A Fundamental Equivalence between Randomized Experiments and Observational Studies

A fundamental probabilistic equivalence between randomized experiments and observational studies is presented. Given a detailed scenario, the reader is asked to consider which of two possible study designs provides more information regarding the expected difference in an outcome due to a time-fixed treatment. A general solution is described, and a particular worked example is also provided. A mathematical proof is given in the appendix. The demonstrated equivalence helps to clarify common ground between randomized experiments and observational studies, and to provide a foundation for considering both the design and interpretation of studies

The Authors Respond

We read with keen interest Cinelli and Pearl’s response to our letter. A key difference in our approaches can be appreciated by examining the first line of each of our derivations

Assessing exposure effects on gene expression

In observational genomics data sets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted regression model does not necessarily estimate the overall effect of the exposure. Using inverse probability weighting (IPW) or the parametric g-formula in these instances is straightforward to apply and yields consistent effect estimates. IPW can readily be integrated into a genomics data analysis pipeline with upstream data processing and normalization, while the g-formula can be implemented by making simple alterations to the regression model. The regression, IPW, and g-formula approaches to exposure effect estimation are compared herein using simulations; advantages and disadvantages of each approach are explored. The methods are applied to a case study estimating the effect of current smoking on gene expression in adipose tissue

Unique Molecular Identifiers and Multiplexing Amplicons Maximize the Utility of Deep Sequencing To Critically Assess Population Diversity in RNA Viruses

Next generation sequencing (NGS)/deep sequencing has become an important tool in the study of viruses. The use of unique molecular identifiers (UMI) can overcome the limitations of PCR errors and PCR-mediated recombination and reveal the true sampling depth of a viral population being sequenced in an NGS experiment. This approach of enhanced sequence data represents an ideal tool to study both high and low abundance drug resistance mutations and more generally to explore the genetic structure of viral populations. Central to the use of the UMI/Primer ID approach is the creation of a template consensus sequence (TCS) for each genome sequenced. Here we describe a series of experiments to validate several aspects of the Multiplexed Primer ID (MPID) sequencing approach using the MiSeq platform. We have evaluated how multiplexing of cDNA synthesis and amplicons affects the sampling depth of the viral population for each individual cDNA and amplicon to understand the relationship between broader genome coverage versus maximal sequencing depth. We have validated reproducibility of the MPID assay in the detection of minority mutations in viral genomes. We have also examined the determinants that allow sequencing reads of PCR recombinants to contaminate the final TCS data set and show how such contamination can be limited. Finally, we provide several examples where we have applied MPID to analyze features of minority variants and describe limits on their detection in viral populations of HIV-1 and SARS-CoV-2 to demonstrate the generalizable utility of this approach with any RNA virus

The Authors Respond

We welcome the discussion by Huitfeldt and Stensrud on our recent article on generalizing study results. One assumption we listed in the set of sufficient conditions for generalizability was exchangeability between the study sample and the target population, perhaps conditional on a set of covariate

Causal impact: Epidemiological approaches for a public health of consequence

The causal impact framework is a conceptual framework encompassing internal validity, external validity, and population intervention effects, which we argue can help us produce evidence of greater utility to public health decision-making

Generalizing Study Results: A Potential Outcomes Perspective

Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example

Impact of human papillomavirus (HPV) self-collection on subsequent cervical cancer screening completion among under-screened US women: MyBodyMyTest-3 protocol for a randomized controlled trial

Background: Screening substantially reduces cervical cancer incidence and mortality. More than half of invasive cervical cancers are attributable to infrequent screening or not screening at all. The current study, My Body My Test (MBMT), evaluates the impact of mailed kits for self-collection of samples for human papillomavirus (HPV) testing on completion of cervical cancer screening in low-income, North Carolina women overdue for cervical cancer screening. Methods/design: The study will enroll at least 510 US women aged 25-64 years who report no Pap test in the last 4 years and no HPV test in the last 6 years. We will randomize participants to an intervention or control arm. The intervention arm will receive kits to self-collect a sample at home and mail it for HPV testing. In both the intervention and control arms, participants will receive assistance in scheduling an appointment for screening in clinic. Study staff will deliver HPV self-collection results by phone and assist in scheduling participants for screening in clinic. The primary outcome is completion of cervical cancer screening. Specifically, completion of screening will be defined as screening in clinic or receipt of negative HPV self-collection results. Women with HPV-negative self-collection results will be considered screening-complete. All other participants will be considered screening-complete if they obtain co-testing or Pap test screening at a study-affiliated institution or other clinic. We will assess whether the self-collection intervention influences participants' perceived risk of cervical cancer and whether perceived risk mediates the relationship between HPV self-collection results and subsequent screening in clinic. We also will estimate the incremental cost per woman screened of offering at-home HPV self-collection kits with scheduling assistance as compared to offering scheduling assistance alone. Discussion: If mailed self-collection of samples for HPV testing is an effective strategy for increasing cervical cancer screening among women overdue for screening, this method has the potential to reduce cervical cancer incidence and mortality in medically underserved women at higher risk of developing cervical cancer. Trial registration: ClinicalTrials.gov NCT02651883, Registered on 11 January 2016