Friedo Dekker - Academia.edu (original) (raw)
Papers by Friedo Dekker
Nephrology Dialysis Transplantation, Jan 14, 2017
Cross-sectional studies represent the second line of evidence (after case reports) in the ladder ... more Cross-sectional studies represent the second line of evidence (after case reports) in the ladder of evidence aimed at defining disease aetiology. This study design is used to generate hypotheses about the determinants of a given disease but also to investigate the accuracy of diagnostic tests and to assess the burden of a given disease in a population. The intrinsic limitation of cross-sectional studies, when applied to generate aetiological hypotheses, is that both the exposure under investigation and the disease of interest are measured at the same point in time. For this reason, generally the cross-sectional design does not provide definitive proofs about cause-and-effect relationships. An advantage of cross-sectional studies in aetiological and diagnostic research is that they allow researchers to consider many different putative risk factors/diagnostic markers at the same time. For example, in a hypothetical study aimed at generating hypotheses about the risk factors for left ventricular hypertrophy (LVH) in patients with chronic kidney disease, investigators could look at several risk factors as potential determinants of LVH (age, gender, cholesterol, blood pressure, inflammation, etc.) with minimal or no additional costs. In this article, we make examples derived from the nephrology literature to show the usefulness of cross-sectional studies in clinical and epidemiological research.
Nephrology Dialysis Transplantation, Jul 24, 2012
Nephrology Dialysis Transplantation, May 22, 2018
Clinical epidemiological studies often focus on investigating the underlying causes of disease. F... more Clinical epidemiological studies often focus on investigating the underlying causes of disease. For instance, a nephrologist may be interested in the association between blood pressure and the development of chronic kidney disease (CKD). However, instead of focusing on the mere occurrence of CKD, the decline of kidney function over time might be the outcome of interest. For examining this kidney function trajectory, patients are typically followed over time with their kidney function estimated at several time points. During follow-up, some patients may drop out earlier than others and for different reasons. Furthermore, some patients may have greater kidney function at study entry or faster kidney function decline than others. Also, a substantial heterogeneity may exist in the number of kidney function estimates available for each patient. This heterogeneity with respect to kidney function, dropout and number of kidney function estimates is important to take into account when estimating kidney function trajectories. In general, two methods are used in the literature to estimate kidney function trajectories over time: linear regression to estimate individual slopes and the linear mixed-effects model (LMM), i.e. repeated measures analysis. Importantly, the linear regression method does not properly take into account the above-mentioned heterogeneity, whereas the LMM is able to retain all information and variability in the data. However, the underlying concepts, use and interpretation of LMMs are not always straightforward. Therefore we illustrate this using a clinical example and offer a framework of how to model and interpret the LMM.
Nephrology Dialysis Transplantation, Sep 10, 2010
Kidney International, Aug 1, 2007
Nephrology Dialysis Transplantation, Feb 18, 2010
Kidney International, Apr 1, 2008
Kidney International, Oct 1, 2008
Nephron Clinical Practice, 2011
Nephrology Dialysis Transplantation, Nov 25, 2011
Kidney International, Dec 1, 2008
Nephrology Dialysis Transplantation, Feb 27, 2017
Kidney International, Aug 1, 2009
Kidney International, Nov 1, 2008
Kidney International, Apr 1, 2009
Nephron Clinical Practice, Apr 21, 2010
Nephron Clinical Practice, Jul 28, 2010
Nephrology Dialysis Transplantation, Nov 8, 2010
Kaplan-Meier analysis is a popular method used for analysing time-to-event data. In case of compe... more Kaplan-Meier analysis is a popular method used for analysing time-to-event data. In case of competing event analyses such as that of cardiovascular and non-cardiovascular mortality, however, the Kaplan-Meier method profoundly overestimates the cumulative mortality probabilities for each of the separate causes of death. This article provides an introduction to the problem of competing events in Kaplan-Meier analysis. It explains cumulative incidence competing risk analysis and demonstrates on a cohort of elderly dialysis patients that, in contrast to the Kaplan-Meier method, application of this method yields unbiased estimates of the cumulative probabilities for cause-specific mortality.
