Samuel Lendle

Cravings as a mediator and moderator of drinking outcomes in the COMBINE study

AIMS Investigators of the COMBINE (Combining Medications and Behavioral Interventions for Alcoholism) study examined whether combining medications with a behavioral intervention would improve outcomes over monotherapies. Unexpectedly, the combination did not offer any advantage over either treatment alone. This study aimed to explain the lack of incremental benefit offered by the combination over either monotherapy by assessing the role of cravings as a treatment mediator and moderator. DESIGN Secondary mediation and moderation analyses of COMBINE study data. SETTING Eleven United States academic sites. PARTICIPANTS A total of 863 patients randomized to one of four treatment groups: naltrexone (100 mg/day; n = 209), the combined behavioral intervention (CBI, n = 236), naltrexone and CBI combined (n = 213) and placebo naltrexone (n = 205). MEASUREMENTS Percentage of days abstinent (PDA) measured between 13 and 16 weeks post-baseline. Cravings, the potential mediator/moderator, were measured at baseline, weeks 4 and 12 using the Obsessive-Compulsive Drinking Scale. FINDINGS Compared with placebo, naltrexone, CBI and the combination all increased PDA by an additional 6-10 percentage points for those with high cravings (P < 0.05 for all three treatment groups). None had significant effects on PDA for those with low cravings. The effects of all three treatments were mediated at least partially by cravings; craving reduction explained 48-53% of treatment effects (P < 0.05 for all three treatment groups). Furthermore, naltrexone appeared to reduce cravings at 4 weeks, while CBI did not reduce cravings until 12 weeks. CONCLUSIONS The Combining Medications and Behavioral Interventions for Alcoholism (COMBINE) naltrexone + CBI combination may not be more beneficial than either monotherapy because craving reduction is a common mechanism of both.

Identification and Efficient Estimation of the Natural Direct Effect among the Untreated

The natural direct effect (NDE), or the effect of an exposure on an outcome if an intermediate variable was set to the level it would have been in the absence of the exposure, is often of interest to investigators. In general, the statistical parameter associated with the NDE is difficult to estimate in the non-parametric model, particularly when the intermediate variable is continuous or high dimensional. In this article, we introduce a new causal parameter called the natural direct effect among the untreated, discuss identifiability assumptions, propose a sensitivity analysis for some of the assumptions, and show that this new parameter is equivalent to the NDE in a randomized controlled trial. We also present a targeted minimum loss estimator (TMLE), a locally efficient, double robust substitution estimator for the statistical parameter associated with this causal parameter. The TMLE can be applied to problems with continuous and high dimensional intermediate variables, and can be used to estimate the NDE in a randomized controlled trial with such data. Additionally, we define and discuss the estimation of three related causal parameters: the natural direct effect among the treated, the indirect effect among the untreated and the indirect effect among the treated.

Targeted maximum likelihood estimation in safety analysis

OBJECTIVES To compare the performance of a targeted maximum likelihood estimator (TMLE) and a collaborative TMLE (CTMLE) to other estimators in a drug safety analysis, including a regression-based estimator, propensity score (PS)-based estimators, and an alternate doubly robust (DR) estimator in a real example and simulations. STUDY DESIGN AND SETTING The real data set is a subset of observational data from Kaiser Permanente Northern California formatted for use in active drug safety surveillance. Both the real and simulated data sets include potential confounders, a treatment variable indicating use of one of two antidiabetic treatments and an outcome variable indicating occurrence of an acute myocardial infarction (AMI). RESULTS In the real data example, there is no difference in AMI rates between treatments. In simulations, the double robustness property is demonstrated: DR estimators are consistent if either the initial outcome regression or PS estimator is consistent, whereas other estimators are inconsistent if the initial estimator is not consistent. In simulations with near-positivity violations, CTMLE performs well relative to other estimators by adaptively estimating the PS. CONCLUSION Each of the DR estimators was consistent, and TMLE and CTMLE had the smallest mean squared error in simulations.

Balancing Score Adjusted Targeted Minimum Loss-based Estimation

Abstract Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies.

