Background and objectives:
Prediction models are useful tools in clinical practise by providing predictive estimates of
outcome probabilities to aid in decision making. As biomedical research advances, concerns
have been raised regarding combined effectiveness (benefit) and safety (harm) outcomes in a
prediction model, while typically different prediction models only focus on predictions of
separate outcomes. A second issue is that, evidence also reveals poor predictive accuracy in
different populations and settings for some prediction models, requiring model calibration or
redevelopment. A third issue in data analyses is whether the treatment effect estimates could
be influenced by competing risk bias. If other events preclude the outcomes of interest, these
events would compete with the outcomes and thus fundamentally change the probability of
the outcomes of interest. Failure to recognize the existence of competing risk or to account
for it may result in misleading conclusions in health research. Therefore in this thesis, we
explored three methodological issues in prediction models and data analyses by: (1)
developing and externally validating a prediction model for patients’ individual combined
benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke,
neither event, or both stroke and major bleeding) with and without warfarin therapy for atrial
fibrillation; (2) constructing a prediction model for hospital mortality in medical-surgical
critically ill patients; and (3) performing a competing risk analysis to assess the efficacy of
the low molecular weight heparin dalteparin versus unfractionated heparin in venous
thromboembolism in medical-surgical critically ill patients.
Methods:
Project 1: Using the Kaiser Permanente Colorado (KPCO) anticoagulation management
cohort in the Denver-Boulder metropolitan area of Colorado in the United States to include
patients with AF who were and were not prescribed warfarin therapy, we used a new
approach to build a prediction model of individual combined benefit and harm outcomes
related to warfarin therapy (stroke with no major bleeding, major bleeding with no stroke, neither event, or both stroke and major bleeding) in patients with AF. We utilized a
polytomous logistic regression (PLR) model to identify risk factors and then construct the
new prediction model. Model performances and validation were evaluated systematically in
the study.
Project 2: We used data from a multicenter randomized controlled trial named Prophylaxis for
Thromboembolism in Critical Care Trial (PROTECT) to develop a new prediction model for
hospital mortality in critically ill medical-surgical patients receiving heparin
thromboprophylaxis. We first identified risk factors independent of APACHE (Acute
Physiology and Chronic Health Evaluation) II score for hospital mortality, and then combined
the identified risk factors and APACHE II score to build the new prediction model. Model
performances were compared between the new prediction model and the APACHE II score.
Project 3: We re-analyzed the data from PROTECT to perform a sensitivity analysis based on
a competing risk analysis to investigate the efficacy of dalteparin versus unfractionated
heparin in preventing venous thromboembolism in medical-surgical critically ill patients,
taking all-cause death as a competing risk for venous thromboembolism. Results from the
competing risk analysis were compared with findings from the cause-specific analysis.
Results and Conclusions:
Project 1: The PLR model could simultaneously predict risk of individual combined benefit
and harm outcomes in patients with and without warfarin therapy for AF. The prediction
model was a good fit, had acceptable discrimination and calibration, and was internally and
externally validated. Should this approach be validated in other patient populations, it has
potential advantages over existing risk stratification approaches.
Project 2: The new model combining other risk factors and APACHE II score was a good fit,
well calibrated and internally validated. However, the discriminative ability of the prediction
model was not satisfactory. Compared with the APACHE II score alone, the new prediction
model increased data collection, was more complex but did not substantially improve discriminative ability.
Project 3: The competing risk analysis yielded no significant effect of dalteparin compared
with unfractionated heparin on proximal leg deep vein thromboses, but a lower risk of
pulmonary embolism in critically ill medical-surgical patients. Findings from the competing
risk analysis were similar to results from the cause-specific analysis. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20270 |
| Date | January 2016 |
| Creators | LI, GUOWEI |
| Contributors | THABANE, LEHANA, Clinical Epidemiology/Clinical Epidemiology & Biostatistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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