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

Long-term interleukin-6 levels and subsequent risk of coronary heart disease: Two new prospective studies and a systematic review

Background The relevance to coronary heart disease (CHD) of cytokines that govern inflammatory cascades, such as interleukin-6 (IL-6), may be underestimated because such mediators are short acting and prone to fluctuations. We evaluated associations of long-term circulating IL-6 levels with CHD risk (defined as nonfatal myocardial infarction [MI] or fatal CHD) in two population-based cohorts, involving serial measurements to enable correction for within-person variability. We updated a systematic review to put the new findings in context. Methods and Findings Measurements were made in samples obtained at baseline from 2,138 patients who had a first-ever nonfatal MI or died of CHD during follow-up, and from 4,267 controls in two cohorts comprising 24,230 participants. Correction for within-person variability was made using data from repeat measurements taken several years apart in several hundred participants. The year-to-year variability of IL-6 values within individuals was relatively high (regression dilution ratios of 0.41, 95% confidence interval [CI] 0.28-0.53, over 4 y, and 0.35, 95% CI 0.23-0.48, over 12 y). Ignoring this variability, we found an odds ratio for CHD, adjusted for several established risk factors, of 1.46 (95% CI 1.29-1.65) per 2 standard deviation (SD) increase of baseline IL-6 values, similar to that for baseline C-reactive protein. After correction for within-person variability, the odds ratio for CHD was 2.14 (95% CI 1.45-3.15) with long-term average ("usual'') IL-6, similar to those for some established risk factors. Increasing IL-6 levels were associated with progressively increasing CHD risk. An updated systematic review of electronic databases and other sources identified 15 relevant previous population-based prospective studies of IL-6 and clinical coronary outcomes (i.e., MI or coronary death). Including the two current studies, the 17 available prospective studies gave a combined odds ratio of 1.61 (95% CI 1.42-1.83) per 2 SD increase in baseline IL-6 (corresponding to an odds ratio of 3.34 [95% CI 2.45-4.56] per 2 SD increase in usual [long-term average] IL-6 levels). Conclusions Long-term IL-6 levels are associated with CHD risk about as strongly as are some major established risk factors, but causality remains uncertain. These findings highlight the potential relevance of IL-6-mediated pathways to CH

An evaluation of exact matching and propensity score methods as applied in a comparative effectiveness study of inhaled corticosteroids in asthma

Peer reviewedPublisher PD

A First Course in Causal Inference

I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only require basic knowledge of probability theory, statistical inference, and linear and logistic regressions

New Instrumental Variable Methods for Causal Inference.

In observational studies, unmeasured differences between treatment groups often confound the relationship of interest. Instrumental variable (IV) methods can give consistent effect estimates in the presence of this unmeasured confounding, and are becoming increasingly popular in health and medical research. In this dissertation, we develop new IV methods and apply them in studies comparing mortality among patients receiving dialysis as treatment for end stage renal disease. In the first project, we develop a weighted IV estimator that adjusts for instrument-outcome confounders through the IV propensity score. The weights are designed to approximate the probability of being selected into a one-to-one match, though the extension to many-to-one designs is also presented. Advantages of weighting over matching include increased efficiency, straightforward variance estimation, and ease of computation. The estimator is shown to be more efficient than alternatives. Its use is illustrated in a study comparing the relationship between mortality and dialysis session length among hemodialysis patients. While developed for use with binary outcomes, future work on applying the method to survival data is presented as well. In the second project, we develop a weighting procedure for increasing the strength of the instrument when matching. Compared with existing methods, the proposed weighting procedure strengthens the instrument without compromising match quality. This is a major advantage of the proposed method, as poor match quality can bias estimation. Methods are illustrated with a study comparing early mortality in hemodialysis and peritoneal dialysis patients. In the third project, we compare estimation with strengthened instruments to estimation with instruments that are naturally stronger. Methods for strengthening the instrument are motivated by the benefits of using stronger instruments, including decreased finite-sample bias, increased efficiency, and results that are more robust to unmeasured instrument-outcome confounders. It has not been shown, however, that strengthened instruments provide these same benefits. Results indicate that while they provide for more efficient estimation, they do not decrease finite-sample bias or improve the robustness to unmeasured instrument-outcome confounders. We highlight an important issue that has thus far been overlooked in the literature, and give guidance for future research related to strengthening the instrument.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133465/1/lehmannd_1.pd