Relative sparsity for medical decision problems.

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (eg, whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (ie, the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields "relative sparsity," where, as a function of a tuning parameter, λ $$ \lambda $$ , we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (eg, heart rate only). We propose a criterion for selecting λ $$ \lambda $$ , perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.

Group sequential testing under instrumented difference-in-differences approach.

Unmeasured confounding is a major obstacle to reliable causal inference based on observational studies. Instrumented difference-in-differences (iDiD), a novel idea connecting instrumental variable and standard DiD, ameliorates the above issue by explicitly leveraging exogenous randomness in an exposure trend. In this article, we utilize the above idea of iDiD, and propose a novel group sequential testing method that provides valid inference even in the presence of unmeasured confounders. At each time point, we estimate the average or conditional average treatment effect under iDiD setting using the data accumulated up to that time point, and test the significance of the treatment effect. We derive the joint distribution of the test statistics under the null using the asymptotic properties of M-estimation, and the group sequential boundaries are obtained using the α $$ \alpha $$ -spending functions. The performance of our proposed approach is evaluated on both synthetic data and Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN) to examine the association between rofecoxib and acute myocardial infarction, and our method detects significant adverse effect of rofecoxib much earlier than the time when it was finally withdrawn from the market.

Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.

Existing methods for estimation of dynamic treatment regimes are mostly limited to intention-to-treat analyses-which estimate the effect of randomization to a particular treatment regime without considering the compliance behavior of patients. In this article, we propose a novel nonparametric Bayesian Q-learning approach to construct optimal sequential treatment regimes that adjust for partial compliance. We consider the popular potential compliance framework, where some potential compliances are latent and need to be imputed. The key challenge is learning the joint distribution of the potential compliances, which we accomplish using a Dirichlet process mixture model. Our approach provides two kinds of treatment regimes: (1) conditional regimes that depend on the potential compliance values; and (2) marginal regimes where the potential compliances are marginalized. Extensive simulation studies highlight the usefulness of our method compared to intention-to-treat analyses. We apply our method to the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) study , where the goal is to construct optimal treatment regimes to engage patients in therapy.

A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making.

Existing methods for estimating the mean outcome under a given sequential treatment rule often rely on intention-to-treat analyses, which estimate the effect of following a certain treatment rule regardless of compliance behavior of patients. There are two major concerns with intention-to-treat analyses: (1) the estimated effects are often biased toward the null effect; (2) the results are not generalizable and reproducible due to the potentially differential compliance behavior. These are particularly problematic in settings with a high level of non-compliance, such as substance use disorder studies. Our work is motivated by the Adaptive Treatment for Alcohol and Cocaine Dependence study (ENGAGE), which is a multi-stage trial that aimed to construct optimal treatment strategies to engage patients in therapy. Due to the relatively low level of compliance in this trial, intention-to-treat analyses essentially estimate the effect of being randomized to a certain treatment, instead of the actual effect of the treatment. We obviate this challenge by defining the target parameter as the mean outcome under a dynamic treatment regime conditional on a potential compliance stratum. We propose a flexible non-parametric Bayesian approach based on principal stratification, which consists of a Gaussian copula model for the joint distribution of the potential compliances, and a Dirichlet process mixture model for the treatment sequence specific outcomes. We conduct extensive simulation studies which highlight the utility of our approach in the context of multi-stage randomized trials. We show robustness of our estimator to non-linear and non-Gaussian settings as well.

Effectiveness of a targeted brief intervention for recent suicide attempt survivors: a randomised controlled trial protocol.

