Randomization Inference in a Group–Randomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance and Quantile Effects

In the Prospect Study, in ten pairs of two primary-care practices, one practice was picked at random to receive a “depression care manager” to treat its depressed patients. Randomization inference, properly performed, reflects the assignment of practices, not patients, to treatment or control. Yet, pertinent data describe individual patients: depression outcomes, baseline covariates, compliance with treatment. The methods discussed use only (i) the random assignment of clusters to treatment or control and (ii) the hypothesis about effects being tested or inverted for confidence intervals, so they are randomization inferences in Fisher\u27s strict sense. There is no assumption that the covariance model generated the data, that compliers resemble noncompliers, that dependence is from additive random cluster effects, that individuals in a same cluster do not interfere with one another, or that units are sampled from a population. We contrast methods of covariance adjustment, never assuming the models are “true,” obtaining exact randomization inferences. We consider exact inference about effects proportional to doses with noncompliance and effects whose magnitude varies with the degree of improvement that would occur without treatment. A simulation examines power

Acute Alcohol Consumption, Alcohol Outlets, and Gun Suicide

A case-control study of 149 intentionally self-inflicted gun injury cases (including completed gun suicides) and 302 population-based controls was conducted from 2003 to 2006 in a major US city. Two focal independent variables, acute alcohol consumption and alcohol outlet availability, were measured. Conditional logistic regression was adjusted for confounding variables. Gun suicide risk to individuals in areas of high alcohol outlet availability was less than the gun suicide risk they incurred from acute alcohol consumption, especially to excess. This corroborates prior work but also uncovers new information about the relationships between acute alcohol consumption, alcohol outlets, and gun suicide. Study limitations and implications are discussed

Efficient Nonparametric Estimation of Causal Effects in Randomized Trials With Noncompliance

Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and provides substantial finite-sample efficiency gains over the standard instrumental variable estimator. Our estimator is asymptotically equivalent to the standard instrumental variable estimator, and it can be applied to outcome variables with a continuous, ordinal or binary scale. We apply our method to data from a randomized trial of an intervention to improve the treatment of depression among depressed elderly patients in primary care practices

How to Earn Money with an EMI Problem: Static Energy Meters Running Backwards

The increased use of non-linear appliances in households has resulted in several conducted electromagnetic interference issues, such as misreadings of static energy meters used for billing purposes of the households' energy consumption. In this paper a case is presented where a static energy meter indicates a power generation, while power is actually being consumed. A perceived power generation of more than 430 W is measured by a static energy meter installed in a household when a television with a commercial off the shelf remote controlled switch with dimming functionalities consumed 21 W. The same situation is reproduced in a controlled lab environment, to eliminate possible influences of other appliances in the grid, which confirmed the on-site results. The current waveforms causing this supposed generation of power are investigated and it is observed that the phase firing angle of the current pulse drawn by the load in combination with the commercial off the shelf remote controlled switch affects the metering errors and determines whether the errors indicate a false generation, a too high consumption of power, or no error at all. A combination of the household equipment and a basic unloaded switched mode power supply in conjunction with two remote controlled switches resulted in a perceived power generation of more than 600 W. Having these loads connected for the entire day would counteract the total consumption of an average household and could even "generate" energy, and thus generate money for the consumer

Random Effects Logistic Models for Analyzing Efficacy of a Longitudinal Randomized Treatment With Non-Adherence

We present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent-to-treat longitudinal analyses based on random effects logistic models that are commonly used in primary care depression research. Our estimation approach is an extension of Nagelkerke et al.\u27s instrumental variables approximation for cross-sectional binary outcomes. Our approach is easily implementable with standard random effects logistic regression software. We show through a simulation study that our approach provides reasonably accurate inferences for the setting of the depression trial under model assumptions. We also evaluate the sensitivity of our approach to model assumptions for the depression trial

Older Adults’ Attitudes Toward Enrollment of Noncompetent Subjects Participating in Alzheimer’s Research

