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

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

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

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

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

PC program for analyzing one-sample longitudinal data sets which satisfy the two-stage polynomial growth curve model

The two-stage polynomial growth curve model is described and a GAUSS program to perform the associated computations is documented and made available to interested readers. The two-stage model is similar to that considered by us earlier (Schneiderman and Kowalski: American Journal of Physical Anthropology 67:323–333, 1985; American Journal of Human Biology 1:31–42, 1989), i.e., it is appropriate for the analysis of one-sample longitudinal data collected at either equal or unequal time intervals. Here, however, the covariance matrix, Σ, instead of being considered arbitrary, is now assumed to have the special structure Σ = W A W′ + Σ 2 I. We show the conditions under which this special structure may be expected to arise and how it may be exploited to produce sharper results in certain situations. The method and the program are illustrated and the results are contrasted to those obtained when Σ is arbitrary. It is suggested that the two-stage model is more efficient when the same degree polynomial is adequate to model the data in the two situations, but that, should a higher degree be necessary for the two-stage model, confidence intervals and/or bands may be wider than those corresponding to Σ arbitrary.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38550/1/1310030306_ftp.pd

WHICH TRANSFORMATION FOR NORMALISING SKINFOLD AND FATNESS DISTRIBUTIONS?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26472/1/0000007.pd

Genetic heterogeneity and trans regulators of gene expression

Heterogeneity poses a challenge to linkage mapping. Here, we apply a latent class extension of Haseman-Elston regression to expression phenotypes with significant evidence of linkage to trans regulators in 14 large pedigrees. We test for linkage, accounting for heterogeneity, and classify individual families as "linked" and "unlinked" on the basis of their contribution to the overall evidence of linkage

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