INTRODUCTION: Effective, brief, low-cost interventions for suicide attempt survivors are essential to saving lives and achieving the goals of the National Strategy for Suicide Prevention and Zero Suicide. This study aims to examine the effectiveness of the Attempted Suicide Short Intervention Program (ASSIP) in averting suicide reattempts in the United States healthcare system, its psychological mechanisms as predicted by the Interpersonal Theory of Suicide, and the potential implementation costs, barriers and facilitators for delivering it. METHODS AND ANALYSIS: This study is a hybrid type 1 effectiveness-implementation randomised controlled trial (RCT). ASSIP is delivered at three outpatient mental healthcare clinics in New York State. Participant referral sites include three local hospitals with inpatient and comprehensive psychiatric emergency services, and outpatient mental health clinics. Participants include 400 adults who have had a recent suicide attempt. All are randomised to 'Zero Suicide-Usual Care plus ASSIP' or 'Zero Suicide-Usual Care'. Randomisation is stratified by sex and whether the index attempt is a first suicide attempt or not. Participants complete assessments at baseline, 6 weeks, and 3, 6, 12 and, 18 months. The primary outcome is the time from randomisation to the first suicide reattempt. Prior to the RCT, a 23-person open trial took place, in which 13 participants received 'Zero Suicide-Usual Care plus ASSIP' and 14 completed the first follow-up time point. ETHICS AND DISSEMINATION: This study is overseen by the University of Rochester, with single Institutional Review Board (#3353) reliance agreements from Nathan Kline Institute (#1561697) and SUNY Upstate Medical University (#1647538). It has an established Data and Safety Monitoring Board. Results will be published in peer-reviewed academic journals, presented at scientific conferences, and communicated to referral organisations. Clinics considering ASSIP may use a stakeholder report generated by this study, including incremental cost-effectiveness data from the provider point of view. TRIAL REGISTRATION NUMBER: NCT03894462.

Discussion on "Instrumental variable estimation of the causal hazard ratio," by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt.

We propose and study an augmented variant of the estimator proposed by Wang, Tchetgen Tchetgen, Martinussen, and Vansteelandt.

Rejoinder to "Instrumented difference-in-differences".

We thank all the discussants for the careful reading and insightful comments. In our rejoinder, we extend the discussion of how the assumptions of instrumented difference-in-differences (iDID) compare to the assumptions of the standard instrumental variable method. We also make additional comments on how iDID is related to the fuzzy DID. We highlight future research directions to enhance the utility of iDID, including extensions to adjust for covariate shift in two-sample iDID design, and generalization of iDID to multiple time points and a multi-valued instrumental variable for DID.

Nonparametric inverse-probability-weighted estimators based on the highly adaptive lasso.

Inverse-probability-weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an effect of interest by constructing a pseudopopulation in which selection biases are eliminated. Despite their ease of use, these estimators require the correct specification of a model for the weighting mechanism, are known to be inefficient, and suffer from the curse of dimensionality. We propose a class of nonparametric inverse-probability-weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at nearly n - 1 / 3 $ n^{-1/3}$ -rate to the true weighting mechanism. We demonstrate that our estimators are asymptotically linear with variance converging to the nonparametric efficiency bound. Unlike doubly robust estimators, our procedures require neither derivation of the efficient influence function nor specification of the conditional outcome model. Our theoretical developments have broad implications for the construction of efficient inverse-probability-weighted estimators in large statistical models and a variety of problem settings. We assess the practical performance of our estimators in simulation studies and demonstrate use of our proposed methodology with data from a large-scale epidemiologic study.

Instrumented difference-in-differences.

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference-in-differences, we propose a new method called instrumented difference-in-differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference-in-differences to a two-sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Selective inference for effect modification via the lasso.

Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the lasso to choose a low-complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method.

