Instrumental Variable Estimation in a Survival Context

Bias due to unobserved confounding can seldom be ruled out with certainty when estimating the causal effect of a nonrandomized treatment. The instrumental variable (IV) design offers, under certain assumptions, the opportunity to tame confounding bias, without directly observing all confounders. The IV approach is very well developed in the context of linear regression and also for certain generalized linear models with a nonlinear link function. However, IV methods are not as well developed for regression analysis with a censored survival outcome. In this article, we develop the IV approach for regression analysis in a survival context, primarily under an additive hazards model, for which we describe 2 simple methods for estimating causal effects. The first method is a straightforward 2-stage regression approach analogous to 2-stage least squares commonly used for IV analysis in linear regression. In this approach, the fitted value from a first-stage regression of the exposure on the IV is entered in place of the exposure in the second-stage hazard model to recover a valid estimate of the treatment effect of interest. The second method is a so-called control function approach, which entails adding to the additive hazards outcome model, the residual from a first-stage regression of the exposure on the IV. Formal conditions are given justifying each strategy, and the methods are illustrated in a novel application to a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We also establish that analogous strategies can also be used under a proportional hazards model specification, provided the outcome is rare over the entire follow-up

Analysis of time-to-event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.Comment: 28 pages, 12 figures. For associated Supplementary material, see http://publicifsv.sund.ku.dk/~pka/STRATOSTG8

Recurrent event survival analysis predicts future risk of hospitalization in patients with paroxysmal and persistent atrial fibrillation

BackgroundIn patients with paroxysmal atrial fibrillation (PAF) or persistent atrial fibrillation (PeAF) symptom burden and fear of hospital readmission are major causes of reduced quality of life. We attempted to develop a prediction model for future atrial fibrillation hospitalization (AFH) risk in PAF and PeAF patients including all previously experienced AFHs in the analysis, as opposed to time to first event.MethodsRecurrent event survival analysis was used to model the impact of past AFHs on the risk of future AFHs. A recurrent event was defined as a hospitalization due to a new episode of AF. Death or progression to permanent AF were included as competing risks.ResultsWe enrolled 174 patients with PAF or PeAF, mean follow up duration was 1279 days, and 325 AFHs were observed. Median patient age was 63.0 (IQR 52.2-68.0), 29% had PAF, and 71% were male. Highly significant predictors of future AFH risk were PeAF (HR 3.20, CI 2.01-5.11) and number of past AFHs observed (HR for 1 event: 2.97, CI 2.04-4.32, HR for ≥2 events: 7.54, CI 5.47-10.40).ConclusionIn PAF and PeAF patients, AF type and observed AFH frequency are highly significant predictors of future AFH risk. The developed model enables risk prediction in individual patients based on AFH history and baseline characteristics, utilizing all events experienced by the patient. This is the first time recurrent event survival analysis has been used in AF patients

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

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

The Role of Diabetes Co-Morbidity for Tuberculosis Treatment Outcomes: A Prospective Cohort Study from Mwanza, Tanzania.

Due to the association between diabetes and pulmonary tuberculosis (TB), diabetes may threaten the control of TB. In a prospective cohort study nested in a nutrition trial, we investigated the role of diabetes on changes in anthropometry, grip strength, and clinical parameters over a five months follow-up period. Among pulmonary TB patients with known diabetes status, we assessed anthropometry and clinical parameters (e.g. haemoglobin) at baseline and after two and five months of TB treatment. A linear mixed-effects model (repeated measurements) was used to investigate the role of diabetes during recovery. Of 1205 TB patients, the mean (standard deviation) age was 36.6 (13.0) years, 40.9% were females, 48.9% were HIV co-infected, and 16.3% had diabetes. TB patients with diabetes co-morbidity experienced a lower weight gain at two (1.3 kg, CI95% 0.5; 2.0, p = 0.001) and five months (1.0 kg, CI95% 0.3; 1.7, p = 0.007). Similarly, the increase in the level of haemoglobin was lower among TB patients with diabetes co-morbidity after two (Δ 0.6 g/dL, CI95% 0.3; 0.9 p < 0.001) and five months (Δ 0.5 g/dL, CI95% 0.2; 0.9 p = 0.004) of TB treatment, respectively. TB patients initiating TB treatment with diabetes co-morbidity experience delayed recovery of body mass and haemoglobin, which are important for the functional recovery from disease

