Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the ...counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.
We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.
We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.
To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Competing events are often ignored in epidemiological studies. Conventional methods for the analysis of survival data assume independent or noninformative censoring, which is violated when subjects ...that experience a competing event are censored. Because many survival studies do not apply competing risk analysis, we explain and illustrate in a nonmathematical way how to analyze and interpret survival data in the presence of competing events.
Using data from the Longitudinal Aging Study Amsterdam, both marginal analyses (Kaplan–Meier method and Cox proportional-hazards regression) and competing risk analyses (cumulative incidence function CIF, cause-specific and subdistribution hazard regression) were performed. We analyzed the association between sex and depressive symptoms, in which death before the onset of depression was a competing event.
The Kaplan–Meier method overestimated the cumulative incidence of depressive symptoms. Instead, the CIF should be used. As the subdistribution hazard model has a one-to-one relation with the CIF, it is recommended for prediction research, whereas the cause-specific hazard model is recommended for etiologic research.
When competing risks are present, the type of research question guides the choice of the analytical model to be used. In any case, results should be presented for all event types.
Abstract
Background
Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing electronic health records ...(EHRs) provide opportunities but up to now showed limited clinical value as risk stratification tool, because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHRs and to internally validate its predictive performance.
Methods
We used EHR data of individuals aged 65 or older. Age, sex, history of falls, medications, and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration.
Results
Data of 36 470 eligible participants were extracted from the data set. The number of participants who fell at least once was 4 778 (13.1%). The final prediction model included age, sex, history of falls, 2 medications, and 5 medical conditions. The model had a median area under the receiver operating curve of 0.705 (interquartile range 0.700–0.714).
Conclusions
Our prediction model to identify older people at high risk for falls achieved fair discrimination and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.
Abstract Purpose The aim of this study was to examine the longitudinal association between educational level and frailty prevalence in older adults and to investigate the role of material, ...biomedical, behavioral, social, and mental factors in explaining this association. Methods Data over a period of 13 years were used from the Longitudinal Aging Study Amsterdam. The study sample consisted of older adults aged 65 years and above at baseline ( n = 1205). Frailty was assessed using Fried's frailty criteria. A relative index of inequality was calculated for the level of education. Longitudinal logistic regression analyses based on multilevel modeling were performed. Results Older adults with a low educational level had higher odds of being frail compared with those with a high educational level (relative index of inequality odds ratio, 2.94; 95% confidence interval, 1.84–4.71). These differences persisted during 13 years of follow-up. Adjustment for all explanatory factors reduced the effect of educational level on frailty by 76%. Income, self-efficacy, cognitive impairment, obesity, and number of chronic diseases had the largest individual contribution in reducing the effect. Social factors had no substantial contribution. Conclusions Our findings highlight the need for a multidimensional approach in developing interventions aimed at reducing frailty, especially in lower educated groups.
Handling missing data in clinical research Heymans, Martijn W.; Twisk, Jos W.R.
Journal of clinical epidemiology,
November 2022, 2022-11-00, 20221101, Letnik:
151
Journal Article
Recenzirano
Odprti dostop
Because missing data are present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either ...completely at random (MCAR), at random (MAR), or not at random (MNAR). When missing data are MCAR, a complete case analysis can be valid. Also when missing data are MAR, in some situations a complete case analysis leads to valid results. However, in most situations, missing data imputation should be used. Regarding imputation methods, it is highly advised to use multiple imputations because multiple imputations lead to valid estimates including the uncertainty about the imputed values. When missing data are MNAR, also multiple imputations do not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data are MAR or MNAR. Finally, it should be realized that preventing to have missing data is always better than the treatment of missing data.
Background Male sex is an independent predictor of worse survival in pulmonary arterial hypertension (PAH). This finding might be explained by more severe pulmonary vascular disease, worse right ...ventricular (RV) function, or different response to therapy. The aim of this study was to investigate the underlying cause of sex differences in survival in patients treated for PAH. Methods This was a retrospective cohort study of 101 patients with PAH (82 idiopathic, 15 heritable, four anorexigen associated) who were diagnosed at VU University Medical Centre between February 1999 and January 2011 and underwent right-sided heart catheterization and cardiac MRI to assess RV function. Change in pulmonary vascular resistance (PVR) was taken as a measure of treatment response in the pulmonary vasculature, whereas change in RV ejection fraction (RVEF) was used to assess RV response to therapy. Results PVR and RVEF were comparable between men and women at baseline; however, male patients had a worse transplant-free survival compared with female patients ( P = .002). Although male and female patients showed a similar reduction in PVR after 1 year, RVEF improved in female patients, whereas it deteriorated in male patients. In a mediator analysis, after correcting for confounders, 39.0% of the difference in transplant-free survival between men and women was mediated through changes in RVEF after initiating PAH medical therapies. Conclusions This study suggests that differences in RVEF response with initiation of medical therapy in idiopathic PAH explain a significant portion of the worse survival seen in men.
