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Iterated conditional expectation (ICE) g‐computation is an estimation approach for addressing time‐varying confounding for both longitudinal and time‐to‐event data. Unlike other g‐computation implementations, ICE avoids the need to specify models for each time‐varying covariate. For variance estimation, previous work has suggested the bootstrap. However, bootstrapping can be computationally intense. Here, we present ICE g‐computation as a set of stacked estimating equations. Therefore, the variance for the ICE g‐computation estimator can be consistently estimated using the empirical sandwich variance estimator. Performance of the variance estimator was evaluated empirically with a simulation study. The proposed approach is also demonstrated with an illustrative example on the effect of cigarette smoking on the prevalence of hypertension. In the simulation study, the empirical sandwich variance estimator appropriately estimated the variance. When comparing runtimes between the sandwich variance estimator and the bootstrap for the applied example, the sandwich estimator was substantially faster, even when bootstraps were run in parallel. The empirical sandwich variance estimator is a viable option for variance estimation with ICE g‐computation. [ABSTRACT FROM AUTHOR]

Background and aims: Extended release buprenorphine injection (INJ‐BUP) has been available in the United States since 2018. INJ‐BUP has the potential to positively impact opioid use disorder (OUD) treatment outcomes by providing additional treatment options. As one of the largest payers of OUD treatment in the US, Medicaid coverage is important for access and uptake of INJ‐BUP. Uptake of INJ‐BUP among Medicaid beneficiaries has not been described since 2019 and variation in uptake by state has not previously been explored. We aimed to measure prescribing of INJ‐BUP for Medicaid beneficiaries since 2018, nationwide and by state. Methods: We analyzed State Drug Utilization Data from 2017 to 2022 and calculated the number of prescription fills for INJ‐BUP and oral buprenorphine paid by Medicaid. To compare across states, we calculated the number of prescription fills per 100 Medicaid beneficiaries treated for OUD using data from Transformed Medicaid Statistical Information System Substance Use Disorder (T‐MSIS SUD) Data Books. Data sources are publicly available. Results: The number of prescription fills for INJ‐BUP paid by Medicaid increased from 4322 (0.1% of all buprenorphine prescription fills) in 2018 to 186 861 (2.0%) in 2022. Each year the increase in fills exceeded the prior year change, indicating accelerating uptake. There was notable variability across states. Conclusions: The number of extended release buprenorphine injection prescriptions among US Medicaid beneficiaries treated for opioid use disorder increased from over 4000 prescriptions in 2018 to over 185 000 in 2022 but uptake is much less than observed in other countries over shorter time periods. [ABSTRACT FROM AUTHOR]

Background and aims: Extended‐release naltrexone (XR‐NTX) and sublingual buprenorphine (SL‐BUP) are both approved for opioid use disorder (OUD) treatment in any medical setting. We aimed to compare the real‐world effectiveness of XR‐NTX and SL‐BUP. Design and setting: This was an observational active comparator, new user cohort study of Medicaid claims records for patients in New Jersey and California, USA, 2016–19. Participants/cases: The participants were adult Medicaid patients aged 18–64 years who initiated XR‐NTX or SL‐BUP for maintenance treatment of OUD and did not use medications for OUD in the 90 days before initiation. Our cohort included 1755 XR‐NTX and 9886 SL‐BUP patients. Measurements: We examined two outcomes up to 180 days after medication initiation: (1) composite of medication discontinuation and death and (2) composite of overdose and death. Findings In adjusted analyses, treatment with XR‐NTX was more likely to result in discontinuation or death by the end of follow‐up than treatment with SL‐BUP: cumulative risk 75.9% [95% confidence interval (CI) = 73.9%, 77.9%] versus 62.2% (95% CI = 61.2%, 63.2%), respectively (risk difference = 13.7 percentage points, 95% CI = 11.4, 16.0). There was minimal difference in the cumulative risk of overdose or death by the end of follow‐up: XR‐NTX 3.9% (95% CI = 3.0%, 4.8%) versus SL‐BUP 3.3% (95% CI = 2.9%, 3.7%); risk difference = 0.5 percentage points, 95% CI = –0.4, 1.5. Results were consistent across sensitivity analyses. Conclusions: Medicaid patients in California and New Jersey, USA, receiving treatment for opioid use disorder stayed in treatment longer on sublingual buprenorphine than on extended‐release naltrexone, but the risk of overdose was similar. Most patients in this study discontinued medication within 6 months, regardless of which medication was initiated. [ABSTRACT FROM AUTHOR]

