scholarly journals Robust Estimation of Inverse Probability Weights for Marginal Structural Models

2015 ◽  
Vol 110 (511) ◽  
pp. 1013-1023 ◽  
Author(s):  
Kosuke Imai ◽  
Marc Ratkovic
Keyword(s):  
Robust Estimation ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Inverse Probability ◽  
Inverse Probability Weights

scholarly journals Causal inference in continuous time: an example on prostate cancer therapy

Biostatistics ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 172-185 ◽  
Author(s):  
Pål Christie Ryalen ◽  
Mats Julius Stensrud ◽  
Sophie Fosså ◽  
Kjetil Røysland
Keyword(s):  
Prostate Cancer ◽  
Continuous Time ◽  
Causal Effect ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Cancer Treatments ◽  
Discrete Parameter ◽  
Inverse Probability Weights ◽  
Using Data ◽  
Prostate Cancer Treatments

Abstract In marginal structural models (MSMs), time is traditionally treated as a discrete parameter. In survival analysis on the other hand, we study processes that develop in continuous time. Therefore, Røysland (2011. A martingale approach to continuous-time marginal structural models. Bernoulli 17, 895–915) developed the continuous-time MSMs, along with continuous-time weights. The continuous-time weights are conceptually similar to the inverse probability weights that are used in discrete time MSMs. Here, we demonstrate that continuous-time MSMs may be used in practice. First, we briefly describe the causal model assumptions using counting process notation, and we suggest how causal effect estimates can be derived by calculating continuous-time weights. Then, we describe how additive hazard models can be used to find such effect estimates. Finally, we apply this strategy to compare medium to long-term differences between the two prostate cancer treatments radical prostatectomy and radiation therapy, using data from the Norwegian Cancer Registry. In contrast to the results of a naive analysis, we find that the marginal cumulative incidence of treatment failure is similar between the strategies, accounting for the competing risk of other death.

scholarly journals Metformin use and risk of cancer in patients with type 2 diabetes: a cohort study of primary care records using inverse probability weighting of marginal structural models

2019 ◽  
Vol 48 (2) ◽  
pp. 527-537 ◽  
Author(s):  
Ruth E Farmer ◽  
Deborah Ford ◽  
Rohini Mathur ◽  
Nish Chaturvedi ◽  
Rick Kaplan ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Cancer Risk ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Diabetes Diagnosis ◽  
Inverse Probability ◽  
Risk Of Cancer

Abstract Background Previous studies provide conflicting evidence on whether metformin is protective against cancer. When studying time-varying exposure to metformin, covariates such as body mass index (BMI) and glycated haemoglobin (HbA1c) may act as both confounders and causal pathway variables, and so cannot be handled adequately by standard regression methods. Marginal structural models (MSMs) with inverse probability of treatment weights (IPTW) can correctly adjust for such confounders. Using this approach, the main objective of this study was to estimate the effect of metformin on cancer risk compared with risk in patients with T2DM taking no medication. Methods Patients with incident type 2 diabetes (T2DM) were identified in the Clinical Practice Research Datalink (CPRD), a database of electronic health records derived from primary care in the UK. Patients entered the study at diabetes diagnosis or the first point after this when they had valid HbA1c and BMI measurements, and follow-up was split into 1-month intervals. Logistic regression was used to calculate IPTW; then the effect of metformin on all cancers (including and excluding non-melanoma skin cancer) and breast, prostate, lung, colorectal and pancreatic cancers was estimated in the weighted population. Results A total of 55 629 T2DM patients were alive and cancer-free at their study entry; 2530 people had incident cancer during a median follow-up time of 2.9 years [interquartile range (IQR) 1.3–5.4 years]. Using the MSM approach, the hazard ratio (HR) for all cancers, comparing treatment with metformin with no glucose-lowering treatment, was 1.02 (0.88–1.18). Results were robust to a range of sensitivity analyses and remained consistent when estimating the treatment effect by length of exposure. We also found no evidence of a protective effect of metformin on individual cancer outcomes. Conclusions We find no evidence that metformin has a causal association with cancer risk.

scholarly journals Estimating the causal effects of cumulative treatment episodes for adolescents using marginal structural models and inverse probability of treatment weighting

