Standardization and Control for Confounding in Observational Studies: A Historical Perspective

Control for confounders in observational studies was generally handled through stratification and standardization until the 1960s. Standardization typically reweights the stratum-specific rates so that exposure categories become comparable. With the development first of loglinear models, soon also of nonlinear regression techniques (logistic regression, failure time regression) that the emerging computers could handle, regression modelling became the preferred approach, just as was already the case with multiple regression analysis for continuous outcomes. Since the mid 1990s it has become increasingly obvious that weighting methods are still often useful, sometimes even necessary. On this background we aim at describing the emergence of the modelling approach and the refinement of the weighting approach for confounder control.Comment: Published in at http://dx.doi.org/10.1214/13-STS453 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Matching Methods for Causal Inference: A Review and a Look Forward

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods---or developing methods related to matching---do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.Comment: Published in at http://dx.doi.org/10.1214/09-STS313 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating marginal treatment effects from observational studies and indirect treatment comparisons: When are standardization-based methods preferable to those based on propensity score weighting?

In light of newly developed standardization methods, we evaluate, via simulation study, how propensity score weighting and standardization -based approaches compare for obtaining estimates of the marginal odds ratio and the marginal hazard ratio. Specifically, we consider how the two approaches compare in two different scenarios: (1) in a single observational study, and (2) in an anchored indirect treatment comparison (ITC) of randomized controlled trials. We present the material in such a way so that the matching-adjusted indirect comparison (MAIC) and the (novel) simulated treatment comparison (STC) methods in the ITC setting may be viewed as analogous to the propensity score weighting and standardization methods in the single observational study setting. Our results suggest that current recommendations for conducting ITCs can be improved and underscore the importance of adjusting for purely prognostic factors.Comment: 33 page

Causal Inference with Partial Interference and Right Censored Outcomes

Interference arises when the outcome of one individual depends on the treatment status of another individual. Partial interference is a special case of interference where individuals can be partitioned into groups such that no interference occurs between groups but may occur within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effect. Tchetgen Tchetgen and VanderWeele (2012) proposed a modified IPW estimator for different causal effects in the presence of partial interference. An extension of the Tchetgen Tchetgen and VanderWeele IPW estimator is proposed for the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are calculated using parametric frailty models. The large sample properties of the IPCW estimators are derived and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are illustrated using data from a cholera vaccination trial in Matlab, Bangladesh. Unfortunately, IPW methods often suffer from some significant disadvantage because of the instability of propensity scores. The parametric g formula is a natural alternative for IPW estimators. Robins (1986) proposed the use of g-computation algorithm in the absence of interference to infer about causal estimands of interest. Since then, the parametric g formula has been used for data with time varying confounding and exposure and also for time to event data (Westreich et al. (2012), Keil et al. (2014)). An extension of the parametric g estimator is proposed when there is time to event data with right censoring and possible partial interference. Parametric frailty models are used to model the outcome of probability of an event. Derivation of large sample properties of the estimator is provided. Simulation studies show the effectiveness of the method for finite sample. The cholera vaccination trial in Matlab, Bangladesh mentioned before is used to illustrate the methods in a real scenario. But both of these methods rely on the intrinsic assumption that the underlying models are correctly specified. If the treatment model is incorrect then the IPCW estimator will not be consistent. Similarly, if the outcome model is incorrect then the parametric g estimator will not be consistent. A doubly robust method is proposed to incorporate robustness under model misspecification so that the estimator is consistent even when only one of the two models is correctly specified. Large sample properties of the estimator are discussed. Finite sample performance of the method is also observed through simulation and the results are compared with the IPCW and parametric g estimator. Finally, the doubly robust method is also applied to the cholera vaccine trial.Doctor of Philosoph

Inequality in healthcare costs between residing and non-residing patients: evidence from Vietnam

