A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.
Ontology highlight
ABSTRACT:
SUBMITTER: Cui Y
PROVIDER: S-EPMC8118566 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:A parameter in a statistical model is identified if its value can be uniquely determined from the distribution of the observable data. We consider the context of an instrumental variable analysis with a binary outcome for estimating a causal risk ratio. The semiparametric generalized method of moments and structural mean model frameworks use estimating equations for parameter estimation. In this paper, we demonstrate that lack of identification can occur in either of these frameworks, especially if the instrument is weak. In particular, the estimating equations may have no solution or multiple solutions. We investigate the relationship between the strength of the instrument and the proportion of simulated data sets for which there is a unique solution of the estimating equations. We see that this proportion does not appear to depend greatly on the sample size, particularly for weak instruments (ρ(2) ≤ 0.01). Poor identification was observed in a considerable proportion of simulated data sets for instruments explaining up to 10% of the variance in the exposure with sample sizes up to 1 million. In an applied example considering the causal effect of body mass index (weight (kg)/height (m)(2)) on the probability of early menarche, estimates and standard errors from an automated optimization routine were misleading.
| S-EPMC4070936 | biostudies-literature
Project description:Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an instrumental variable (IV) is observed for all subjects which satisfies the exclusion restriction that the IV affects the missingness process without directly influencing the outcome. In this paper, we provide necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV. In addition, we give sufficient identification conditions that are more straightforward to verify in practice. For inference, we focus on estimation of a population outcome mean, for which we develop a suite of semiparametric estimators that extend methods previously developed for data missing at random. Specifically, we propose inverse probability weighted estimation, outcome regression-based estimation and doubly robust estimation of the mean of an outcome subject to MNAR. For illustration, the methods are used to account for selection bias induced by HIV testing refusal in the evaluation of HIV seroprevalence in Mochudi, Botswana, using interviewer characteristics such as gender, age and years of experience as IVs.
| S-EPMC7743916 | biostudies-literature
Project description:There is an extensive literature on the estimation and evaluation of optimal individualized treatment rules in settings where all confounders of the effect of treatment on outcome are observed. We study the development of individualized decision rules in settings where some of these confounders may not have been measured but a valid binary instrument is available for a binary treatment. We first consider individualized treatment rules, which will naturally be most interesting in settings where it is feasible to intervene directly on treatment. We then consider a setting where intervening on treatment is infeasible, but intervening to encourage treatment is feasible. In both of these settings, we also handle the case that the treatment is a limited resource so that optimal interventions focus the available resources on those individuals who will benefit most from treatment. Given a reference rule, we evaluate an optimal individualized rule by its average causal effect relative to a prespecified reference rule. We develop methods to estimate optimal individualized rules and construct asymptotically efficient plug-in estimators of the corresponding average causal effect relative to a prespecified reference rule.
| S-EPMC7959164 | biostudies-literature
Project description:Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This paper studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications. Given a collection of treatment regimes, we consider robust estimation of the quantile-optimal treatment regime, which does not require the analyst to specify an outcome regression model. We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter. This novel representation allows us to investigate the asymptotic theory of the estimated optimal treatment regime using empirical process techniques. We derive theory involving a nonstandard convergence rate and a non-normal limiting distribution. The same nonstandard convergence rate would also occur if the mean optimality criterion is applied, but this has not been studied. Thus, our results fill an important theoretical gap for a general class of policy search methods in the literature. The paper investigates both static and dynamic treatment regimes. In addition, doubly robust estimation and alternative optimality criterion such as that based on Gini's mean difference or weighted quantiles are investigated. Numerical simulations demonstrate the performance of the proposed estimator. A data example from a trial in HIV+ patients is used to illustrate the application.
| S-EPMC6223317 | biostudies-literature
Project description:In assessing causal mediation effects in randomized studies, a challenge is that the direct and indirect effects can vary across participants due to different measured and unmeasured characteristics. In that case, the population effect estimated from standard approaches implicitly averages over and does not estimate the heterogeneous direct and indirect effects. We propose a Bayesian semiparametric method to estimate heterogeneous direct and indirect effects via clusters, where the clusters are formed by both individual covariate profiles and individual effects due to unmeasured characteristics. These cluster-specific direct and indirect effects can be estimated through a set of regression models where specific coefficients are clustered by a stick-breaking prior. To let clustering be appropriately informed by individual direct and indirect effects, we specify a data-dependent prior. We conduct simulation studies to assess performance of the proposed method compared to other methods. We use this approach to estimate heterogeneous causal direct and indirect effects of an expressive writing intervention for patients with renal cell carcinoma.