Logistic regression is often used for mediation analysis with a dichotomous outcome. However, previous studies showed that the indirect effect and proportion mediated are often affected by a change ...of scales in logistic regression models. To circumvent this, standardization has been proposed. The aim of this study was to show the relative performance of the unstandardized and standardized estimates of the indirect effect and proportion mediated based on multiple regression, structural equation modeling, and the potential outcomes framework for mediation models with a dichotomous outcome.
We compared the performance of the effect estimates yielded by the three methods using a simulation study and two real-life data examples from an observational cohort study (n = 360).
Lowest bias and highest efficiency were observed for the estimates from the potential outcomes framework and for the crude indirect effect ab and the proportion mediated ab/(ab + c') based on multiple regression and SEM.
We advise the use of either the potential outcomes framework estimates or the ab estimate of the indirect effect and the ab/(ab + c') estimate of the proportion mediated based on multiple regression and SEM when mediation analysis is based on logistic regression. Standardization of the coefficients prior to estimating the indirect effect and the proportion mediated may not increase the performance of these estimates.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to ...compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model.
PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times.
The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm
. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3%
58.0% and 3-year OS 51.5%
66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities.
A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.
Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin's Rules (RR) are easily applied to pool parameter estimates. In a ...logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model, different methods are available. For example pooling chi-square tests with multiple degrees of freedom, pooling likelihood ratio test statistics, and pooling based on the covariance matrix of the regression model. These methods are more complex than RR and are not available in all mainstream statistical software packages. In addition, they do not always obtain optimal power levels. We argue that the median of the p-values from the overall significance tests from the analyses on the imputed datasets can be used as an alternative pooling rule for categorical variables. The aim of the current study is to compare different methods to test a categorical variable for significance after multiple imputation on applicability and power.
In a large simulation study, we demonstrated the control of the type I error and power levels of different pooling methods for categorical variables.
This simulation study showed that for non-significant categorical covariates the type I error is controlled and the statistical power of the median pooling rule was at least equal to current multiple parameter tests. An empirical data example showed similar results.
It can therefore be concluded that using the median of the p-values from the imputed data analyses is an attractive and easy to use alternative method for significance testing of categorical variables.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives The purpose of this study was to examine the relationship between changes in pulmonary vascular resistance (PVR) and right ventricular ejection fraction (RVEF) and survival in patients ...with pulmonary arterial hypertension (PAH) under PAH-targeted therapies. Background Despite the fact that medical therapies reduce PVR, the prognosis of patients with PAH is still poor. The primary cause of death is right ventricular (RV) failure. One possible explanation for this apparent paradox is the fact that a reduction in PVR is not automatically followed by an improvement in RV function. Methods A cohort of 110 patients with incident PAH underwent baseline right heart catheterization, cardiac magnetic resonance imaging, and 6-min walk testing. These measurements were repeated in 76 patients after 12 months of therapy. Results Two patients underwent lung transplantation, 13 patients died during the first year, and 17 patients died in the subsequent follow-up of 47 months. Baseline RVEF (hazard ratio HR: 0.938; p = 0.001) and PVR (HR: 1.001; p = 0.031) were predictors of mortality. During the first 12 months, changes in PVR were moderately correlated with changes in RVEF (R = 0.330; p = 0.005). Changes in RVEF (HR: 0.929; p = 0.014) were associated with survival, but changes in PVR (HR: 1.000; p = 0.820) were not. In 68% of patients, PVR decreased after medical therapy. Twenty-five percent of those patients with decreased PVR showed a deterioration of RV function and had a poor prognosis. Conclusions After PAH-targeted therapy, RV function can deteriorate despite a reduction in PVR. Loss of RV function is associated with a poor outcome, irrespective of any changes in PVR.
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