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

BACKGROUND: Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings. OBJECTIVE: The aim of this study was to derive a clinical-based IBI predictive model for febrile infants. DESIGNS, SETTING, AND PARTICIPANTS: This is a cross-sectional study of infants brought to two pediatric emergency departments from January 2011 to December 2018. Inclusion criteria were age 0-90 days, temperature ≥38°C, and documented gestational age, fever duration, and illness duration. MAIN OUTCOME AND MEASURES: To detect IBIs, we used regression and ensemble machine learning models and evidence-based predictors (i.e., sex, age, chronic medical condition, gestational age, appearance, maximum temperature, fever duration, illness duration, cough status, and urinary tract inflammation). We up-weighted infants with IBIs 8-fold and used 10-fold cross-validation to avoid overfitting. We calculated the area under the receiver operating characteristic curve (AUC), prioritizing a high sensitivity to identify the optimal cut-point to estimate sensitivity and specificity. RESULTS: Of 2311 febrile infants, 39 had an IBI (1.7%); the median age was 54 days (interquartile range: 35-71). The AUC was 0.819 (95% confidence interval: 0.762, 0.868). The predictive model achieved a sensitivity of 0.974 (0.800, 1.00) and a specificity of 0.530 (0.484, 0.575). Findings suggest that a clinical-based model can detect IBIs in febrile infants, performing similarly to serum biomarker-based models. This model may improve health equity by enabling clinicians to estimate IBI risk in any setting. Future studies should prospectively validate findings across multiple sites and investigate performance by age.

Derivation of a natural language processing algorithm to identify febrile infants.

BACKGROUND: Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed. OBJECTIVE: To derive and internally validate a natural language processing algorithm to identify febrile infants and compare its performance to diagnostic codes. METHODS: This cross-sectional study consisted of infants aged 0-90 days brought to one pediatric emergency department from January 2016 to December 2017. We aimed to identify infants with fever, defined as a documented temperature ≥38°C. We used 2017 clinical notes to develop two rule-based algorithms to identify infants with fever and tested them on data from 2016. Using manual abstraction as the gold standard, we compared performance of the two rule-based algorithms (Models 1, 2) to four previously published diagnostic code groups (Models 5-8) using area under the receiver-operating characteristics curve (AUC), sensitivity, and specificity. RESULTS: For the test set (n = 1190 infants), 184 infants were febrile (15.5%). The AUCs (0.92-0.95) and sensitivities (86%-92%) of Models 1 and 2 were significantly greater than Models 5-8 (0.67-0.74; 20%-74%) with similar specificities (93%-99%). In contrast to Models 5-8, samples from Models 1 and 2 demonstrated similar characteristics to the gold standard, including fever prevalence, median age, and rates of bacterial infections, hospitalizations, and severe outcomes. CONCLUSIONS: Findings suggest rule-based algorithms can accurately identify febrile infants with greater sensitivity while preserving specificity compared to diagnostic codes. If externally validated, rule-based algorithms may be important tools to create representative study samples, thereby improving generalizability of findings.

Individuals who text crisis text line: Key characteristics and opportunities for suicide prevention.

OBJECTIVE: Text-based crisis services are increasingly prominent, with inclusion in the national 988 crisis number launching in 2022. Yet little is known about who uses them. This study seeks to understand the population served by Crisis Text Line (CTL), the largest crisis text service in the United States. METHODS: Secondary data analysis was conducted on de-identified Crisis Counselor reports, texter post-conversation survey responses, and anonymized text conversation data from 85,877 texters who contacted CTL during a 12-month period. We examined Crisis Counselor's ratings of suicide ideation severity, texters' reports of race, gender, sexual orientation, recent mental health symptoms, and additional sources of help, and logs of frequency of contact. RESULTS: 76% of texters were under 25. 79% were female. 48% identified as other than heterosexual/straight. 64% had only one conversation. 79% were above the clinical cutoff for depression and 80% for anxiety, while 23% had thoughts of suicide. 23% received help from a doctor or therapist, and 28% received help only from CTL. CONCLUSIONS: CTL reaches a highly distressed, young, mostly female population, including typically underserved minorities and a substantial percentage of individuals who do not receive help elsewhere. These findings support the decision to include texting in the forthcoming national 988 implementation.

Crisis text-line interventions: Evaluation of texters' perceptions of effectiveness.

