Local average treatment effects estimation via substantive model compatible multiple imputation

Nonadherence to assigned treatment is common in randomized controlled trials (RCTs). Recently, there has been increased interest in estimating causal effects of treatment received, for example, the so‐called local average treatment effect (LATE). Instrumental variables (IV) methods can be used for identification, with estimation proceeding either via fully parametric mixture models or two‐stage least squares (TSLS). TSLS is popular but can be problematic for binary outcomes where the estimand of interest is a causal odds ratio. Mixture models are rarely used in practice, perhaps because of their perceived complexity and need for specialist software. Here, we propose using multiple imputation (MI) to impute the latent compliance class appearing in the mixture models. Since such models include an interaction term between the latent compliance class and randomized treatment, we use “substantive model compatible” MI (SMC MIC), which can additionally handle missing data in outcomes and other variables in the model, before fitting the mixture models via maximum likelihood to the MI data sets and combining results via Rubin's rules. We use simulations to compare the performance of SMC MIC to existing approaches and also illustrate the methods by reanalyzing an RCT in UK primary health. We show that SMC MIC can be more efficient than full Bayesian estimation when auxiliary variables are incorporated, and is superior to two‐stage methods, especially for binary outcomes.     

Multiple imputation methods for treatment noncompliance and nonresponse in randomized clinical trials

Summary .  Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine.     

On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death

Summary .  We focus on estimation of the causal effect of treatment on the functional status of individuals at a fixed point in time t * after they have experienced a catastrophic event, from observational data with the following features: (i) treatment is imposed shortly after the event and is nonrandomized, (ii) individuals who survive to t * are scheduled to be interviewed, (iii) there is interview nonresponse, (iv) individuals who die prior to t * are missing information on preevent confounders, and (v) medical records are abstracted on all individuals to obtain information on postevent, pretreatment confounding factors. To address the issue of survivor bias, we seek to estimate the survivor average causal effect (SACE), the effect of treatment on functional status among the cohort of individuals who would survive to t * regardless of whether or not assigned to treatment. To estimate this effect from observational data, we need to impose untestable assumptions, which depend on the collection of all confounding factors. Because preevent information is missing on those who die prior to t *, it is unlikely that these data are missing at random. We introduce a sensitivity analysis methodology to evaluate the robustness of SACE inferences to deviations from the missing at random assumption. We apply our methodology to the evaluation of the effect of trauma center care on vitality outcomes using data from the National Study on Costs and Outcomes of Trauma Care.     

Methods for clustered encouragement design studies with noncompliance and missing data

Encouragement design studies are particularly useful for estimating the effect of an intervention that cannot itself be randomly administered to some and not to others. They require a randomly selected group receive extra encouragement to undertake the treatment of interest, where the encouragement typically takes the form of additional information or incentives. We consider a "clustered encouragement design" (CED), where the randomization is at the level of the clusters (e.g. physicians), but the compliance with assignment is at the level of the units (e.g. patients) within clusters. Noncompliance and missing data are particular problems in encouragement design studies, where encouragement to take the treatment, rather than the treatment itself, is randomized. The motivating study looks at whether computer-based care suggestions can improve patient outcomes in veterans with chronic heart failure. Since physician adherence has been inadequate, the original study focused on methods to improve physician adherence, although an equally important question is whether physician adherence improves patient outcomes. Here, we reanalyze the data to determine the effect of physician adherence on patient outcomes. We propose causal inference methodology for the effect of a treatment versus a control in a randomized CED study with all-or-none compliance at the unit level. These methods extend the current approaches to account for nonignorable missing data and use an alternative approach to inference using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems and have recently been applied to the potential outcomes framework of causal inference (Taylor and Zhou, 2009b).     

The large sample bounds on the principal strata effect with application to a prostate cancer prevention trial

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected after randomization can give rise to biased outcome comparisons. One approach to deal with such issues is to consider the principal strata effect (PSE, or equally, the survivor average causal effect). PSE is defined as the effect of treatment on the outcome among the subpopulation that would have been selected under either treatment arm. Unfortunately, the PSE cannot generally be estimated without the identifying assumptions; however, the bounds can be derived using a deterministic causal model. In this paper, we propose a number of assumptions for deriving the bounds with narrow width. The assumptions and bounds, which differ from those introduced by Zhang and Rubin (2003), are illustrated using data from a randomized prostate cancer prevention trial.     

A Bayesian nonparametric model for zero‐inflated outcomes: Prediction,clustering, and causal estimation

Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero‐inflation complicate these tasks—requiring highly flexible, data‐adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero‐inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero‐inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest—allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero‐inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER‐Medicare database.     

