Variable Selection When Estimating Effects in External Target Populations.

External validity is an important part of epidemiologic research. To validly estimate effects in specific external target populations using a chosen effect measure (i.e., "transport"), some methods require that one account for all effect measure modifiers [EMMs]. However, little is known about how including other variables that are not EMMs (i.e., non-EMMs) in adjustment sets impacts estimates. Using simulations, we evaluated how inclusion of non-EMMs affected estimation of the transported risk difference (RD) by assessing impacts of covariates that A) differ (or not) between the trial and the target, B) are associated with the outcome (or not), and C) modify the RD (or not). We assessed variation and bias when covariates with each possible combination of these factors were used to transport RDs using outcome modeling or inverse odds weighting. Including variables that differed in distribution between the populations but were non-EMMs reduced precision, regardless of whether they were associated with the outcome. However, non-EMMs associated with selection did not amplify bias resulting from omitting necessary EMMs. Including all variables associated with the outcome may result in unnecessarily imprecise estimates when estimating treatment effects in external target populations.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

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.

Mass Probation: Effects of Sentencing Severity on Mental Health for Black and White Individuals.

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.

Missing Outcome Data in Epidemiologic Studies.

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.

Accounting for nonmonotone missing data using inverse probability weighting.

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.

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies.

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations.

Initiator Types and the Causal Question of the Prevalent New-User Design: A Simulation Study.

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.

Meta-Analysis and Sparse-Data Bias.

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases.

Decreased Susceptibility of Marginal Odds Ratios to Finite-sample Bias.

Parameters representing adjusted treatment effects may be defined marginally or conditionally on covariates. The choice between a marginal or covariate-conditional parameter should be driven by the study question. However, an unappreciated benefit of marginal estimators is a reduction in susceptibility to finite-sample bias relative to the unpenalized maximum likelihood estimator of the covariate-conditional odds ratio (OR). Using simulation, we compare the finite-sample bias of different marginal and conditional estimators of the OR. We simulated a logistic model to have 15 events per parameter and two events per parameter. We estimated the covariate-conditional OR by maximum likelihood with and without Firth's penalization. We used three estimators of the marginal OR: g-computation, inverse probability of treatment weighting, and augmented inverse probability of treatment weighting. At 15 events per parameter, as expected, all estimators were effectively unbiased. At two events per parameter, the unpenalized covariate-conditional estimator was notably biased but penalized covariate-conditional and marginal estimators exhibited minimal bias.

When Is a Complete-Case Approach to Missing Data Valid? The Importance of Effect-Measure Modification.

When estimating causal effects, careful handling of missing data is needed to avoid bias. Complete-case analysis is commonly used in epidemiologic analyses. Previous work has shown that covariate-stratified effect estimates from complete-case analysis are unbiased when missingness is independent of the outcome conditional on the exposure and covariates. Here, we assess the bias of complete-case analysis for adjusted marginal effects when confounding is present under various causal structures of missing data. We show that estimation of the marginal risk difference requires an unbiased estimate of the unconditional joint distribution of confounders and any other covariates required for conditional independence of missingness and outcome. The dependence of missing data on these covariates must be considered to obtain a valid estimate of the covariate distribution. If none of these covariates are effect-measure modifiers on the absolute scale, however, the marginal risk difference will equal the stratified risk differences and the complete-case analysis will be unbiased when the stratified effect estimates are unbiased. Estimation of unbiased marginal effects in complete-case analysis therefore requires close consideration of causal structure and effect-measure modification.

Association of Broad- vs Narrow-Spectrum Antibiotics With Treatment Failure, Adverse Events, and Quality of Life in Children With Acute Respiratory Tract Infections.