Corn-Soy-Blend Fortified with Phosphorus to Prevent Refeeding Hypophosphatemia in Undernourished Piglets

<div><p>Background</p><p>Phosphorus (P) levels in refeeding diets are very important as undernourished children are at risk of hypophosphatemia during refeeding. For this reason, conventional corn-soy-blends (CSB) have been reformulated by the World Food Programme to obtain a mono-calcium-phosphate fortified product (CSB+) and a product further fortified with skim milk powder (CBS++).</p><p>Methods</p><p>Using a piglet model of undernourished children, we hypothesized that feeding of CSB+, CSB++ or CSB+ with added whey permeate (CSB+/wp) would help to prevent refeeding hypophosphatemia. Pigs were weaned at 4 weeks of age and undernutrition was induced with a nutritionally inadequate pure maize diet for 7 weeks, after which they were refed for 3 weeks with either CSB+ (n = 10), CSB++ (n = 10) or CSB+/wp (n = 10). For reference, a fourth group continued on the maize diet (REF, n = 10).</p><p>Results</p><p>Following induction of undernutrition, body weight and length were 29±5% and 67±4% (means±SD) of values in age-matched pigs fed a nutritionally adequate diet, and the mean serum P level was 1.77±0.34 mmol/l. During the first week of refeeding, P levels in the CSB+ pigs decreased to 55% of values before refeeding (P < 0.05) while values in the CSB++ and CSB+/wp pigs were able to maintain their plasma phosphate at a similar level as before refeeding.</p><p>Conclusion</p><p>We conclude that fortification of CSB with only monocalcium-phosphate does not prevent hypophosphatemia. Dairy products like skim milk powder or whey permeate may represent relevant sources of phosphorus during refeeding. The content and form of phosphorus in such diets need to be carefully evaluated, and the undernourished piglet may be used to test the efficacy of such diets.</p></div

Göttingen minipig model of diet-induced atherosclerosis: influence of mild streptozotocin-induced diabetes on lesion severity and markers of inflammation evaluated in obese, obese and diabetic, and lean control animals

BACKGROUND: From a pharmacological perspective, readily-available, well-characterized animal models of cardiovascular disease, including relevant in vivo markers of atherosclerosis are important for evaluation of novel drug candidates. Furthermore, considering the impact of diabetes mellitus on atherosclerosis in human patients, inclusion of this disease aspect in the characterization of a such model, is highly relevant. The objective of this study was to evaluate the effect of mild streptozotocin-induced diabetes on ex- and in vivo end-points in a diet-induced atherosclerotic minipig model. METHODS: Castrated male Göttingen minipigs were fed standard chow (CD), atherogenic diet alone (HFD) or with superimposed mild streptozotocin-induced diabetes (HFD-D). Circulating markers of inflammation (C-reactive protein (CRP), oxidized low-density lipoprotein (oxLDL), plasminogen activator inhibitor-1, lipid and glucose metabolism were evaluated together with coronary and aortic atherosclerosis after 22 or 43 diet-weeks. Group differences were evaluated by analysis of variance for parametric data and Kruskal–Wallis test for non-parametric data. For qualitative assessments, Fisher’s exact test was applied. For all analyses, p < 0.05 was considered statistically significant. RESULTS: Overall, HFD and HFD-D displayed increased CRP, oxLDL and lipid parameters compared to CD at both time points. HFD-D displayed impaired glucose metabolism as compared to HFD and CD. Advanced atherosclerotic lesions were observed in both coronary arteries and aorta of HFD and HFD-D, with more advanced plaque findings in the aorta but without differences in lesion severity or distribution between HFD and HFD-D. Statistically, triglyceride was positively (p = 0.0039), and high-density lipoprotein negatively (p = 0.0461) associated with aortic plaque area. CONCLUSIONS: In this model, advanced coronary and aortic atherosclerosis was observed, with increased levels of inflammatory markers, clinically relevant to atherosclerosis. No effect of mild streptozotocin-induced diabetes was observed on plaque area, lesion severity or inflammatory markers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-015-0670-2) contains supplementary material, which is available to authorized users