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods. [ABSTRACT FROM AUTHOR]

Background: Incarceration is associated with negative impacts on mental health. Probation, a form of community supervision, has been lauded as an alternative. However, the effect of probation versus incarceration on mental health is unclear. Our objective was to estimate the impact on mental health of reducing sentencing severity at individuals' first adult criminal–legal encounter. Methods: We used the US National Longitudinal Survey on Youth 1997, a nationally representative dataset of youth followed into their mid-thirties. Restricting to those with an adult encounter (arrest, charge alone or no sentence, probation, incarceration), we used parametric g-computation to estimate the difference in mental health at age 30 (Mental Health Inventory-5) if (1) everyone who received incarceration for their first encounter had received probation and (2) everyone who received probation had received no sentence. Results: Among 1835 individuals with adult encounters, 19% were non-Hispanic Black and 65% were non-Hispanic White. Median age at first encounter was 20. Under hypothetical interventions to reduce sentencing, we did not see better mental health overall (Intervention 1, incarceration to probation: RD = −0.01; CI = −0.02, 0.01; Intervention 2, probation to no sentence: RD = 0.00; CI = −0.01, 0.01) or when stratified by race. Conclusion: Among those with criminal–legal encounters, hypothetical interventions to reduce sentencing, including incremental sentencing reductions, were not associated with improved mental health. Future work should consider the effects of preventing individuals' first criminal–legal encounter. [ABSTRACT FROM AUTHOR]

Objective: To illustrate the difference between exposure effects and population attributable effects. Methods: We examined the effect of mid‐pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g‐computation. Results: Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80–4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02–1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth. Conclusion: Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective. Synopsis: Multiple types of effects can be estimated from observational data, and researchers should estimate the type of effect that aligns with the stated research objective. [ABSTRACT FROM AUTHOR]

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology. [ABSTRACT FROM AUTHOR]

Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and how assumptions of the missing data mechanisms affect this construction. We use a simulation to illustrate the estimation and application of the weights in the two examples. Notably, when only outcome data are missing, construction of combined weights is straightforward; however, when confounder data are missing, we show that in general we must follow a specific estimation procedure which entails first estimating missingness weights and then estimating treatment probabilities from data with missingness weights applied. However, if treatment and missingness are conditionally independent, then treatment probabilities can be estimated among the complete cases. [ABSTRACT FROM AUTHOR]

New-user designs restricting to treatment initiators have become the preferred design for studying drug comparative safety and effectiveness using nonexperimental data. This design reduces confounding by indication and healthy-adherer bias at the cost of smaller study sizes and reduced external validity, particularly when assessing a newly approved treatment compared with standard treatment. The prevalent new-user design includes adopters of a new treatment who switched from or previously used standard treatment (i.e. the comparator), expanding study sample size and potentially broadening the study population for inference. Previous work has suggested the use of time-conditional propensity-score matching to mitigate prevalent user bias. In this study, we describe 3 "types" of initiators of a treatment: new users, direct switchers, and delayed switchers. Using these initiator types, we articulate the causal questions answered by the prevalent new-user design and compare them with those answered by the new-user design. We then show, using simulation, how conditioning on time since initiating the comparator (rather than full treatment history) can still result in a biased estimate of the treatment effect. When implemented properly, the prevalent new-user design estimates new and important causal effects distinct from the new-user design. [ABSTRACT FROM AUTHOR]