Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements -- effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this paper is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates two perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the paper proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types -- interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on within and across methods

In epidemiology and social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the within approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the across approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation). We show that the within method is valid and can be used with any causal effect estimator that is consistent in the full-data setting. Existing across methods are inconsistent, but a different across method that averages the inverse probability weights across imputed datasets is consistent for propensity score weighting. We also comment on methods that rely on imputing a function of the missing covariate rather than the covariate itself, including imputation of the propensity score and of the probability weight. Based on consistency results and practical flexibility, we recommend generally using the standard within method. Throughout, we provide intuition to make the results meaningful to the broad audience of applied researchers

Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects

In the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatment if applied to a target population of interest. If all treatment effect moderators are observed in the RCT and in a dataset representing the target population, then we can obtain an estimate for the target population ATE by adjusting for the difference in the distribution of the moderators between the two samples. This paper considers sensitivity analyses for two situations: (1) where we cannot adjust for a specific moderator $V$ observed in the RCT because we do not observe it in the target population; and (2) where we are concerned that the treatment effect may be moderated by factors not observed even in the RCT, which we represent as a composite moderator $U$. In both situations, the outcome is not observed in the target population. For situation (1), we offer three sensitivity analysis methods based on (i) an outcome model, (ii) full weighting adjustment and (iii) partial weighting combined with an outcome model. For situation (2), we offer two sensitivity analyses based on (iv) a bias formula and (v) partial weighting combined with a bias formula. We apply methods (i) and (iii) to an example where the interest is to generalize from a smoking cessation RCT conducted with participants of alcohol/illicit drug use treatment programs to the target population of people who seek treatment for alcohol/illicit drug use in the US who are also cigarette smokers. In this case a treatment effect moderator is observed in the RCT but not in the target population dataset

Biological Time Series Analysis Using a Context Free Language: Applicability to Pulsatile Hormone Data

National Space Biomedical Research Institute (NASA NCC 9-58 HFP01603)National Space Biomedical Research Institute (NASA NCC 9-58 HPF00405)National Institutes of Health (U.S.) (NIH NCRR-GCRC-M01-RR-02635)United States. Air Force Office of Scientific Research (AFOSR F49620-95-1-0388)United States. Air Force Office of Scientific Research (AFOSR FA9550-06-0080)National Institutes of Health (U.S.) (NIH P01-AG09975)National Institutes of Health (U.S.) (NIH T32 HL07901-10)National Institutes of Health (U.S.) (NIH F31-GM095340-01)National Institutes of Health (U.S.) (NIH K24-HL105664)National Institutes of Health (U.S.) (NIH K02-HD045459)National Institutes of Health (U.S.) (NIH RC2-HL101340)National Institutes of Health (U.S.) (NIH R01-AR43130)National Institutes of Health (U.S.) (NIH K24-HL103845)National Institutes of Health (U.S.) (NIH R01-MH071847)National Institutes of Health (U.S.) (NIH R01 HL098433)National Institutes of Health (U.S.) (NIH R01 HL098433-02S1

Biological Time Series Analysis Using a Context Free Language: Applicability to Pulsatile Hormone Data

We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals

The Role of Tuberin in DNA Damage Repair During Cell Proliferation

The cell cycle contains DNA damage checkpoints that delay mitotic progression and allow for DNA repair before cell division. DNA damage can be caused by radiation, drugs, and other processes which lead to cellular mutations and carcinogenesis. The tumour suppressor protein p53 is activated in the presence of DNA damage. It induces apoptosis or cell cycle arrest which allows cells to repair themselves. Tuberin (TSC2), another tumour suppressor protein, regulates the G2/M transition in the cell cycle and negatively regulates protein synthesis and cell growth. Mutations in tuberin can lead to the multisystem autosomal dominant disease known as tuberous sclerosis (TSC). Previously, our lab has shown that Tuberin regulates mitotic onset through cellular localization of the G2/M Cyclin, Cyclin B1. My project focuses on the Tuberin/Cyclin B1 complex in relation to G2/M arrest and DNA damage repair. In this study, we will overexpress Tuberin-WT and Tuberin clinical mutants in NIH-3T3 (mouse) and U2OS (human) p53 wild type cells. Etoposide, a topoisomerase II drug, will be used to induce DNA damage. Cells will then be analyzed by flow cytometry, TUNEL assay, and western blot to assess their cell cycle profile, apoptotic levels, and protein expression. Using CRISPR-Cas9 technology, a NIH-3T3 null TSC2 cell line will be created to clarify the role of Tuberin during DNA repair. Preliminary results have determined that 4μM of etoposide treatment at 8 hours arrests 50% of NIH-3T3 cells at G2/M. This project will provide greater insight into DNA damage induced carcinogenesis, TSC, and other proliferative diseases

Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details

Background: Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population -- an assumption that may not hold in practice. Methods: The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods' assumptions and provide detailed implementation instructions. Illustration: We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion: These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects