| 1. |
- Ciocanea-Teodorescu, Iuliana, et al.
(författare)
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Causal inference in survival analysis under deterministic missingness of confounders in register data
- 2023
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 42:12, s. 1946-1964
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Tidskriftsartikel (refereegranskat)abstract
- Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.
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| 2. |
- Gabriel, Erin E., et al.
(författare)
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Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
- 2024
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:3, s. 534-547
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Tidskriftsartikel (refereegranskat)abstract
- There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.
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| 3. |
- Gabriel, Erin E., et al.
(författare)
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Propensity weighting plus adjustment in proportional hazards model is not doubly robust
- 2024
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Ingår i: Biometrics. - 0006-341X .- 1541-0420.
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Tidskriftsartikel (refereegranskat)abstract
- Recently, it has become common for applied works to combine commonly used survival analysis modelingmethods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doublyrobust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model orthe propensity score model is correctly specified. This combination does not, in general, produce a doubly robustestimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulationthis lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and asimple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. Weprovide a novel proof that the combination of propensity score weighting and a proportional hazards survival model,fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcomeunder particular censoring mechanisms if either the propensity score or the outcome model is correctly specified andcontains all confounders. Given our results suggesting that double robustness only exists under the null, we outlinetwo simple alternative estimators that are doubly robust for the survival difference at a given time point (in the abovesense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimationfor the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.
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| 4. |
- Goetghebeur, Els, et al.
(författare)
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Formulating causal questions and principled statistical answers
- 2020
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Ingår i: Statistics in Medicine. - : WILEY. - 0277-6715 .- 1097-0258. ; 39:30, s. 4922-4948
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Tidskriftsartikel (refereegranskat)abstract
- Although review papers on causal inference methods are now available, there is a lack of introductory overviews onwhatthey can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on , where SAS and Stata code for analysis is also provided.
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| 5. |
- Lindmark, Anita, et al.
(författare)
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The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care - Illustrated Using the Swedish Stroke Register
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance.METHODS: The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008-2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method.RESULTS: Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence.CONCLUSIONS: The study emphasizes the importance of combining clinical relevance and level of statistical confidence when profiling hospital performance. To guide the decision process a web-based tool that gives ROC-curves for different scenarios is provided.
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| 6. |
- Olarte Parra, Camila, et al.
(författare)
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Trial emulation and survival analysis for disease incidence registers : A case study on the causal effect of pre-emptive kidney transplantation
- 2022
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 41:21, s. 4176-4199
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Tidskriftsartikel (refereegranskat)abstract
- When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.
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| 7. |
- Rompaye, Bart Van, et al.
(författare)
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Evaluating hospital performance based on excess cause-specific incidence
- 2015
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 34:8, s. 1334-1350
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Tidskriftsartikel (refereegranskat)abstract
- Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry.
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| 8. |
- Varewyck, Machteld, et al.
(författare)
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On shrinkage and model extrapolation in the evaluation of clinical center performance
- 2014
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Ingår i: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 15:4, s. 651-664
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Tidskriftsartikel (refereegranskat)abstract
- We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.
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