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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]

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results. [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]

BACKGROUND/OBJECTIVES: A claims‐based model predicting 5‐year mortality (Lund‐Lewis) was developed in a 2008 cohort of North Carolina (NC) Medicare beneficiaries and included indicators of comorbid conditions, frailty, disability, and functional impairment. The objective of this study was to validate the Lund‐Lewis model externally within a nationwide sample of Medicare beneficiaries. DESIGN: Retrospective validation study. SETTING: U.S. Medicare population. PARTICIPANTS: From a random sample of Medicare beneficiaries, we created four annual cohorts from 2008 to 2011 of individuals aged 66 and older with an office visit in that year. The annual cohorts ranged from 1.13 to 1.18 million beneficiaries. MEASUREMENTS: The outcome was 5‐year all‐cause mortality. We assessed clinical indicators in the 12 months before the qualifying office visit and estimated predicted 5‐year mortality for each beneficiary in the nationwide sample by applying estimates derived in the original NC cohort. Model performance was assessed by quantifying discrimination, calibration, and reclassification metrics compared with a model fit on a comorbidity score. RESULTS: Across the annual cohorts, 5‐year mortality ranged from 24.4% to 25.5%. The model had strong discrimination (C‐statistics ranged across cohorts from.823 to.826). Reclassification measures showed improvement over a comorbidity score model for beneficiaries who died but reduced performance among beneficiaries who survived. The calibration slope ranged from.83 to.86; the model generally predicted a higher risk than observed. CONCLUSION: The Lund‐Lewis model showed strong and consistent discrimination in a national U.S. Medicare sample, although calibration indicated slight overfitting. Future work should investigate methods for improving model calibration and evaluating performance within specific disease settings. [ABSTRACT FROM AUTHOR]