A5004580198- Advanced Causal Inference Techniques
- Patient-Provider Communication in Healthcare
- Statistical Methods and Inference
- Mental Health Research Topics
- Scientific Computing and Data Management
- Statistical Methods and Bayesian Inference
- Cancer survivorship and care
- Data Analysis with R
- Meta-analysis and systematic reviews
- Trauma and Emergency Care Studies
- Sleep and related disorders
- Imbalanced Data Classification Techniques
- AI in cancer detection
- Palliative Care and End-of-Life Issues
- Health Systems, Economic Evaluations, Quality of Life
- Circadian rhythm and melatonin
- Global Cancer Incidence and Screening
- Hip and Femur Fractures
- Genetic factors in colorectal cancer
- Patient Satisfaction in Healthcare
- Statistical Methods in Clinical Trials
- Machine Learning in Healthcare
- Face and Expression Recognition
- Emergency and Acute Care Studies
- Frailty in Older Adults
Netherlands Comprehensive Cancer Organisation
2020-2024
Tilburg University
2019-2024
Results of simulation studies evaluating the performance statistical methods can have a major impact on way empirical research is implemented. However, so far there limited evidence replicability studies. Eight highly cited were selected, and their was assessed by teams replicators with formal training in quantitative methodology. The used information original publications to write code aim replicating results. primary outcome determine feasibility based reported supplementary materials....
Abstract Purpose Circadian rhythms control a wide range of physiological processes and may be associated with fatigue, depression, sleep problems. We aimed to identify subgroups breast cancer survivors based on symptoms insomnia, depression; assess whether circadian parameters (i.e., chronotype, amplitude, stability) were these over time. Methods Among survivors, usual assessed at 3–4 months after diagnosis (T0), insomnia 2–3 years (T1, N = 265) 6–8 (T2, 169). applied latent class analysis...
Abstract Background Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients facilitate shared decision making treatment options. In this pre-registered study, we assessed cancer survivors’ need generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical after diagnosis. We examined how coping style, subjective numeracy, and anxiety levels of survivors relate...
In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context shared decision making. Currently, is often not discussed, partly because (until recently) data has been lacking. our work, rely newly developed prediction model, assigns patients to scenarios. Furthermore, use techniques explain these scenario-based predictions personalized and understandable language. We...
Abstract Background Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) with similar HRQOL patterns and investigated their stability over time the association clinical covariates these classes. Materials Methods Data from population-based PROFILES registry were used. Included nonmetastatic (TNM stage I–III) (n = 1,489). was assessed Dutch translation European Organisation for Research Treatment...
Abstract Bias-adjusted three-step latent class analysis (LCA) is widely popular to relate covariates membership. However, if the causal effect of a treatment on membership interest and only observational data available, inference techniques such as inverse propensity weighting (IPW) need be used. In this article, we extend bias-adjusted LCA incorporate IPW. This approach separates estimation measurement model from using IPW for later step. Compared previous methods, solves several conceptual...
Results of simulation studies evaluating the performance statistical methods are often considered actionable and thus can have a major impact on way empirical research is implemented. However, so far there limited evidence about reproducibility replicability studies. Therefore, eight highly cited were selected, their was assessed by teams replicators with formal training in quantitative methodology. The found relevant information original publications used it to write code aim replicating...
Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it impossible to randomize membership, causal inference techniques are needed estimate effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores effect on outcome variable. Both modify bias-adjusted approach by using in last step control confounding. The first strategy utilizes...
The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect a treatment on membership even observational data.In this article, we present an extension bias-adjusted three-step LCA IPW, which accounting differential item function (DIF) caused by or exposure variable.Following approach Vermunt and Magidson, propose including its direct indicators in step-one model.In step-three model include IPW...
Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it impossible to randomize membership, causal inference techniques are needed estimate effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores effect on outcome variable. Both modify bias-adjusted approach by using in last step control confounding. The first strategy utilizes...
The parametric g-formula can be used to estimate causal effects of time-varying exposures or treatments on observable outcomes. In such a longitudinal setting, confounders that are affected by prior need adjusted for. does this specifying several models, one each for every variable, and performing micro-simulations. However, its restriction use cases outcomes limits the possibility applications in social sciences. sciences, many variables interest unobservable constructs. cases, measurement...
Bias-adjusted three-step latent class analysis (LCA) is a popular tool to relate external variables membership. The integration of causal inference techniques such as inverse propensity weighting (IPW) with LCA allows for addressing questions about the relationship between these and classes even when data collected in an observational design. However, LCA’s key assumption conditional independence indicators often violated practice. This case have direct effects on (some of) indicators, i.e.,...