OBJECTIVE: Crisis Text Line (CTL), the largest provider of text-based crisis intervention services in the U.S., has answered nearly 7 million conversations since its inception in 2013. The study's objective was to assess texter's perceptions of the effectiveness of CTL crisis interventions. METHOD: Survey data completed by 85,877 texters linked to volunteer crisis counselor (CC) reports from October 12, 2017, to October 11, 2018 were analyzed. The relationship of several effectiveness measures with texters' demographic and psychosocial characteristics, frequency of CTL usage, and texters' perceptions of engagement with their CCs was examined using a series of logistic regression analyses. RESULTS: By the end of the text-based conversation, nearly 90% of suicidal texters reported that the conversation was helpful, and nearly half reported being less suicidal. CONCLUSIONS: Our study offers evidence for CTL's perceived effectiveness. These findings are of critical importance in light of the launch of a nationwide three-digit number (988) for suicide prevention and mental health crisis supports in the U.S., which will include texting.

Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants.

BACKGROUND AND OBJECTIVE: For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models. METHODS: This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities. RESULTS: Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%). CONCLUSIONS: Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

Bayesian set of best dynamic treatment regimes: Construction and sample size calculation for SMARTs with binary outcomes.

Sequential, multiple assignment, randomized trials (SMARTs) compare sequences of treatment decision rules called dynamic treatment regimes (DTRs). In particular, the Adaptive Treatment for Alcohol and Cocaine Dependence (ENGAGE) SMART aimed to determine the best DTRs for patients with a substance use disorder. While many authors have focused on a single pairwise comparison, addressing the main goal involves comparisons of >2 DTRs. For complex comparisons, there is a paucity of methods for binary outcomes. We fill this gap by extending the multiple comparisons with the best (MCB) methodology to the Bayesian binary outcome setting. The set of best is constructed based on simultaneous credible intervals. A substantial challenge for power analysis is the correlation between outcome estimators for distinct DTRs embedded in SMARTs due to overlapping subjects. We address this using Robins' G-computation formula to take a weighted average of parameter draws obtained via simulation from the parameter posteriors. We use non-informative priors and work with the exact distribution of parameters avoiding unnecessary normality assumptions and specification of the correlation matrix of DTR outcome summary statistics. We conduct simulation studies for both the construction of a set of optimal DTRs using the Bayesian MCB procedure and the sample size calculation for two common SMART designs. We illustrate our method on the ENGAGE SMART. The R package SMARTbayesR for power calculations is freely available on the Comprehensive R Archive Network (CRAN) repository. An RShiny app is available at https://wilart.shinyapps.io/shinysmartbayesr/.

Estimating mean potential outcome under adaptive treatment length strategies in continuous time.

An adaptive treatment length strategy is a sequential stage-wise treatment strategy where a subject's treatment begins at baseline and one chooses to stop or continue treatment at each stage provided the subject has been continuously treated. The effects of treatment are assumed to be cumulative and, therefore, the effect of treatment length on clinical endpoint, measured at the end of the study, is of primary scientific interest. At the same time, adverse treatment-terminating events may occur during the course of treatment that require treatment be stopped immediately. Because the presence of a treatment-terminating event may be strongly associated with the study outcome, the treatment-terminating event is informative. In observational studies, decisions to stop or continue treatment depend on covariate history that confounds the relationship between treatment length on outcome. We propose a new risk-set weighted estimator of the mean potential outcome under the condition that time-dependent covariates update at a set of common landmarks. We show that our proposed estimator is asymptotically linear given mild assumptions and correctly specified working models. Specifically, we study the theoretical properties of our estimator when the nuisance parameters are modeled using either parametric or semiparametric methods. The finite sample performance and theoretical results of the proposed estimator are evaluated through simulation studies and demonstrated by application to the Enhanced Suppression of the Platelet Receptor IIb/IIIa with Integrilin Therapy (ESPRIT) infusion trial data.

Robust Q-learning.

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the "Extending Treatment Effectiveness of Naltrexone" multi-stage randomized trial to illustrate our proposed methods.