2014 ◽  
Vol 136 ◽  
pp. 69-78 ◽  
Author(s):  
Beth Ann Griffin ◽  
Rajeev Ramchand ◽  
Daniel Almirall ◽  
Mary E. Slaughter ◽  
Lane F. Burgette ◽  
...  
Keyword(s):  
Structural Models ◽  
Causal Effects ◽  
Marginal Structural Models ◽  
Inverse Probability

scholarly journals Causal models and their application to estimate the effect of chronic hepatitis B treatment

2015 ◽  
Author(s):  
Γεωργία Βουρλή
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Structural Models ◽  
Causal Models ◽  
Marginal Structural Models ◽  
Inverse Probability ◽  
Hepatitis B Treatment

Οι κλασικές μέθοδοι στατιστικής ανάλυσης εξετάζουν αν οι παρατηρούμενες σχέσεις είναι αποτέλεσμα τύχης και να μας δώσουν συμπεράσματα σχετικά με μη περιστασιακές σχέσεις μεταξύ μεταβλητών, οι οποίες όμως ενδέχεται να μην έχουν αιτιολογική ερμηνεία. Δυστυχώς, έχει αποδειχθεί ότι στο πλαίσιο μιας διαχρονικής μελέτης παρατήρησης, όταν μια μεταβλητή που επηρεάζεται από προηγούμενη έκθεση είναι ταυτόχρονα προγνωστικός παράγοντας α. μελλοντικής έκθεσης και β. της έκβασης, υπάρχει δηλαδή συγχυτικός παράγοντας εξαρτώμενος από το χρόνο, οι κλασικές προσεγγίσεις ανάλυσης για την εκτίμηση της επίδρασης της έκθεσης μπορεί να είναι μεροληπτικές. Οι g- μέθοδοι είναι μια κατηγορία των μεθόδων που εισήχθησαν για την εκτίμηση των αιτιακών επιδράσεων. Η πιο πρόσφατη εξ αυτών είναι η μέθοδος της στάθμισης με την αντίστροφη πιθανότητα θεραπείας (Inverse Probability of Treatment Weighting; IPTW), η οποία εφαρμόζεται για την εκτίμηση των παραμέτρων των περιθωριακών δομικών μοντέλων (Marginal Structural Models; MSMs). Ο σκοπός αυτής της εργασίας ήταν να αξιολογήσει τις επιδόσεις των MSM σε καταστάσεις που συχνά συναντώνται σε διαχρονικές μελέτες παρατήρησης με έκβαση την επιβίωση. Χρησιμοποιώντας μια ακριβή μέθοδο προσομοίωσης, διερευνήσαμε δύο κατηγορίες σεναρίων: α)Απουσία από προγραμματισμένες επισκέψεις, όπως συμβαίνει κατά την παρακολούθηση μιας κλινικής κοόρτης (clinic cohort) και β) μη καταγραφή των τιμών του συγχυτικού παράγοντα, που θα μπορούσε να προκύψει ακόμα και όταν οι επισκέψεις γίνονται ανά συγκεκριμένα χρονικά διαστήματα. Στην πρώτη περίπτωση, τα δεδομένα αναλύθηκαν αρχικά χωρίς καμία διόρθωση και στη συνέχεια χρησιμοποιήθηκε είτε περικοπή είτε κανονικοποίηση των βαρών/σταθμών. Στη δεύτερη περίπτωση, τα δεδομένα αναλύθηκαν είτε συμπληρώνοντας τις ελλείπουσες τιμές με την τελευταία παρατηρηθήσα τιμή τους (Last Observation Carried Forward; LOCF), είτε συμπληρώνοντας τις ελλείπουσες τιμές με τη μέθοδο των πολλαπλών υποκαταστάσεων (Mutliple Imputation; MI) είτε εφαρμόζοντας πρόσθετη στάθμιση με την αντίστροφη πιθανότητα του να είναι μια τιμή μη παρατηρηθήσα (ελλείπουσα) (Inverse Probability of Missingness Weighting; IPMW).Επιπλέον, αναλύσαμε δεδομένα από τη μελέτη HEPNET.Greece για την χρόνια ηπατίτιδα Β (Chronic Hepatitis B; CHB), προκειμένου να αξιολογηθεί η επίδραση της θεραπείας και του τύπου θεραπείας (Ιντερφερόνη +/- Νουκλεο(τ)ιδικό Ανάλογο (NA) έναντι μόνο Νουκλεο(τ)ιδικό Ανάλογο) στην εμφάνισηκλινικού συμβάντος σε ασθενείς με CHB.Τα αποτελέσματα αυτής της εργασίας δείχνουν ότι τα δεδομένα από τις μελέτες παρατήρησης μπορούν να παρέχουν χρήσιμα αποτελέσματα, εφόσον αναλυθούν με την κατάλληλη μέθοδο. Ακόμα και όταν υπάρχουν ελλείπουσες τιμές στο συγχυτικό παράγοντα ή σε περιπτώσεις παρακολούθησης ανά τυχαία/μη προκαθορισμένα χρονικά διαστήματα, όπως συμβαίνει συχνά σε μελέτες παρατήρησης, υπάρχουν εξειδικευμένες προσεγγίσεις για την αντιμετώπιση της πιθανής μεροληψίας οι οποίες μπορούν να ενσωματωθούν στα MSM.

scholarly journals 742Analyzing the statistical pitfalls of treating mediators as confounders

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Seham Elmrayed ◽  
Tanis Fenton ◽  
Amy Metcalfe ◽  
Darren Brenner
Keyword(s):  
Body Size ◽  
Direct Effect ◽  
Regression Models ◽  
Marginal Structural Models ◽  
Growth Study ◽  
Inverse Probability ◽  
Logistic Regression Models ◽  
Inverse Probability Weights ◽  
Baseline Covariates ◽  
Child’S Weight

Abstract Background Numerous studies indicated that infants born small-for-gestational-age (SGA) are at higher risk of overweight. However, the association between SGA and overweight may be due to overcontrolling for body size. This study aimed to analyze the effect of controlling for child’s weight and height in the association between SGA and overweight in children born preterm. Methods Data were obtained from the Preterm Infant Multicenter Growth Study (n = 1089). The association between SGA and overweight at 36 months corrected age (CA) was analyzed using logistic regression models: 1) crude, 2) adjusted for baseline covariates, 3) adjusted for baselines covariates with additional adjustments separately for child’s weight and height at 21 months CA. Marginal structural models (MSM) with stabilized inverse probability weights were used to estimate the direct effect of SGA on overweight. Results The crude and adjusted models yielded a null association (OR, 95% CI: 0.88, 0.26-2.96; 0.95, 0.28-3.29). Adjusting for later height reversed the effect (OR, 95% CI: 2.31, 0.52-10.26), and adjusting for later weight reversed the effect and provided a significant association (OR, 95% CI: 6.60, 1.10-37.14). The MSMs with height and weight considered as mediators indicated no direct effect of SGA on overweight (OR, 95% CI: 0.83, 0.14-5.01; 0.71, 0.18-2.81). Conclusions Overcontrolling for body size can falsely induce an association between SGA and overweight. Key messages Mediators should not be treated as confounders.

Marginal structural models to estimate the effects of time-varying treatments on clustered outcomes in the presence of interference

2017 ◽  
Vol 28 (2) ◽  
pp. 613-625 ◽  
Author(s):  
Jiwei He ◽  
Alisa Stephens-Shields ◽  
Marshall Joffe
Keyword(s):  
Structural Models ◽  
Marginal Structural Models ◽  
Time Varying ◽  
Nested Models ◽  
Unit Level ◽  
Inverse Probability Weights ◽  
Effect Of Treatment ◽  
Working Correlation ◽  
Structural Nested Models ◽  
Cluster Level

Marginal structural models are a class of causal models useful for characterizing the effect of treatment in the presence of time-varying confounding. They are more widely used than structural nested models, partly because these models are easier to understand and to implement. We extend marginal structural models to situations with clustered observations with unit- and cluster-level treatment and introduce an appropriate inferential method. We consider how to formulate models with cluster-level and unit-level treatments. For unit-level treatments, we consider cases with and without interference. We also consider the use of unit-specific inverse probability weights and certain working correlation structures to improve the efficiency of estimators in some situations. We apply our method to different scenarios including 2 or 3 units per cluster and a mixture of larger clusters. Simulation examples and data from the treatment arm of a glaucoma clinical trial were used to illustrate our method.

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