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Many lifestyle intervention trials depend on collecting self-reported outcomes, such as dietary intake, to assess the intervention's effectiveness. Self-reported outcomes are subject to measurement error, which impacts treatment effect estimation. External validation studies measure both self-reported outcomes and accompanying biomarkers, and can be used to account for measurement error. However, in order to account for measurement error using an external validation sample, an assumption must be made that the inferences are transportable from the validation sample to the intervention trial of interest. This assumption does not always hold. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample, and we also formally investigate when bias due to poor transportability may arise. Lastly, we examine the performance of the methods using simulation, and illustrate them using PREMIER, a lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study measuring both self-reported diet and urinary biomarkers. [ABSTRACT FROM AUTHOR]

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425–436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor). [ABSTRACT FROM AUTHOR]

Observational studies are commonplace in medicine. A frequent concern is confounding bias due to differences in patient characteristics across treatment groups, but other important issues include dependency among observations nested within clusters (e.g. patients clustered within physicians or surgeons) and confounding due to cluster characteristics (e.g. physician or surgeon experience or training). Given the frequency with which these issues arise in medical research, as well as their relative complexity, methods for the analysis of clustered observational data are reviewed. We argue for estimating causal treatment effects using marginal models that either match or weight observations using a suitable distance metric (e.g. the propensity score). Simulation results demonstrated that methods incorporating clustering into calculation of the variance were generally more accurate than those that did not. Moreover, methods that account for cluster confounding when estimating the treatment effect were least biased and most accurate. Throughout the paper we illustrate the proposed methods in a medical device setting that compares the effectiveness of femoral heads used in total hip replacements. Whenever possible the clustered aspect of the data should be considered in the design of the study when constructing the distance measure or in the matching process, as well as in the analysis when estimating the variance of the treatment effect. [ABSTRACT FROM AUTHOR]

One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounders. Using a case-control design, we present a propensity score estimation using only case samples and we provide sufficient conditions for the consistency of the estimation of the causal log-odds ratio using case-based propensity scores. Computationally, we implement a principle component analysis to reduce high-dimensional confounders. Extensive simulation studies are performed to demonstrate superior performance of our method to existing methods. Finally, we apply the proposed method to analyze drug-induced myopathy data sampled from a de-identified subset of medical record database (close to 5 million patient records), The Indiana Network for Patient Care. Our method identified 70 drug-induced myopathy (p < 0.05) out 72 drugs, which have myoathy side effects on their FDA drug labels. These 70 drugs include three statins who are known for their myopathy side effects. [ABSTRACT FROM AUTHOR]

Many approaches, including traditional parametric modeling and machine learning techniques, have been proposed to estimate propensity scores. This paper describes a new model averaging approach to propensity score estimation in which parametric and nonparametric estimates are combined to achieve covariate balance. Simulation studies are conducted across different scenarios varying in the degree of interactions and nonlinearities in the treatment model. The results show that, based on inverse probability weighting, the proposed propensity score estimator produces less bias and smaller standard errors than existing approaches. They also show that a model averaging approach with the objective of minimizing the average Kolmogorov-Smirnov statistic leads to the best performing IPW estimator. The proposed approach is also applied to a real data set in evaluating the causal effect of formula or mixed feeding versus exclusive breastfeeding on a child's body mass index Z-score at age 4. The data analysis shows that formula or mixed feeding is more likely to lead to obesity at age 4, compared to exclusive breastfeeding. [ABSTRACT FROM AUTHOR]

We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of receiving treatment. Previous works on randomization tests often assume these probabilities are equal within blocks of units. We consider the general case where they differ across units and show how to perform randomization tests and obtain point estimates and confidence intervals. Furthermore, we develop rejection-sampling and importance-sampling approaches for conducting randomization-based inference conditional on any statistic of interest, such as the number of treated units or forms of covariate balance. We establish that our randomization tests are valid tests, and through simulation we demonstrate how the rejection-sampling and importance-sampling approaches can yield powerful randomization tests and thus precise inference. Our work also has implications for observational studies, which commonly assume a strongly ignorable assignment mechanism. Most methodologies for observational studies make additional modeling or asymptotic assumptions, while our framework only assumes the strongly ignorable assignment mechanism, and thus can be considered a minimal-assumption approach. [ABSTRACT FROM AUTHOR]

Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need. [ABSTRACT FROM AUTHOR]

Propensity score methods are common for estimating a binary treatment effect when treatment assignment is not randomized. When exposure is measured on an ordinal scale (i.e. low-medium-high), however, propensity score inference requires extensions which have received limited attention. Estimands of possible interest with an ordinal exposure are the average treatment effects between each pair of exposure levels. Using these estimands, it is possible to determine an optimal exposure level. Traditional methods, including dichotomization of the exposure or a series of binary propensity score comparisons across exposure pairs, are generally inadequate for identification of optimal levels. We combine subclassification with regression adjustment to estimate transitive, unbiased average causal effects across an ordered exposure, and apply our method on the 2005-2006 National Health and Nutrition Examination Survey to estimate the effects of nutritional label use on body mass index. [ABSTRACT FROM AUTHOR]

The framework of principal stratification provides a way to think about treatment effects conditional on post-randomization variables, such as level of compliance. In particular, the complier average causal effect (CACE) - the effect of the treatment for those individuals who would comply with their treatment assignment under either treatment condition - is often of substantive interest. However, estimation of the CACE is not always straightforward, with a variety of estimation procedures and underlying assumptions, but little advice to help researchers select between methods. In this article, we discuss and examine two methods that rely on very different assumptions to estimate the CACE: a maximum likelihood ('joint') method that assumes the 'exclusion restriction,' (ER) and a propensity score-based method that relies on 'principal ignorability.' We detail the assumptions underlying each approach, and assess each methods' sensitivity to both its own assumptions and those of the other method using both simulated data and a motivating example. We find that the ER-based joint approach appears somewhat less sensitive to its assumptions, and that the performance of both methods is significantly improved when there are strong predictors of compliance. Interestingly, we also find that each method performs particularly well when the assumptions of the other approach are violated. These results highlight the importance of carefully selecting an estimation procedure whose assumptions are likely to be satisfied in practice and of having strong predictors of principal stratum membership. [ABSTRACT FROM AUTHOR]