Background: Place of residence has been shown to impact health. To date, however, previous studies have only focused on the variability in health outcomes and healthcare costs between urban and rural patients. This study takes a different approach and investigates cost inequality facing non-residing patients - patients who do not reside in the regions in which the hospitals are located. Understanding the sources for this inequality is important, as they are directly related to healthcare accessibility in developing countries. Methods: The causal impact of residency status on individual healthcare spending is documented with a quasi-experimental design. The propensity score matching method is applied to a unique patient-level dataset (n = 900) collected at public general and specialist hospitals across North Vietnam. Results: Propensity score matching shows that Vietnamese patients who do not reside in the regions in which the hospitals are located are expected to pay about 15 million Vietnamese dongs (approximately 750 USD) more than those who do, a sizable gap, given the distribution of total healthcare costs for the overall sample. This estimate is robust to alternative matching specifications. The obtained discrepancy is empirically attributable to the differences in three potential contributors, namely spending on accompanying relatives, "courtesy funds," and days of hospitalization. Conclusions: The present study finds that there is significant inequality in healthcare spending between residing and non-residing patients at Vietnamese hospitals and that this discrepancy can be partially explained by both institutional and non-institutional factors. These factors signal practical channels through which policymakers can improve healthcare accessibility

The Effect of High School Employment on Educational Attainment: A Conditional Difference-in-Differences Approach

Using American panel data from the National Educational Longitudinal Study of 1988 (NELS:88) this paper investigates the effect of working during grade 12 on attainment. We exploit the longitudinal nature of the NELS by employing, for the first time in the related literature, a semiparametric propensity score matching approach combined with difference-in- differences. This identification strategy allows us to address in a flexible way selection on both observables and unobservables associated with part-time work decisions. Once such factors are controlled for, insignificant effects on reading and math scores are found. We show that these results are robust to a matching approach combined with difference-in-difference-in-differences which allows differential time trends in attainment according to the working status in grade 12.education, evaluation, propensity score matching

Estimating Oral Anticoagulant Comparative Effectiveness in the Setting of Effect Heterogeneity: Comparing Clinical Trial Transport and Non-experimental Epidemiologic Methods

Oral anticoagulation is vital to the health of patients with atrial fibrillation at elevated risk of stroke. The first treatment for these patients, warfarin, was approved in the 1990s. Since 2010, dabigatran has been available for use after demonstrating non-inferiority to warfarin in a randomized controlled trial. Non-experimental studies comparing dabigatran to warfarin and censoring at treatment discontinuation have shown greater benefits than the original trial for all-cause mortality and attenuated harms for gastrointestinal bleeding. The goals of this dissertation, then, were to compute and compare 1) estimates of the absolute-scale effects of dabigatran vs warfarin initiation on ischemic stroke (IS), death, and gastrointestinal bleeding (GIB) in trial-eligible older adults using non-experimental Medicare data and 2) estimates of those effects in the same populations using inverse odds of sampling weights to transport results from the Randomized Evaluation of Long-Term Anticoagulation (RE-LY) trial. First, we conducted a propensity score weighted non-experimental study with the new user active comparator design in a 20% random sample of Medicare beneficiares. We estimated on-treatment two-year risk differences for IS (RD for dabigatran users, RDdabi: -0.67%, 95% CI -1.10%, -0.24%), mortality (RDdabi: -2.98%, 95% CI -3.97%, -1.95%) and GIB (RDdabi: 0.51%, 95% CI -0.30%, 1.31%). Intention-to-treat estimates showed attenuation for mortality (RDdabi: -1.65%, 95% CI -2.32%, -0.98%) and reversal for IS (RDdabi: 0.16%, 95% CI -0.20%, 0.52%). Next, we reweighted RE-LY to resemble the Medicare new users of warfarin or dabigatran (restricted to those with less than 15% predicted probability of frailty). After weighting, we estimated on-treatment two-year risk differences for IS (RDdabi: -0.77%, 95% CI -1.69%, 0.14%), death (RDdabi: -0.57%, 95% CI -1.83%, 0.68%) and GIB (RDdabi: 1.75%, 95% CI 0.76%, 2.74%). These twin studies show non-experimental and weighted trial analyses comparing dabigatran to warfarin agree much better for IS than they do for mortality or GIB. This could be due to confounding in the non-experimental estimates, missing treatment effect modifiers, or outcome misclassification. Researchers should be cautious about comparing studies without considering treatment effect heterogeneity and differences in adherence across study populations.Doctor of Philosoph