| S-EPMC5837944 | biostudies-literature
Project description:BackgroundInstrumental variable (IV) methods are often used to identify 'local' causal effects in a subgroup of the population of interest. Such 'local' effects may not be ideal for informing clinical or policy decision making. When the instrument is non-causal, additional difficulties arise for interpreting 'local' effects. Little attention has been paid to these difficulties, even though commonly proposed instruments in epidemiology are non-causal (e.g. proxies for physician's preference; genetic variants in some Mendelian randomization studies).MethodsFor IV estimates obtained from both causal and non-causal instruments under monotonicity, we present results to help investigators pose four questions about the local effect estimates obtained in their studies. (1) To what subgroup of the population does the effect pertain? Can we (2) estimate the size of or (3) describe the characteristics of this subgroup relative to the study population? (4) Can the sensitivity of the effect estimate to deviations from monotonicity be quantified?ResultsWe show that the common interpretations and approaches for answering these four questions are generally only appropriate in the case of causal instruments.ConclusionsAppropriate interpretation of an IV estimate under monotonicity as a 'local' effect critically depends on whether the proposed instrument is causal or non-causal. The results and formal proofs presented here can help in the transparent reporting of IV results and in enhancing the use of IV estimates in informing decision-making efforts.
| S-EPMC6124612 | biostudies-literature
Project description:The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are frequently present, we propose a doubly robust estimation with adaptive L 1 penalties to select important variables at both group and within-group levels for competing risks data. The proposed method is applied to hematopoietic cell transplantation data to personalize the graft source choice for treatment-related mortality (TRM). While the existing medical literature attempts to find a uniform solution ignoring the heterogeneity of the graft source effects on TRM, the analysis results show the effect of the graft source on TRM could be different depending on the patient-specific characteristics.
| S-EPMC8117077 | biostudies-literature
Project description:A treatment regime is a rule that assigns a treatment, among a set of possible treatments, to a patient as a function of his/her observed characteristics, hence "personalizing" treatment to the patient. The goal is to identify the optimal treatment regime that, if followed by the entire population of patients, would lead to the best outcome on average. Given data from a clinical trial or observational study, for a single treatment decision, the optimal regime can be found by assuming a regression model for the expected outcome conditional on treatment and covariates, where, for a given set of covariates, the optimal treatment is the one that yields the most favorable expected outcome. However, treatment assignment via such a regime is suspect if the regression model is incorrectly specified. Recognizing that, even if misspecified, such a regression model defines a class of regimes, we instead consider finding the optimal regime within such a class by finding the regime that optimizes an estimator of overall population mean outcome. To take into account possible confounding in an observational study and to increase precision, we use a doubly robust augmented inverse probability weighted estimator for this purpose. Simulations and application to data from a breast cancer clinical trial demonstrate the performance of the method.
| S-EPMC3556998 | biostudies-literature
Project description:ObjectiveExamine the endogenous relationship between uncompensated care and hospital patient experience scores.Data sources/study settingThe Hospital Consumer Assessment of Healthcare Providers and Systems Survey, CMS Healthcare Cost Report Information System, and the US Census Bureau.Study designThe exogenous change in uncompensated care caused by the 2014 Medicaid expansion was exploited to measure the effect of uncompensated care on patient experience scores using a 2SLS regression with instrumental variables approach.Data collection/extraction methodsU.S. general, short-term hospitals whose DSH status remained constant and had nonmissing data for 2011-2015, which totaled 969 unique hospitals per year.Principal findingsThe effect of uncompensated care on patient experience was in the predicted direction, with three of the 10 measures being statistically significant. A one percentage point increase in uncompensated care costs resulted in a 0.25-0.50 percentage point decrease in select patient experience scores.ConclusionsResults indicate a weak relationship between uncompensated care and patient experience scores, as a reduction in uncompensated care is related to quality improvement for some hospitals. These findings have implications for hospitals as they navigate changing reimbursement structures and policy makers considering changes to Obama-era health care reforms.
| S-EPMC6505421 | biostudies-literature
Project description:There is growing interest and investment in precision medicine as a means to provide the best possible health care. A treatment regime formalizes precision medicine as a sequence of decision rules, one per clinical intervention period, that specify if, when and how current treatment should be adjusted in response to a patient's evolving health status. It is standard to define a regime as optimal if, when applied to a population of interest, it maximizes the mean of some desirable clinical outcome, such as efficacy. However, in many clinical settings, a high-quality treatment regime must balance multiple competing outcomes; eg, when a high dose is associated with substantial symptom reduction but a greater risk of an adverse event. We consider the problem of estimating the most efficacious treatment regime subject to constraints on the risk of adverse events. We combine nonparametric Q-learning with policy-search to estimate a high-quality yet parsimonious treatment regime. This estimator applies to both observational and randomized data, as well as settings with variable, outcome-dependent follow-up, mixed treatment types, and multiple time points. This work is motivated by and framed in the context of dosing for chronic pain; however, the proposed framework can be applied generally to estimate a treatment regime which maximizes the mean of one primary outcome subject to constraints on one or more secondary outcomes. We illustrate the proposed method using data pooled from 5 open-label flexible dosing clinical trials for chronic pain.
| S-EPMC6293986 | biostudies-literature