血小板膜P2Y12受体基因T744C多态性对外伤性脑梗死(PTCI)患者血小板聚合率及疗效的影响

目的:外伤性脑梗死(posttraumatic cerebral infarction,PTCI)是颅脑损伤的常见并发症之一,P2Y12受体介导血小板聚集是血栓形成的重要通路,与血小板聚集形成密切相关。本研究探讨外伤性脑梗死发生发展与血小板膜P2Y12受体基因T744C基因多态性的关系。方法:用聚合酶链反应(PCR)和限制性酶切片段长度多态性(RFLP)技术对186例外伤性脑梗死患者P2Y12受体基因T744C多态性进行分析。分别在治疗前和治疗后对所有颅脑外伤患者的伤情GCS评分,并按基因型分组对照分析结果。结果:血小板膜T744C血小板膜T744C基因型基因频率分别为TT基因型59.14%、TC型32.26%、CC型8.60%,T等位基因75.27%、C等位基因24.73%;其中TT基因型对奥扎格雷反应较敏感,GCS评分预后好;而CC型对奥扎格雷反应性低,预后差。结论:T744C基因多态性中CC基因型可能导致外伤性脑梗死临床及预后存在明显的个体差异,与其对抗血小板药物抵抗有关。T744C的C等位基因可能是脑梗死的遗传危险因素,开展相关遗传学风险研究,对于进一步缓解脑梗症状、改善预后具有重要意义。     

Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is “partial interference,” in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. that allow for failure time outcomes subject to right censoring are proposed. Permitting right-censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang et al. (2010, Biostatistics, 11, 676–692) for two-sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73 000 children and women in Matlab, Bangladesh.     

Four novel sequences in Drosophila melanogaster homologous to the auxiliary Para sodium channel subunit TipE

TipE is an auxiliary subunit of the Drosophila Para sodium channel. Here we describe four sequences, TEH1-4, homologous to TipE in the Drosophila melanogaster genome, harboring all typical structures of both TipE and the beta-Subunit family of big-conductance Ca(2+)-activated potassium channels: short cytosolic N- and C-terminal stretches, two transmembrane domains, and a large extracellular loop with two disulfide bonds. Whereas TEH1 and TEH2 lack the TipE-specific extension in the extracellular loop, both TEH3 and TEH4 possess two extracellular EGF-like domains. A CNS-specific expression was found for TEH1, while TEH2-4 were more widely expressed. The genes for TEH2-4 are localized close to the tipE gene on chromosome 3L. Coexpression of TEH subunits with Para in Xenopus oocytes showed a strong (30-fold, TEH1), medium (5- to 10-fold, TEH2 and TEH3), or no (TEH4) increase in sodium current amplitude, while TipE increased the current 20-fold. In addition, steady-state inactivation and the recovery from fast inactivation were altered by coexpression of Para with TEH1. We conclude that members of the TEH-family are auxiliary subunits for Para sodium channels and possibly other ion channels.     

Indirect Estimation of the Comparative Treatment Effect in Pharmacogenomic Subgroups

Evidence of clinical utility is a key issue in translating pharmacogenomics into clinical practice. Appropriately designed randomized controlled trials generally provide the most robust evidence of the clinical utility, but often only data from a pharmacogenomic association study are available. This paper details a method for reframing the results of pharmacogenomic association studies in terms of the comparative treatment effect for a pharmacogenomic subgroup to provide greater insight into the likely clinical utility of a pharmacogenomic marker, its’ likely cost effectiveness, and the value of undertaking the further (often expensive) research required for translation into clinical practice. The method is based on the law of total probability, which relates marginal and conditional probability. It takes as inputs: the prevalence of the pharmacogenomic marker in the patient group of interest, prognostic effect of the pharmacogenomic marker based on observational association studies, and the unstratified comparative treatment effect based on one or more conventional randomized controlled trials. The critical assumption is that of exchangeability across the included studies. The method is demonstrated using a case study of cytochrome P450 (CYP) 2C19 genotype and the anti-platelet agent clopidogrel. Indirect subgroup analysis provided insight into relationship between the clinical utility of genotyping CYP2C19 and the risk ratio of cardiovascular outcomes between CYP2C19 genotypes for individuals using clopidogrel. In this case study the indirect and direct estimates of the treatment effect for the cytochrome P450 2C19 subgroups were similar. In general, however, indirect estimates are likely to have substantially greater risk of bias than an equivalent direct estimate.     

Counterfactual links to the proportion of treatment effect explained by a surrogate marker

In a randomized clinical trial, a statistic that measures the proportion of treatment effect on the primary clinical outcome that is explained by the treatment effect on a surrogate outcome is a useful concept. We investigate whether a statistic proposed to estimate this proportion can be given a causal interpretation as defined by models of counterfactual variables. For the situation of binary surrogate and outcome variables, two counterfactual models are considered, both of which include the concept of the proportion of the treatment effect, which acts through the surrogate. In general, the statistic does not equal either of the two proportions from the counterfactual models, and can be substantially different. Conditions are given for which the statistic does equal the counterfactual model proportions. A randomized clinical trial with potential surrogate endpoints is undertaken in a scientific context; this context will naturally place constraints on the parameters of the counterfactual model. We conducted a simulation experiment to investigate what impact these constraints had on the relationship between the proportion explained (PE) statistic and the counterfactual model proportions. We found that observable constraints had very little impact on the agreement between the statistic and the counterfactual model proportions, whereas unobservable constraints could lead to more agreement.     

