Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Currently, platelet transfusion is the main treatment for patients with thrombocytopenia due to haematological malignancy and intensive chemotherapy. When the platelet count is low, transfusion is...Show moreCurrently, platelet transfusion is the main treatment for patients with thrombocytopenia due to haematological malignancy and intensive chemotherapy. When the platelet count is low, transfusion is given to prevent bleedings. However, the platelet count is not the only determinant of bleeding (Ypma et al., 2019). Other biomarkers might additionally or even better predict bleeding such as the albumincreatinine ratio measured in urine. This thesis project will determine the predictive value of these new biomarkers where we would like to predict the ”untreated risk” of bleeding: the risk of bleeding if patients would not receive a transfusion. We used a real dataset that contains 88 patients with 116 thrombocytopenic episodes in which patients’ platelet counts are low and they may develop a bleeding. A problem is that the patients who received transfusions cause diculty in predicting the “untreated risk”. Another problem is that transfusions were given partly based on the platelet counts, which makes the e↵ect of transfusion on bleeding confounded by platelet count. We considered two situations. One was to predict the bleeding during the day based on the platelet count that was measured in the morning (the one-day situation). The two-day situation was to predict bleeding in the next two days, but before the second day-night based on the platelet count that was measured on the first day morning. In the first part of this thesis, we structured the relationship between biomarkers, transfusions and bleeding by expressing them in causal diagrams. Using the causal diagrams, we found the reason why the conventional models failed to predict untreated risk in the two-day situation. We found that the marginal structural model might be a solution. In the second part, we set up a simulation to verify whether the marginal structural model or conventional regression models can handle the confounding in the one-day situation and the time-dependent confounding in the two-day situation. Based on our simulation studies, we concluded that for the one-day situation the regression model including treatment and predictor was well equipped, while in the two-day situation the marginal structure model is recommended to estimate the “untreated” risk. In the third part, we applied the models to the dataset. We found that in the one-day situation urine albumin/creatinine ratio and the platelet count have potential predictive value for predicting same day bleeding, while, for the two-day situation, only the urine albumin/creatinine ratio was significantly associated with the risk of bleeding in all models. Additionally, there was not a clear e↵ect of transfusion detected in the one-day situation and two-day situation.Show less
