Inverse probability of treatment weighted (IPTW) estimation for marginal structural models (MSMs) requires the specification of a nuisance model describing the conditional relationship between treatment allocation and confounders. However, there is still limited information on the best strategy for building these treatment models in practice. We developed a series of simulations to systematically determine the effect of including different types of candidate variables in such models. We explored the performance of IPTW estimators across several scenarios of increasing complexity, including one designed to mimic the complexity typically seen in large pharmacoepidemiologic studies.Our results show that including pure predictors of treatment (i.e. not confounders) in treatment models can lead to estimators that are biased and highly variable, particularly in the context of small samples. The bias and mean-squared error of the MSM-based IPTW estimator increase as the complexity of the problem increases. The performance of the estimator is improved by either increasing the sample size or using only variables related to the outcome to develop the treatment model. Estimates of treatment effect based on the true model for the probability of treatment are asymptotically unbiased.We recommend including only pure risk factors and confounders in the treatment model when developing an IPTW-based MSM.
Objective To determine whether the use of inhaled corticosteroids during pregnancy increases the risk of pregnancy induced hypertension and pre-eclampsia among asthmatic women. Design Nested case-control study. Setting Three administrative health databases from Quebec: RAMQ, MED-ECHO, and Fichier des événements démographiques. Participants 3505 women with asthma, totalling 4593 pregnancies, between 1990 and 2000. Main outcome measures Pregnancy induced hypertension and pre-eclampsia. Results 302 cases of pregnancy induced hypertension and 165 cases of pre-eclampsia were identified. Use of inhaled corticosteroids from conception until date of outcome was not associated with an increased risk of pregnancy induced hypertension (adjusted odds ratio 1.02, 95% confidence interval 0.77 to 1.34) or pre-eclampsia (1.06, 0.74 to 1.53). No significant dose-response relation was observed between inhaled corticosteroids and pregnancy induced hypertension or pre-eclampsia. Oral corticosteroids were significantly associated with the risk of pregnancy induced hypertension (adjusted odds ratio 1.57, 1.02 to 2.41), and a trend was seen for pre-eclampsia (1.72, 0.98 to 3.02). Conclusion No significant increase of the risk of pregnancy induced hypertension or pre-eclampsia was detected among users of inhaled corticosteroids during pregnancy, while markers of uncontrolled and severe asthma were found to significantly increase the risks of pregnancy induced hypertension and pre-eclampsia.
The PETALE study will contribute to comprehensively characterize clinical, psychosocial, biologic, and genomic features of cALL survivors using an integrated approach. Expected outcomes include LAE early detection biomarkers, long-term follow-up guidelines, and recommendations for physicians and health professionals.
Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision. The issue of missing data for inverse probability of treatment weighted estimation of marginal structural models (MSMs) has often been addressed, though little has been done to compare different missing data techniques in this relatively new method of analysis. We propose a method for systematically dealing with missingness in MSMs by treating missingness as a cause for censoring and weighting subjects by the inverse probability of missingness. We developed a series of simulations to systematically compare the effect of using case deletion, our inverse weighting approach, and multiple imputation in a MSM when there is missing information on an important confounder. We found that multiple imputation was slightly less biased and considerably less variable than the inverse probability approach. Thus, the lower variability achieved through multiple imputation makes it desirable in most practical cases where the missing data are strongly predicted by the available data. Inverse probability weighting is, however, a superior alternative to naive approaches such as complete-case analysis.
Our objectives were to assess the prevalence of cardiometabolic complications in children, adolescents, and young adult survivors of childhood acute lymphoblastic leukemia (cALL), to identify their predictors and the risk compared to the Canadian population. We performed a cardiometabolic assessment of cALL survivors from the PETALE cohort (n = 247, median age at visit of 21.7 years). In our group, overweight and obesity affected over 70% of women. Pre-hypertension and hypertension were mostly common in men, both adults (20%) and children (19%). Prediabetes was mainly present in women (6.1% of female adult survivors) and 41.3% had dyslipidemia. Cranial radiation therapy was a predictor of dyslipidemia (RR: 1.60, 95% CI: 1.07–2.41) and high LDL-cholesterol (RR: 4.78, 95% CI: 1.72–13.28). Male gender was a predictor for pre-hypertension and hypertension (RR: 5.12, 95% CI: 1.81–14.46). Obesity at the end of treatment was a predictor of obesity at interview (RR: 2.07, 95% CI: 1.37–3.14) and of metabolic syndrome (RR: 3.04, 95% CI: 1.14–8.09). Compared to the general population, cALL survivors were at higher risk of having the metabolic syndrome, dyslipidemia, pre-hypertension/hypertension and high LDL-cholesterol, while the risk for obesity was not different. Our results support the need for early screening and lifestyle intervention in this population.
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