
- Zingmark, Per-Henrik, 1972-
(författare)
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Models for Ordered Categorical Pharmacodynamic Data
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.
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