Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies

Díaz, Iván; Williams, Nicholas; Hoffman, Katherine L.; Schenck, Edward J.

doi:10.1080/01621459.2021.1955691

Cited by 55 publications

(108 citation statements)

References 44 publications

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“…( 42 We proposed an approach of reformulating the research question of interest to alleviate some of these challenges that included: 1) bundling laws that cannot be disentangled together, and 2) considering an estimand of an effect of hypothetically shifting an exposure quantity by an amount supported by the observed data. (11) Because NAL enactment was closely correlated with GSL enactment, we bundled NAL with GSL, and estimated the hypothetical effects of increasing duration of NAL/GSL enactment by up to 2 years on naloxone dispensation and opioid overdose mortality. We estimated that such a shift in NAL/GSL duration would have been associated with increased naloxone dispensations, consistent with prior studies.…”

Section: Discussionmentioning

confidence: 99%

“…Proposal: Instead of explicitly comparing states as the above ATEs would entail, we instead reformulate the research question to correspond to the effect of shifting the distribution of the number of years since NAL/GSL enactment, corresponding to what is called a "modified treatment policy". (11,20) Under this reformulation, we ask: what would the effect on the opioid overdose mortality rate have been had late-enactor (early-enactor) states had their NAL/GSL for 2 years longer than their actual duration up to some maximum number of years (e.g., 5 years)? We can formulate it in terms of a hypothetical regime d(a) that depends on the natural exposure level a (i.e., under no intervention).…”

Section: Estimandsmentioning

confidence: 99%

“…To estimate these quantities, we use a doubly robust substitution estimator of a modified treatment policy. (11) We incorporate an ensemble of machine learning algorithms into model fitting(51)specifically, generalized linear models and gradient boosted machines. Interference( 49) is likely present,(13) as characteristics (covariates, exposures) of one county may influence the potential naloxone dispensing rate and potential fatal opioid overdose rate in a nearby county, which may bias estimates.…”

Section: Estimatormentioning

confidence: 99%

“…for all a t ∈ 0, 1 and all t ∈ 2013-2018 (2007-2013) for late-enactors (early-enactors). We use a cross-fitted (5 folds), longitudinal targeted minimum loss-based estimator of a longitudinal additive treatment effect, (11,50) including the following machine learning algorithms: intercept-only models, generalized linear models, lasso, multiple additive regression splines, and extreme gradient boosting.…”

Section: A Sensitivity Analysismentioning

confidence: 99%

“…(34,38) With these challenges in mind, we adopt option 1) to address the frequent co-enactment of NAL with GSL, considering them together as a bundle, and adopt option 2) to address other, opioid-related laws, refining the research question to correspond to a hypothetical intervention that "shifts" the exposure distribution to weaken the positivity assumption. (11) We estimate the associations between longer duration of a state NAL/GSL and naloxone pharmacy dispensations, one of the proximal outcomes that NALs are designed to affect, and opioid overdose mortality rates, the longer-term outcome and ultimate target of such laws. Thus, our objective is not only to provide evidence of the extent to which NAL/GSLs may affect relevant opioid-related outcomes, but also to offer a proposed strategy for answering such policy questions more generally.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws

Rudolph¹,

Gimbrone²,

Matthay³

et al. 2021

Preprint

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Background: All states in the US have enacted at least some naloxone access laws (NALs) in an effort to reduce opioid overdose lethality. Previous evaluations found NALs increased naloxone dispensing but showed mixed results in terms of opioid overdose mortality. One reason for mixed results could be failure to address violations of the positivity assumption caused by the co-occurrence of NAL enactment with enactment of related laws, ultimately resulting in bias, increased variance, and low statistical power. Methods:We reformulated the research question to alleviate some challenges related to law co-occurrence. Because NAL enactment was closely correlated with Good Samaritan Law (GSL) enactment, we bundled NAL with GSL, and estimated the hypothetical associations of enacting NAL/GSL up to 2 years earlier (an amount supported by the observed data) on naloxone dispensation and opioid overdose mortality. Results:We estimated that such a shift in NAL/GSL duration would have been associated with increased naloxone dispensations (0.28 dispensations/100,000 people (95%

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Section: Discussionmentioning

confidence: 99%

Section: Estimandsmentioning

confidence: 99%

Section: Estimatormentioning

confidence: 99%

Section: A Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws

Rudolph¹,

Gimbrone²,

Matthay³

et al. 2021

Preprint

View full text Add to dashboard Cite

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Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality

et al. 2022

Self Cite

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ImportanceCommunication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data.ObjectiveTo compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models.Design, Setting, and ParticipantsThis retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022.ExposuresCorticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia).Main Outcomes and MeasuresThe main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark.ResultsA total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47).Conclusions and RelevanceThese findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.

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The Relationship of Pain Reduction With Prevention of Knee Replacement Under Dynamic Intervention Strategies

Jafarzadeh

Neogi

White

et al. 2022

Arthritis & Rheumatology

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Objective. Knee replacement (KR) rates are increasing exponentially in the US and straining insurance budgets. This study was undertaken to investigate how many KRs would be prevented at different levels of pain improvement, a major target of osteoarthritis (OA) trials.Methods. We used data from the Osteoarthritis Initiative (OAI) to emulate a trial of knee pain interventions on KR risk changes. We modeled hypothetical 1-, 2-or 3-unit reductions of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale whenever a person reported a pain score of ≥5 (of 20) in an affected knee at any clinic visit. We used causal inference-based targeted learning to estimate treatment effects for hypothesized pain intervention strategies adjusted for time-dependent confounding. Sensitivity analyses assessed interventions at WOMAC pain scores of ≥4 and ≥7.Results. Of the 9,592 knees studied (n = 4,796 participants; 58.5% female; baseline age 61.2 years), 40.7% experienced WOMAC pain scores of ≥5. The estimated knee-level (reference) risk of a KR, adjusted for loss to follow-up and death, was 6.3% (95% confidence interval 5.0, 7.7%) in the OAI. Reductions of WOMAC pain scores by 1, 2, or 3 units decreased the KR risk from 6.3% to 5.8%, 5.3%, and 4.9%, respectively. Larger reductions in KR risk were achieved when interventions were applied at a WOMAC pain score of ≥4.Conclusion. Modest pain reductions from OA interventions would substantially reduce the number of KRs, with greater reductions achieved when pain decreased more and when interventions were introduced at lower pain levels.

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Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies

Cited by 55 publications

References 44 publications

When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws

When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws

Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality

The Relationship of Pain Reduction With Prevention of Knee Replacement Under Dynamic Intervention Strategies

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