Importance: Acute respiratory tract infections account for the majority of antibiotic exposure in children, and broad-spectrum antibiotic prescribing for acute respiratory tract infections is increasing. It is not clear whether broad-spectrum treatment is associated with improved outcomes compared with narrow-spectrum treatment. Objective: To compare the effectiveness of broad-spectrum and narrow-spectrum antibiotic treatment for acute respiratory tract infections in children. Design, Setting, and Participants: A retrospective cohort study assessing clinical outcomes and a prospective cohort study assessing patient-centered outcomes of children between the ages of 6 months and 12 years diagnosed with an acute respiratory tract infection and prescribed an oral antibiotic between January 2015 and April 2016 in a network of 31 pediatric primary care practices in Pennsylvania and New Jersey. Stratified and propensity score-matched analyses to account for confounding by clinician and by patient-level characteristics, respectively, were implemented for both cohorts. Exposures: Broad-spectrum antibiotics vs narrow-spectrum antibiotics. Main Outcomes and Measures: In the retrospective cohort, the primary outcomes were treatment failure and adverse events 14 days after diagnosis. In the prospective cohort, the primary outcomes were quality of life, other patient-centered outcomes, and patient-reported adverse events. Results: Of 30 159 children in the retrospective cohort (19 179 with acute otitis media; 6746, group A streptococcal pharyngitis; and 4234, acute sinusitis), 4307 (14%) were prescribed broad-spectrum antibiotics including amoxicillin-clavulanate, cephalosporins, and macrolides. Broad-spectrum treatment was not associated with a lower rate of treatment failure (3.4% for broad-spectrum antibiotics vs 3.1% for narrow-spectrum antibiotics; risk difference for full matched analysis, 0.3% [95% CI, -0.4% to 0.9%]). Of 2472 children enrolled in the prospective cohort (1100 with acute otitis media; 705, group A streptococcal pharyngitis; and 667, acute sinusitis), 868 (35%) were prescribed broad-spectrum antibiotics. Broad-spectrum antibiotics were associated with a slightly worse child quality of life (score of 90.2 for broad-spectrum antibiotics vs 91.5 for narrow-spectrum antibiotics; score difference for full matched analysis, -1.4% [95% CI, -2.4% to -0.4%]) but not with other patient-centered outcomes. Broad-spectrum treatment was associated with a higher risk of adverse events documented by the clinician (3.7% for broad-spectrum antibiotics vs 2.7% for narrow-spectrum antibiotics; risk difference for full matched analysis, 1.1% [95% CI, 0.4% to 1.8%]) and reported by the patient (35.6% for broad-spectrum antibiotics vs 25.1% for narrow-spectrum antibiotics; risk difference for full matched analysis, 12.2% [95% CI, 7.3% to 17.2%]). Conclusions and Relevance: Among children with acute respiratory tract infections, broad-spectrum antibiotics were not associated with better clinical or patient-centered outcomes compared with narrow-spectrum antibiotics, and were associated with higher rates of adverse events. These data support the use of narrow-spectrum antibiotics for most children with acute respiratory tract infections.

Value of Procalcitonin Measurement for Early Evidence of Severe Bacterial Infections in the Pediatric Intensive Care Unit.

OBJECTIVES: To determine whether peak blood procalcitonin (PCT) measured within 48 hours of pediatric intensive care unit (PICU) admission can differentiate severe bacterial infections from sterile inflammation and viral infection and identify potential subgroups of PICU patients for whom PCT may not have clinical utility. STUDY DESIGN: This was a retrospective, observational study of 646 critically ill children who had PCT measured within 48 hours of admission to an urban, academic PICU. Patients were stratified into 6 categories by infection status. We compared test characteristics for peak PCT, C-reactive protein (CRP), white blood cell count (WBC), absolute neutrophil count (ANC), and % immature neutrophils. The area under the receiver operating characteristic curve was determined for each biomarker to discriminate bacterial infection. RESULTS: The area under the receiver operating characteristic curve was similar for PCT (0.73, 95% CI 0.69, 0.77) and CRP (0.75, 95% CI 0.71, 0.79; P = .36), but both outperformed WBC, ANC, and % immature neutrophils (P < .01 for all pairwise comparisons). The combination of PCT and CRP was no better than either PCT or CRP alone. Diagnostic patterns prone to false-positive and false-negative PCT values were identified. CONCLUSIONS: Peak blood PCT measured close to PICU admission was not superior to CRP in differentiating severe bacterial infection from viral illness and sterile inflammation; both PCT and CRP outperformed WBC, ANC, and % immature neutrophils. PCT appeared especially prone to inaccuracies in detecting localized bacterial central nervous system infections or bacterial coinfection in acute viral illness causing respiratory failure.

Antibiotic Exposure During the First 6 Months of Life and Weight Gain During Childhood.

