Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time-dependent cause-specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem-cell transplanted patients, where results from cause-specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a time-averaged effect on the cumulative event probability scale.
Hypoxaemic burden was a robust and independent predictor of all-cause mortality in chronic stable HF-REF patients. Whether or not targeting nocturnal hypoxaemia is associated with beneficial effects on mortality in HF-REF patients remains to be determined.
BackgroundHypertension is one of the key factors causing cardiovascular diseases. A substantial proportion of treated hypertensive patients do not reach recommended target blood pressure values. Shared decision making (SDM) is to enhance the active role of patients. As until now there exists little information on the effects of SDM training in antihypertensive therapy, we tested the effect of an SDM training programme for general practitioners (GPs). Our hypotheses are that this SDM training (1) enhances the participation of patients and (2) leads to an enhanced decrease in blood pressure (BP) values, compared to patients receiving usual care without prior SDM training for GPs.MethodsThe study was conducted as a cluster randomised controlled trial (cRCT) with GP practices in Southwest Germany. Each GP practice included patients with treated but uncontrolled hypertension and/or with relevant comorbidity. After baseline assessment (T0) GP practices were randomly allocated into an intervention and a control arm. GPs of the intervention group took part in the SDM training. GPs of the control group treated their patients as usual. The intervention was blinded to the patients. Primary endpoints on patient level were (1) change of patients’ perceived participation (SDM-Q-9) and (2) change of systolic BP (24h-mean). Secondary endpoints were changes of (1) diastolic BP (24h-mean), (2) patients’ knowledge about hypertension, (3) adherence (MARS-D), and (4) cardiovascular risk score (CVR).ResultsIn total 1357 patients from 36 general practices were screened for blood pressure control by ambulatory blood pressure monitoring (ABPM). Thereof 1120 patients remained in the study because of uncontrolled (but treated) hypertension and/or a relevant comorbidity. At T0 the intervention group involved 17 GP practices with 552 patients and the control group 19 GP practices with 568 patients. The effectiveness analysis could not demonstrate a significant or relevant effect of the SDM training on any of the endpoints.ConclusionThe study hypothesis that the SDM training enhanced patients’ perceived participation and lowered their BP could not be confirmed. Further research is needed to examine the impact of patient participation on the treatment of hypertension in primary care.Trial registrationGerman Clinical Trials Register (DRKS): DRKS00000125
Background and Purpose-Ischemic strokes with motor deficits lead to widespread changes in neural activity and interregional coupling between primary and secondary motor areas. Compared with frontal circuits, the knowledge is still limited to what extent parietal cortices and their interactions with frontal motor areas undergo plastic changes and might contribute to residual motor functioning after stroke. Methods-Fifteen well-recovered patients were evaluated 3 months after stroke by means of functional magnetic resonance imaging while performing visually guided hand grips with their paretic hand. Dynamic causal modeling was used to investigate task-related effective connectivity between ipsilesional posterior parietal regions along the intraparietal sulcus and frontal key motor areas, such as the primary motor cortex, the ventral premotor cortex, and the supplementary motor area. Results-Compared with healthy controls of similar age and sex, we observed significantly enhanced reciprocal facilitatory connectivity between the primary motor cortex and the anterior intraparietal sulcus of the ipsilesional hemisphere. Beyond that and as a fingerprint of excellent recovery, the coupling pattern of the parietofrontal network was near-normal. An association between coupling parameters and clinical scores was not detected. Conclusions-The
In many areas of science where empirical data are analyzed, a task is often to identify important variables with influence on an outcome. Most often this is done by using a variable selection strategy in the context of a multivariable regression model. Using a study on ozone effects in children (n = 496, 24 covariates), we will discuss aspects relevant for deriving a suitable model. With an emphasis on model stability, we will explore and illustrate differences between predictive models and explanatory models, the key role of stopping criteria, and the value of bootstrap resampling (with and without replacement). Bootstrap resampling will be used to assess variable selection stability, to derive a predictor that incorporates model uncertainty, check for influential points, and visualize the variable selection process. For the latter two tasks we adapt and extend recent approaches, such as stability paths, to serve our purposes. Based on earlier experiences and on results from the example, we will argue for simpler models and that predictions are usually very similar, irrespective of the selection method used. Important differences exist for the corresponding variances, and the model uncertainty concept helps to protect against serious underestimation of the variance of a predictor-derived data dependently. Results of stability investigations illustrate severe difficulties in the task of deriving a suitable explanatory model. It seems possible to identify a small number of variables with an important and probably true influence on the outcome, but too often several variables are included whose selection may be a result of chance or may depend on a small number of observations.
Plant growth and fertility strongly depend on environmental conditions such as temperature. Remarkably, temperature also influences meiotic recombination and thus, the current climate change will affect the genetic make-up of plants. To better understand the effects of temperature on meiosis, we followed male meiocytes in Arabidopsis thaliana by live cell imaging under three temperature regimes: at 21°C; at heat shock conditions of 30°C and 34°C; after an acclimatization phase of 1 week at 30°C. This work led to a cytological framework of meiotic progression at elevated temperature. We determined that an increase from 21°C to 30°C speeds up meiosis with specific phases being more amenable to heat than others. An acclimatization phase often moderated this effect. A sudden increase to 34°C promoted a faster progression of early prophase compared to 21°C. However, the phase in which cross-overs mature was prolonged at 34°C. Since mutants involved in the recombination pathway largely did not show the extension of this phase at 34°C, we conclude that the delay is recombination-dependent. Further analysis also revealed the involvement of the ATAXIA TELANGIECTASIA MUTATED kinase in this prolongation, indicating the existence of a pachytene checkpoint in plants, yet in a specialized form.
The focus of many medical applications is to model the impact of several factors on time to an event. A standard approach for such analyses is the Cox proportional hazards model. It assumes that the factors act linearly on the log hazard function (linearity assumption) and that their effects are constant over time (proportional hazards (PH) assumption). Variable selection is often required to specify a more parsimonious model aiming to include only variables with an influence on the outcome. As follow-up increases the effect of a variable often gets weaker, which means that it varies in time. However, spurious time-varying effects may also be introduced by mismodelling other parts of the multivariable model, such as omission of an important covariate or an incorrect functional form of a continuous covariate. These issues interact. To check whether the effect of a variable varies in time several tests for non-PH have been proposed. However, they are not sufficient to derive a model, as appropriate modelling of the shape of time-varying effects is required. In three examples we will compare five recently published strategies to assess whether and how the effects of covariates from a multivariable model vary in time. For practical use we will give some recommendations.
A timely cardiac MRI is the most sensitive investigation for the identification of early myocardial changes in DMD which is a prerequisite for early interventions and therapeutic strategies.
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