OBJECTIVE: Research that seeks to enroll noncompetent patients with Alzheimer’s disease without presenting any potential benefit to participants is the source of substantial ethical controversy. The authors used hypothetical Alzheimer’s disease studies that included either a blood draw or a blood draw and lumbar puncture to explore older persons’ attitudes on this question. METHOD: Face-to-face interviews were conducted with 538 persons age 65 and older. Questions explored participants’ understanding of research concepts, their views on enrolling persons with Alzheimer’s disease in research, and their preferences regarding having a proxy decision maker, granting advance consent, and granting their proxy leeway to override the participant’s decision. Additional questions assessed altruism, trust, value for research, and perceptions of Alzheimer’s disease. RESULTS: The majority (83%) were willing to grant advance consent to a blood draw study, and nearly half (48%) to a blood draw plus lumbar puncture study. Most (96%) were willing to identify a proxy for research decision making, and most were willing to grant their proxy leeway over their advance consent: 81% for the blood draw study and 70% for the blood draw plus lumbar puncture study. Combining the preferences for advance consent and leeway, the proportion who would permit being enrolled in the blood draw and lumbar puncture studies, respectively, were 92% and 75%. Multivariate models showed that willingness to be enrolled in research was most strongly associated with a favorable attitude toward biomedical research. CONCLUSIONS: Older adults generally support enrolling noncompetent persons with Alzheimer’s disease into research that does not present a benefit to subjects. Willingness to grant their proxy leeway over advance consent and a favorable attitude about biomedical research substantially explain this willingness

The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis

E-commerce companies have a number of online products, such as organic search, sponsored search, and recommendation modules, to fulfill customer needs. Although each of these products provides a unique opportunity for users to interact with a portion of the overall inventory, they are all similar channels for users and compete for limited time and monetary budgets of users. To optimize users' overall experiences on an E-commerce platform, instead of understanding and improving different products separately, it is important to gain insights into the evidence that a change in one product would induce users to change their behaviors in others, which may be due to the fact that these products are functionally similar. In this paper, we introduce causal mediation analysis as a formal statistical tool to reveal the underlying causal mechanisms. Existing literature provides little guidance on cases where multiple unmeasured causally-dependent mediators exist, which are common in A/B tests. We seek a novel approach to identify in those scenarios direct and indirect effects of the treatment. In the end, we demonstrate the effectiveness of the proposed method in data from Etsy's real A/B tests and shed lights on complex relationships between different products.Comment: Accepted by The 25th ACM SIGKDD Conference on Knowledge Discovery and DataMining (KDD '19), August 4-8, 2019, Anchorage, AK, US

Achieving Effective Antidepressant Pharmacotherapy in Primary Care: The Role of Depression Care Management in Treating Late-Life Depression

To estimate the effect of an evidence-based depression care management (DCM) intervention on the initiation and appropriate use of antidepressant in primary care patients with late-life depression. DESIGN : Secondary analysis of data from a randomized trial. SETTING : Community, primary care. PARTICIPANTS : Randomly selected individuals aged 60 and older with routine appointments at 20 primary care clinics randomized to provide a systematic DCM intervention or care as usual. METHODS : Rates of antidepressant use and dose adequacy of patients in the two study arms were compared at each patient assessment (baseline, 4, 8, and 12 months). For patients without any antidepressant treatment at baseline, a longitudinal analysis was conducted using multilevel logistic models to compare the rate of antidepressant treatment initiation, dose adequacy when initiation was first recorded, and continued therapy for at least 4 months after initiation between study arms. All analyses were conducted for the entire sample and then repeated for the subsample with major or clinically significant minor depression at baseline. RESULTS : Rates of antidepressant use and dose adequacy increased over the first year in patients assigned to the DCM intervention, whereas the same rates held constant in usual care patients. In longitudinal analyses, the DCM intervention had a significant effect on initiation of antidepressant treatment (adjusted odds ratio (OR)=5.63, P <.001) and continuation of antidepressant medication for at least 4 months (OR=6.57, P =.04) for patients who were depressed at baseline. CONCLUSIONS : Evidence-based DCM models are highly effective at improving antidepressant treatment in older primary care patients.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66406/1/j.1532-5415.2009.02226.x.pd

PC program extending the two-stage polynomial growth curve model to allow missing data

A stand-alone, menu-driven PC program, written in GAUSS386i, extending the analysis of one-sample longitudinal data sets satisfying the two-stage polynomial growth curve model (Ten Have et al., Am J Hum Biol, 3 (1991) 269-279) to allow missing data is described, illustrated and made available to interested readers. The method and the program are illustrated using data previously analyzed by the authors (Schneiderman and Kowalski, Am J Phys Anthropol, 67 (1985) 323-333) but with several randomly chosen data points discarded and treated as missing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30475/1/0000103.pd