Prospective Postmarketing Surveillance of Acute Myocardial Infarction in New Users of Saxagliptin: A Population-Based Study

OBJECTIVE The cardiovascular safety of saxagliptin, a dipeptidyl-peptidase 4 inhibitor, compared with other antihyperglycemic treatments is not well understood. We prospectively examined the association between saxagliptin use and acute myocardial infarction (AMI). RESEARCH DESIGN AND METHODS We identified patients aged ≥18 years, starting from the approval date of saxagliptin in 2009 and continuing through August 2014, using data from 18 Mini-Sentinel data partners. We conducted seven sequential assessments comparing saxagliptin separately with sitagliptin, pioglitazone, second-generation sulfonylureas, and long-acting insulin, using disease risk score (DRS) stratification and propensity score (PS) matching to adjust for potential confounders. Sequential testing kept the overall chance of a false-positive signal below 0.05 (one-sided) for each pairwise comparison. RESULTS We identified 82,264 saxagliptin users and more than 1.5 times as many users of each comparator. At the end of surveillance, the DRS-stratified hazard ratios (HRs) (95% CI) were 1.08 (0.90–1.28) in the comparison with sitagliptin, 1.11 (0.87–1.42) with pioglitazone, 0.79 (0.64–0.98) with sulfonylureas, and 0.57 (0.46–0.70) with long-acting insulin. The corresponding PS-matched HRs were similar. Only one interim analysis of 168 analyses met criteria for a safety signal: the PS-matched saxagliptin-pioglitazone comparison from the fifth sequential analysis, which yielded an HR of 1.63 (1.12–2.37). This association diminished in subsequent analyses. CONCLUSIONS We did not find a higher AMI risk in saxagliptin users compared with users of other selected antihyperglycemic agents during the first 5 years after U.S. Food and Drug Administration approval of the drug.

Using Super Learner Prediction Modeling to Improve High-dimensional Propensity Score Estimation

The high-dimensional propensity score is a semiautomated variable selection algorithm that can supplement expert knowledge to improve confounding control in nonexperimental medical studies utilizing electronic healthcare databases. Although the algorithm can be used to generate hundreds of patient-level variables and rank them by their potential confounding impact, it remains unclear how to select the optimal number of variables for adjustment. We used plasmode simulations based on empirical data to discuss and evaluate data-adaptive approaches for variable selection and prediction modeling that can be combined with the high-dimensional propensity score to improve confounding control in large healthcare databases. We considered approaches that combine the high-dimensional propensity score with Super Learner prediction modeling, a scalable version of collaborative targeted maximum-likelihood estimation, and penalized regression. We evaluated performance using bias and mean squared error (MSE) in effect estimates. Results showed that the high-dimensional propensity score can be sensitive to the number of variables included for adjustment and that severe overfitting of the propensity score model can negatively impact the properties of effect estimates. Combining the high-dimensional propensity score with Super Learner was the most consistent strategy, in terms of reducing bias and MSE in the effect estimates, and may be promising for semiautomated data-adaptive propensity score estimation in high-dimensional covariate datasets.

Scalable collaborative targeted learning for high-dimensional data

Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instantiation of the collaborative targeted minimum loss-based estimation template can be presented as a greedy forward stepwise collaborative targeted minimum loss-based estimation algorithm. It does not scale well when the number p of covariates increases drastically. This motivates the introduction of a novel instantiation of the collaborative targeted minimum loss-based estimation template where the covariates are pre-ordered. Its time complexity is O ( p ) as opposed to the original O ( p 2 ) , a remarkable gain. We propose two pre-ordering strategies and suggest a rule of thumb to develop other meaningful strategies. Because it is usually unclear a priori which pre-ordering strategy to choose, we also introduce another instantiation called SL-C-TMLE algorithm that enables the data-driven choice of the better pre-ordering strategy given the problem at hand. Its time complexity is O ( p ) as well. The computational burden and relative performance of these algorithms were compared in simulation studies involving fully synthetic data or partially synthetic data based on a real world large electronic health database; and in analyses of three real, large electronic health databases. In all analyses involving electronic health databases, the greedy collaborative targeted minimum loss-based estimation algorithm is unacceptably slow. Simulation studies seem to indicate that our scalable collaborative targeted minimum loss-based estimation and SL-C-TMLE algorithms work well. All C-TMLEs are publicly available in a Julia software package.

Online Targeted Learning for Time Series

We consider the case that we observe a time series where at each time we observe in chronological order a covariate vector, a treatment, and an outcome. We assume that the conditional probability distribution of this time specific data structure, given the past, depends on the past through a fixed (in time) dimensional summary measure, and that this conditional distribution is described by a fixed (in time) mechanism that is known to be an element of some model space (e.g., unspecified). We propose a causal model that is compatible with this statistical model and define a family of causal effects in terms of stochastic interventions on a subset of the treatment nodes on a future outcome, and establish identifiability of these causal effects from the observed data distribution.