Introduction
Observational healthcare data, comprising administrative claims and
electronic health records, present a rich source for generating
real-world evidence pertinent to treatment effects that directly impact
patient well-being. Within this realm, population-level effect
estimation assumes a pivotal role, focusing on elucidating the average
causal effects of exposures—such as medical interventions like drug
exposures or procedures—on specific health outcomes of interest.
Population-level effect estimation delves into two primary realms:
direct effect estimation and comparative effect estimation. In direct
effect estimation, the focus lies on discerning the effect of an
exposure on the risk of an outcome compared to no exposure, while
comparative effect estimation aims to delineate the effect of a target
exposure against a comparator exposure. By contrasting factual outcomes
with counterfactual scenarios—what happened versus what would have
occurred under different circumstances—these estimation tasks offer
critical insights into treatment selection, safety surveillance, and
comparative effectiveness. Whether probing individual hypotheses or
exploring multiple hypotheses concurrently, the overarching goal remains
consistent: to derive high-quality estimates of causal effects from the
intricate fabric of observational healthcare data.
Features and Functionalities
The CohortMethod R package, a cornerstone of
population-level estimation within the OHDSI framework, offers a robust
methodology for conducting comparative effectiveness research and
pharmacoepidemiology studies. Some of the features offered by conducting
population-level effect estimation using the CohortMethod module
are:
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Data Extraction: Extracts necessary data from
databases structured in the OMOP Common Data Model (CDM) format,
ensuring uniformity and compatibility across diverse healthcare
settings.
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Covariate Selection: Utilizing a comprehensive set
of covariates, including drugs, diagnoses, procedures, age, and
comorbidity indexes, CohortMethod constructs propensity and outcome
models tailored to specific research questions.
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Large-Scale Regularized Regression: Employing
large-scale regularized regression techniques, CohortMethod fits
propensity and outcome models with precision and efficiency,
accommodating the complexities of real-world healthcare data.
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Propensity Score Adjustment: Facilitates propensity
score adjustment through trimming, stratification, matching, and
weighting, enabling researchers to address confounding and balance
covariate distributions across treatment groups. Results are viewable
both graphically and in tabular form to assess the model.
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Diagnostic Functions: Diagnostic functions within
CohortMethod offer insights into propensity score distributions and
covariate balance before and after matching or trimming, enhancing
transparency and robustness in estimation procedures.
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Supported Outcome Models: Supported outcome models
include (conditional) logistic regression, (conditional) Poisson
regression, and (conditional) Cox regression, providing flexibility in
modeling various types of outcomes in observational health data
research.
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Power: Incorporates power analysis techniques to
estimate the statistical power of the study design, aiding in sample
size determination and study planning, and provides a minimum-detectable
relative risk (MDRR) statistic.
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Attrition: Assesses attrition rates within cohorts,
providing insights into potential biases introduced by data loss during
the study period, and provides a visualization of attrition across
various cohort criteria.
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Population Characteristics: Analyzes population
characteristics to understand the demographic and clinical makeup of the
study cohorts, informing interpretation of estimation results both
before and after propensity score matching.
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Covariate Balance: Visually monitors covariate
balance before and after matching or trimming, ensuring that confounding
variables are adequately controlled for in the analysis.
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Systematic Error: Assesses effect size estimates
for negative controls (true hazard ratio = 1) and positive controls
(true hazard ratio > 1) both before and after calibration. Estimates
below the diagonal dashed lines are statistically significant (alpha =
0.05) different from the true effect size. A well-calibrated estimator
should have the true effect size within the 95 percent confidence
interval 95 percent of times, providing researchers with confidence in
the reliability of the estimation process and the accuracy of the
obtained results.
Utility and Application
Comparative Effectiveness Research: CohortMethod
empowers researchers to conduct comparative effectiveness studies by
estimating treatment effects while accounting for potential confounding
factors and bias inherent in observational data.
Pharmacoepidemiology and Drug Safety Studies: In
pharmacoepidemiology research, CohortMethod facilitates the evaluation
of drug safety and effectiveness by quantifying the association between
drug exposures and clinical outcomes in real-world populations.