Kidney International, Oct 1, 2009
Nephron Clinical Practice, Sep 11, 2009
Nephrology Dialysis Transplantation, Jan 14, 2017
Cross-sectional studies represent the second line of evidence (after case reports) in the ladder ... more Cross-sectional studies represent the second line of evidence (after case reports) in the ladder of evidence aimed at defining disease aetiology. This study design is used to generate hypotheses about the determinants of a given disease but also to investigate the accuracy of diagnostic tests and to assess the burden of a given disease in a population. The intrinsic limitation of cross-sectional studies, when applied to generate aetiological hypotheses, is that both the exposure under investigation and the disease of interest are measured at the same point in time. For this reason, generally the cross-sectional design does not provide definitive proofs about cause-and-effect relationships. An advantage of cross-sectional studies in aetiological and diagnostic research is that they allow researchers to consider many different putative risk factors/diagnostic markers at the same time. For example, in a hypothetical study aimed at generating hypotheses about the risk factors for left ventricular hypertrophy (LVH) in patients with chronic kidney disease, investigators could look at several risk factors as potential determinants of LVH (age, gender, cholesterol, blood pressure, inflammation, etc.) with minimal or no additional costs. In this article, we make examples derived from the nephrology literature to show the usefulness of cross-sectional studies in clinical and epidemiological research.
Nephrology Dialysis Transplantation, Jul 24, 2012
Nephrology Dialysis Transplantation, May 22, 2018
Clinical epidemiological studies often focus on investigating the underlying causes of disease. F... more Clinical epidemiological studies often focus on investigating the underlying causes of disease. For instance, a nephrologist may be interested in the association between blood pressure and the development of chronic kidney disease (CKD). However, instead of focusing on the mere occurrence of CKD, the decline of kidney function over time might be the outcome of interest. For examining this kidney function trajectory, patients are typically followed over time with their kidney function estimated at several time points. During follow-up, some patients may drop out earlier than others and for different reasons. Furthermore, some patients may have greater kidney function at study entry or faster kidney function decline than others. Also, a substantial heterogeneity may exist in the number of kidney function estimates available for each patient. This heterogeneity with respect to kidney function, dropout and number of kidney function estimates is important to take into account when estimating kidney function trajectories. In general, two methods are used in the literature to estimate kidney function trajectories over time: linear regression to estimate individual slopes and the linear mixed-effects model (LMM), i.e. repeated measures analysis. Importantly, the linear regression method does not properly take into account the above-mentioned heterogeneity, whereas the LMM is able to retain all information and variability in the data. However, the underlying concepts, use and interpretation of LMMs are not always straightforward. Therefore we illustrate this using a clinical example and offer a framework of how to model and interpret the LMM.
Nephrology Dialysis Transplantation, Sep 10, 2010
Kidney International, Aug 1, 2007
Nephrology Dialysis Transplantation, Feb 18, 2010
Kidney International, Apr 1, 2008
Kidney International, Oct 1, 2008
Nephron Clinical Practice, 2011
Nephrology Dialysis Transplantation, Nov 25, 2011
Kidney International, Dec 1, 2008
Nephrology Dialysis Transplantation, Feb 27, 2017
Kidney International, Aug 1, 2009
Kidney International, Nov 1, 2008
Kidney International, Apr 1, 2009
Nephron Clinical Practice, Apr 21, 2010
Nephron Clinical Practice, Jul 28, 2010
Nephrology Dialysis Transplantation, Nov 8, 2010
Kaplan-Meier analysis is a popular method used for analysing time-to-event data. In case of compe... more Kaplan-Meier analysis is a popular method used for analysing time-to-event data. In case of competing event analyses such as that of cardiovascular and non-cardiovascular mortality, however, the Kaplan-Meier method profoundly overestimates the cumulative mortality probabilities for each of the separate causes of death. This article provides an introduction to the problem of competing events in Kaplan-Meier analysis. It explains cumulative incidence competing risk analysis and demonstrates on a cohort of elderly dialysis patients that, in contrast to the Kaplan-Meier method, application of this method yields unbiased estimates of the cumulative probabilities for cause-specific mortality.
Kidney International, Oct 1, 2009
Nephron Clinical Practice, Sep 11, 2009