Observational pharmacoepidemiological studies using routinely collected healthcare data are increasingly being used in the field of nephrology to answer questions on the effectiveness and safety of... Show moreObservational pharmacoepidemiological studies using routinely collected healthcare data are increasingly being used in the field of nephrology to answer questions on the effectiveness and safety of medications. This review discusses a number of biases that may arise in such studies and proposes solutions to minimize them during the design or statistical analysis phase. We first describe designs to handle confounding by indication (e.g. active comparator design) and methods to investigate the influence of unmeasured confounding, such as the E-value, the use of negative control outcomes and control cohorts. We next discuss prevalent user and immortal time biases in pharmacoepidemiology research and how these can be prevented by focussing on incident users and applying either landmarking, using a time-varying exposure, or the cloning, censoring and weighting method. Lastly, we briefly discuss the common issues with missing data and misclassification bias. When these biases are properly accounted for, pharmacoepidemiological observational studies can provide valuable information for clinical practice. Show less
Roumeliotis, S.; Abd ElHafeez, S.; Jager, K.J.; Dekker, F.W.; Stel, V.S.; Pitino, A.; ... ; Tripepi, G. 2021
Ecological studies are observational studies commonly used in public health research. The main characteristic of this study design is that the statistical analysis is based on pooled (i.e.,... Show moreEcological studies are observational studies commonly used in public health research. The main characteristic of this study design is that the statistical analysis is based on pooled (i.e., aggregated) rather than on individual data. Thus, patient-level information such as age, gender, income and disease condition are not considered as individual characteristics but as mean values or frequencies, calculated at country or community level. Ecological studies can be used to compare the aggregated prevalence and incidence data of a given condition across different geographical areas, to assess time-related trends of the frequency of a pre-defined disease/condition, to identify factors explaining changes in health indicators over time in specific populations, to discriminate genetic from environmental causes of geographical variation in disease, or to investigate the relationship between a population-level exposure and a specific disease or condition. The major pitfall in ecological studies is the ecological fallacy, a bias which occurs when conclusions about individuals are erroneously deduced from results about the group to which those individuals belong. In this paper, by using a series of examples, we provide a general explanation of the ecological studies and provide some useful elements to recognize or suspect ecological fallacy in this type of studies. Show less
Vries, B.B.L.P. de; Smeden, M. van; Groenwold, R.H.H. 2021
Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum... Show moreJoint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425-436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor). Show less
In evidence-based medicine, clinical research questions may be addressed by different study designs. This article describes when randomized controlled trials (RCT) are needed and when observational... Show moreIn evidence-based medicine, clinical research questions may be addressed by different study designs. This article describes when randomized controlled trials (RCT) are needed and when observational studies are more suitable. According to the Centre for Evidence-Based Medicine, study designs can be divided into analytic and non-analytic (descriptive) study designs. Analytic studies aim to quantify the association of an intervention (eg, treatment) or a naturally occurring exposure with an outcome. They can be subdivided into experimental (ie, RCT) and observational studies. The RCT is the best study design to evaluate theintendedeffect of an intervention, because the randomization procedure breaks the link between the allocation of the intervention and patient prognosis. If the randomization of the intervention or exposure is not possible, one needs to depend on observational analytic studies, but these studies usually suffer from bias and confounding. If the study focuses onunintendedeffects of interventions (ie, effects of an intervention that are not intended or foreseen), observational analytic studies are the most suitable study designs, provided that there is no link between the allocation of the intervention and the unintended effect. Furthermore, non-analytic studies (ie, descriptive studies) also rely on observational study designs. In summary, RCTs and observational study designs are inherently different, and depending on the study aim, they each have their own strengths and weaknesses. Show less
Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been... Show moreProper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients. Show less
Cnossen, M.C.; Essen, T.A. van; Ceyisakar, I.E.; Polinder, S.; Andriessen, T.M.; Naalt, J. van der; ... ; Lingsma, H.F. 2018