The Nuclear Magnetic Resonance Solution Structure of the Mixed Disulfide between Escherichia coli Glutaredoxin(C14S) and Glutathione

The determination of the nuclear magnetic resonance (NMR) solution structure of the mixed disulfide between the mutant Escherichia coli glutaredoxin Grx(C14S) and glutathione (GSH), Grx(C14S)-SG, is described, the binding site for GSH on Grx(C14S) is located, and the non-bonding interactions between -SG and the protein are characterized. Based on nearly complete sequence-specific NMR assignments, 1010 nuclear Overhauser enhancement upper distance constraints and 116 dihedral angle constraints were obtained as the input for the structure calculations, for which the distance geometry program DIANA was used followed by energy minimization in a waterbath with the AMBER force field in the program OPAL. The -SG moiety was found to be localized on the surface of the protein in a cleft bounded by the amino acid residues Y13, T58, V59, Y72, T73 and D74. Hydrogen bonds have been identified between -SG and the residues V59 and T73 of Grx(C14S), and the formation of an additional hydrogen bond with Y72 and electrostatic interactions with the side-chains of D74 and K45 are also compatible with the NMR, conformational constraints. Comparison of the reduced and oxidized forms of Grx with Grx(C14S)-SG shows that the mixed disulfide more closely resembles the oxidized form of the protein. Functional implications of this observation are discussed. Comparisons are also made with the related proteins bacteriophage T4 glutaredoxin and glutathione S-transferase.     

The effect of interpersonal psychotherapy and other psychodynamic therapies versus 'treatment as usual' in patients with major depressive disorder

Background

Major depressive disorder afflicts an estimated 17% of individuals during their lifetimes at tremendous suffering and costs. Interpersonal psychotherapy and other psychodynamic therapies may be effective interventions for major depressive disorder, but the effects have only had limited assessment in systematic reviews.

Methods/Principal Findings

Cochrane systematic review methodology with meta-analysis and trial sequential analysis of randomized trials comparing the effect of psychodynamic therapies versus ‘treatment as usual’ for major depressive disorder. To be included the participants had to be older than 17 years with a primary diagnosis of major depressive disorder. Altogether, we included six trials randomizing a total of 648 participants. Five trials assessed ‘interpersonal psychotherapy’ and only one trial assessed ‘psychodynamic psychotherapy’. All six trials had high risk of bias. Meta-analysis on all six trials showed that the psychodynamic interventions significantly reduced depressive symptoms on the 17-item Hamilton Rating Scale for Depression (mean difference −3.12 (95% confidence interval −4.39 to −1.86;P<0.00001), no heterogeneity) compared with ‘treatment as usual’. Trial sequential analysis confirmed this result.

Discussion

We did not find convincing evidence supporting or refuting the effect of interpersonal psychotherapy or psychodynamic therapy compared with ‘treatment as usual’ for patients with major depressive disorder. The potential beneficial effect seems small and effects on major outcomes are unknown. Randomized trials with low risk of systematic errors and low risk of random errors are needed.     

Does Clustering Affect the Usual Test Statistics of no Treatment Effect in a Randomized Clinical Trial?

In many clinical trials, such as those undertaken by large cooperative cancer groups (CALGB, ECOG, SWOG, EORTC), patients are randomized to one of two treatments within an institution (a hospital or clinic). In such clinical trials, it has often occurred that outcomes of patients within institutions tend to be similar (correlated), due, possibly, to unmeasured variables such as quality of staff or quality of hospital equipment. We discuss when, in the presence of random hospital effects, the usual test statistics for no treatment effect, which do not take these hospital effects into account, are (asymptotically) unbiased.     