IMPORTANCE: Early-life antibiotic exposure has been associated with increased adiposity in animal models, mediated through the gut microbiome. Infant antibiotic exposure is common and often inappropriate. Studies of the association between infant antibiotics and childhood weight gain have reported inconsistent results. OBJECTIVE: To assess the association between early-life antibiotic exposure and childhood weight gain. DESIGN AND SETTING: Retrospective, longitudinal study of singleton births and matched longitudinal study of twin pairs conducted in a network of 30 pediatric primary care practices serving more than 200,000 children of diverse racial and socioeconomic backgrounds across Pennsylvania, New Jersey, and Delaware. PARTICIPANTS: Children born between November 1, 2001, and December 31, 2011, at 35 weeks' gestational age or older, with birth weight of 2000 g or more and in the fifth percentile or higher for gestational age, and who had a preventive health visit within 14 days of life and at least 2 additional visits in the first year of life. Children with complex chronic conditions and those who received long-term antibiotics or multiple systemic corticosteroid prescriptions were excluded. We included 38,522 singleton children and 92 twins (46 matched pairs) discordant in antibiotic exposure. Final date of follow-up was December 31, 2012. EXPOSURE: Systemic antibiotic use in the first 6 months of life. MAIN OUTCOMES AND MEASURES: Weight, measured at preventive health visits from age 6 months through 7 years. RESULTS: Of 38,522 singleton children (50% female; mean birth weight, 3.4 kg), 5287 (14%) were exposed to antibiotics during the first 6 months of life (at a mean age of 4.3 months). Antibiotic exposure was not significantly associated with rate of weight change (0.7%; 95% CI, -0.1% to 1.5%; P = .07, equivalent to approximately 0.05 kg; 95% CI, -0.004 to 0.11 kg of added weight gain between age 2 years and 5 years). Among 92 twins (38% female; mean birth weight, 2.8 kg), the 46 twins who were exposed to antibiotics during the first 6 months of life received them at a mean age of 4.5 months. Antibiotic exposure was not significantly associated with a weight difference (-0.09 kg; 95% CI, -0.26 to 0.08 kg; P = .30). CONCLUSIONS AND RELEVANCE: Exposure to antibiotics within the first 6 months of life compared with no exposure was not associated with a statistically significant difference in weight gain through age 7 years. There are many reasons to limit antibiotic exposure in young, healthy children, but weight gain is likely not one of them.

Decreasing rates of invasive candidiasis in pediatric hospitals across the United States.

Rates of invasive candidiasis (IC) in children between 2003 and 2011 were evaluated in a retrospective cross-sectional analysis. The rate of IC decreased 72% (P < .001) overall and 91% in neonates (P < .001). Improving infection control efforts is thought to be a contributing factor for this decrease.

Impact of Infectious Diseases Society of America/Pediatric Infectious Diseases Society guidelines on treatment of community-acquired pneumonia in hospitalized children.

We examined the impact of the Pediatric Infectious Diseases Society/Infectious Diseases Society of America guidelines that recommend ampicillin or amoxicillin for children hospitalized with community-acquired pneumonia. Prescribing of ampicillin/amoxicillin increased following guideline publication, but remains low. Cephalosporin and macrolide prescribing decreased but remains common. Further studies exploring outcomes of and reasons for compliance with guidelines are warranted.

M-estimation for common epidemiological measures: introduction and applied examples.

M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist's toolbox.

Comparative effectiveness of extended release naltrexone and sublingual buprenorphine for treatment of opioid use disorder among Medicaid patients.

Aims: To compare the real-world effectiveness of extended release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) for the treatment of opioid use disorder (OUD). Design: An observational active comparator, new user cohort study. Setting: Medicaid claims records for patients in New Jersey and California, 2016-2019. Participants/Cases: 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. Comparators: New initiation with XR-NTX versus SL-BUP for the treatment of OUD. 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: Our cohort included 1,755 XR-NTX and 9,886 SL-BUP patients. 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 76% (95% confidence interval [CI] 75%, 78%) versus 62% (95% CI 61%, 63%), respectively (risk difference 14 percentage points, 95% CI 13, 16). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.8% (95% CI 2.9%, 4.7%) versus SL-BUP 3.3% (95% 2.9%, 3.7%); risk difference 0.5 percentage points, 95%CI -0.5, 1.5. Results were consistent across sensitivity analyses. Conclusions: Longer medication retention is important because risks of negative outcomes are elevated after discontinuation. Our results support selection of SL-BUP over XR-NTX. However, most patients discontinued medication by 6 months indicating that more effective tools are needed to improve medication retention, particularly after initiation with XR-NTX, and to identify which patients do best on which medication.