| 1. |
- Ho, Peh Joo, et al.
(författare)
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Comparison of self-reported and register-based hospital medical data on comorbidities in women
- 2019
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Breast cancer patients commonly present with comorbidities which are known to influence treatment decisions and survival. We aim to examine agreement between self-reported and register-based medical records (National Patient Register [NPR]). Ascertainment of nine conditions, using individually-linked data from 64,961 women enrolled in the Swedish KARolinska MAmmography Project for Risk Prediction of Breast Cancer (KARMA) study. Agreement was assessed using observed proportion of agreement (overall agreement), expected proportion of agreement, and Cohen's Kappa statistic. Two-stage logistic regression models taking into account chance agreement were used to identify potential predictors of overall agreement. High levels of overall agreement (i.e. ≥86.6%) were observed for all conditions. Substantial agreement (Cohen's Kappa) was observed for myocardial infarction (0.74), diabetes (0.71) and stroke (0.64) between self-reported and NPR data. Moderate agreement was observed for preeclampsia (0.51) and hypertension (0.46). Fair agreement was observed for heart failure (0.40) and polycystic ovaries or ovarian cysts (0.27). For hyperlipidemia (0.14) and angina (0.10), slight agreement was observed. In most subgroups we observed negative specific agreement of >90%. There is no clear reference data source for ascertainment of conditions. Negative specific agreement between NPR and self-reported data is consistently high across all conditions.
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| 2. |
- Ning, Yilin, et al.
(författare)
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Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes
- 2020
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 29:2, s. 437-454
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Tidskriftsartikel (refereegranskat)abstract
- The rank-ordered logit (rologit) model was recently introduced as a robust approach for analysing continuous outcomes, with the linear exposure effect estimated by scaling the rank-based log-odds estimate. Here we extend the application of the rologit model to continuous outcomes with ties and ordinal outcomes treated as imperfectly-observed continuous outcomes. By identifying the functional relationship between survival times and continuous outcomes, we explicitly establish the equivalence between the rologit and Cox models to justify the use of the Breslow, Efron and perturbation methods in the analysis of continuous outcomes with ties. Using simulation, we found all three methods perform well with few ties. Although an increasing extent of ties increased the bias of the log-odds and linear effect estimates and resulted in reduced power, which was somewhat worse when the model was mis-specified, the perturbation method maintained a type I error around 5%, while the Efron method became conservative with heavy ties but outperformed Breslow. In general, the perturbation method had the highest power, followed by the Efron and then the Breslow method. We applied our approach to three real-life datasets, demonstrating a seamless analytical workflow that uses stratification for confounder adjustment in studies of continuous and ordinal outcomes.
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