Distinct modulating effects of TipE-homologs 2–4 on Drosophila sodium channel splice variants

The Drosophila melanogaster TipE protein is thought to be an insect sodium channel auxiliary subunit functionally analogous to the β subunits of mammalian sodium channels. Besides TipE, four TipE-homologous proteins (TEH1–4) have been identified. It has been reported that TipE and TEH1 have both common and distinct effects on the gating properties of splice variants of the Drosophila sodium channel, DmNa_v. However, limited information is available on the effects of TEH2, TEH3 and TEH4 on the function of DmNa_v channel variants. In this study, we found that TEH2 increased the amplitude of peak current, but did not alter the gating properties of three examined DmNa_v splice variants expressed in Xenopus oocytes. In contrast, TEH4 had no effect on peak current, yet altered the gating properties of all three channel variants. Furthermore, TEH4 enhanced persistent current and slowed sodium current decay. The effects of TEH3 on DmNa_v variants are similar to those of TEH4, but the data were collected from a small portion of oocytes because co-expression of TEH3 with DmNa_v variants generated a large leak current in the majority of oocytes examined. In addition, TEH3 and TEH4 enhanced the expression of endogenous currents in oocytes. Taken together, our results reveal distinct roles of TEH proteins in modulating the function of sodium channels and suggest that TEH proteins might provide an important layer of regulation of membrane excitability in vivo. Our results also raise an intriguing possibility of TEH3/TEH4 as auxiliary subunits of other voltage-gated ion channels besides sodium channels.     

Evaluating candidate principal surrogate endpoints

SUMMARY: Frangakis and Rubin (2002, Biometrics 58, 21-29) proposed a new definition of a surrogate endpoint (a "principal" surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case-cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the "surrogate value" of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection.     

Species-specific treatment effects of helminth/HIV-1 co-infection: a systematic review and meta-analysis

In sub-Saharan Africa, over 22 million people are estimated to be co-infected with both helminths and HIV-1. Several studies have suggested that de-worming individuals with HIV-1 may delay HIV-1 disease progression, and that the benefit of de-worming may vary by individual helminth species. We conducted a systematic review and meta-analysis of the published literature to determine the effect of treatment of individual helminth infections on markers of HIV-1 progression (CD4 count and HIV viral load). There was a trend towards an association between treatment for Schistosoma mansoni and a decrease in HIV viral load (Weighted mean difference (WMD)=-0·10; 95% Confidence interval (CI): -0·24, 0·03), although this association was not seen for Ascaris lumbricoides, hookworm or Trichuris trichiura. Treatment of A. lumbricoides, S. mansoni, hookworm or T. trichiura was not associated with a change in CD4 count. While pooled data from randomized trials suggested clinical benefit of de-worming for individual helminth species, these effects decreased when observational data were included in the pooled analysis. While further trials are needed to confirm the role of anthelmintic treatment in HIV-1 co-infected individuals, providing anthelmintics to individuals with HIV-1 may be a safe, inexpensive and practical intervention to slow progression of HIV-1.     

The C677T polymorphism of the methylenetetrahydrofolate reductase gene in Mexican mestizo neural-tube defect parents, control mestizo and native populations

The C677T mutation of the methylenetetrahydrofolate reductase (MTHFR) gene, associated with the thermolabile form of the enzyme, has reportedly been found to be increased in neural-tube defects (NTD), though this association is still unclear. A group of 107 mestizo parents of NTD children and five control populations: 101 mestizo (M), 50 Huichol (H), 38 Tarahumara (T), 21 Purepecha (P) and 20 Caucasian (C) individuals were typed for the MTHFR C677T variant by the PCR/RFLP (HinfI) method. Genotype frequencies were in agreement with the Hardy-Weinberg expectations in all six populations. Allele frequency (%) of the C677T variant was 45 in NTD, 44 in M, 56 in H, 36 in T, 57 in P, 35 in C. Pairwise inter-population comparisons of allele frequency disclosed a very similar distribution between NTD and M groups (exact test, P=0.92). Among controls, differences between M and individual native groups were NS (0.06相似文献   

Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.     

Haplotype analysis of the beta2 adrenergic receptor gene and risk of myocardial infarction in humans

Polymorphisms in the beta2 adrenergic receptor (ADRB2), in particular G16R, Q27E, and T164I, have been implicated in the pathogenesis of cardiovascular and metabolic phenotypes. However, no prospective, genetic-epidemiological data are available on the risk of cardiovascular disease associated with these variants. Using DNA samples collected at baseline in a prospective cohort of 14,916 initially healthy American men, we evaluated the G16R, Q27E, and T164I polymorphisms among 523 individuals who subsequently developed myocardial infarction and among 2092 individuals who remained free of reported cardiovascular events during follow-up. The haplotype frequency distribution was significantly different among cases and controls (chi(2)(7d.f.) = 20.92, P = 0.0039). Haplotype-based logistic regression, adjusting for age, smoking, and randomized treatment group, indicated that G16-Q27-I164 (odds ratio 0.178, 95% C.I. 0.043-0.737, P = 0.017) and (non-G16-Q27)-T164 (odds ratio 1.235, 95% C.I. 1.031-1.480, P = 0.022) haplotypes were significantly associated with altered risk of myocardial infarction. These findings remained after further adjustment for BMI, history of hypertension, and presence or absence of diabetes. In conclusion, variation in haplotype frequencies for the beta2 adrenergic receptor gene was found to be associated with risk of myocardial infarction.