Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights

Methodologies for the Emulation of Biomarker-Guided Trials Using Observational Data: A Systematic Review

Background: Target trial emulation involves the application of design principles from randomised controlled trials (RCTs) to observational data, and is particularly useful in situations where an RCT would be unfeasible. Biomarker-guided trials, which incorporate biomarkers within their design to either guide treatment and/or determine eligibility, are often unfeasible in practice due to sample size requirements or ethical concerns. Here, we undertake a systematic review of methodologies used in target trial emulations, comparing treatment effectiveness, critically appraising them, and considering their applicability to the emulation of biomarker-guided trials. Methods: A comprehensive search strategy was developed to identify studies reporting on methods for target trial emulation comparing the effectiveness of treatments using observational data, and applied to the following bibliographic databases: PubMed, Scopus, Web of Science, and Ovid MEDLINE. A narrative description of methods identified in the review was undertaken alongside a critique of their relative strengths and limitations. Results: We identified a total of 59 papers: 47 emulating a target trial ('application' studies), and 12 detailing methods to emulate a target trial ('methods' studies). A total of 25 papers were identified as emulating a biomarker-guided trial (42%). While all papers reported methods to adjust for baseline confounding, 40% of application papers did not specify methods to adjust for time-varying confounding. Conclusions: This systematic review has identified a range of methods used to control for baseline, time-varying, and residual/unmeasured confounding within target trial emulation and provides a guide for researchers interested in emulation of biomarker-guided trials.

Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2 Inhibitor Medications and Urinary Tract Infection

Treatment-confounder feedback is present in time-to-recurrent or longitudinal event analysis when time-dependent confounders are themselves influenced by previous treatments. Conventional models produce misleading statistical inference of causal effects due to conditioning on these factors on the causal pathway. Marginal structural models are often applied to quantify the causal treatment effect, estimated using longitudinal weights that mimic the randomization procedure by balancing the covariate distributions across the treatment groups. The weights are usually constructed in discrete time intervals, which is appropriate if the follow-up visits are scheduled and regular. However, in primary care, visit times can be irregular and informative, and the treatment history consists of duration and doses. This can be modeled through a continuous-time marked point process. We constructed a continuous-time marginal structural model to estimate the effect of cumulative exposure to Sodium-Glucose co-Transporters 2 Inhibitor (SGLT-2i) medications on time-to-recurrent urinary tract infection (UTI). We used a cohort of type II diabetes patients with chronic kidney disease and constructed a marked point process that characterized the recurrent flare episodes of primary care visits (i.e., point process) with marks for the multinominal dose (none, low, high) of SGLT-2i medications and recurrent episodes of UTI. We applied the stabilized and optimal treatment weights to estimate the hypothesized causal effect. Our results are concordant with earlier findings in which the recurrent episodes of UTI did not increase when patients were prescribed low dose or high dose of SGLT-2i medications.

Non-Linear Dose-Response Relationship for Metformin in Japanese Patients With Type 2 Diabetes: Analysis of Irregular Longitudinal Data by Interpretable Machine Learning Models

The dose-response relationship between metformin and change in hemoglobin A1c (HbA1c) shows a maximum at 1500-2000 mg/day in patients with type 2 diabetes (T2D) in the U.S. In Japan, there is little evidence on the HbA1c-lowering effect of high-dose metformin because the maintenance and maximum doses of metformin were raised in 2010. The aim of this study was to investigate whether there is saturation of the dose-response relationship for metformin in Japanese T2D patients. Longitudinal clinical information of T2D patients was extracted from electronic medical records. Supervised machine learning models with random effect were constructed to predict change in HbA1c: generalized linear mixed-effects models (GLMM) with/without a feature selection and combining tree-boosting with Gaussian process and mixed-effects models (GPBoost). GPBoost was interpreted by SHapley Additive exPlanations (SHAP) and partial dependence. GPBoost had better predictive performance than GLMM with/without feature selection: root mean square error was 0.602 (95%CI 0.523-0.684), 0.698 (0.629-0.774) and 0.678 (0.609-0.753), respectively. Interpretation of GPBoost by SHAP and partial dependence suggested that the relationship between the daily dose of metformin and change in HbA1c is non-linear rather than linear, and the HbA1c-lowering effect of metformin reaches a maximum at 1500 mg/day. Interpretation of GPBoost, a non-linear supervised machine-learning algorithm, suggests that there is saturation of the dose-response relationship of metformin in Japanese patients with T2D. This finding may be useful for decision-making in pharmacotherapy for T2D.

The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records

BACKGROUND

This study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses.

METHODS

In UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered.

RESULTS

T2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis ( β = 0.51 , 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD individuals.

CONCLUSIONS

The timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

Effect of a Multifaceted Approach on Perceived Support Among Mothers of Preterm Infants: A Quasi-Experimental Study

Introduction

Mothers of preterm infants need support to deal with the issues caused by the early birth of their infants.

Objective

The authors examined the impact of a multifaceted supportive approach on the mothers' perceived support levels.

Methods

The present quasi-experimental study was performed on 143 mother-preterm infant pairs, in two neonatal intensive care units (NICUs) of referral and educational hospitals in Tehran, Iran. The base of developed interventions was known to support system patterns of mothers with preterm infants. Different supportive interventions (appraisal, instrumental, emotional, and informational) of mothers were implemented during three months. The Nurse Parent Support Tool was applied for assessing perceptions of perceived support by mothers. Routine care was provided for the control group. The results were analyzed by STATA software 13. Categorical variables were analyzed by chi-square test, t-test, and inverse probability treatment weights.

Results

Following adjustments of mean differences of outcomes between study groups (95% confidence interval), all support scores, such as instrumental support, -1.23 (95% CI -1.04 to -1.43), total support, -1.83 (95% CI -1.6 to -2.06), appraisal support, -2.01 (95% CI -1.73 to -2.29), emotional support, -1.87 (95% CI -2.15 to 1.6), and informational support, -2.12 (95% CI -1.82 to -2.43), were significantly higher in the interventional group than in the control group (p < .001).

Conclusions

Support received by mothers of preterm infants determines maternal/neonatal health. Information sharing and effective ways to support are essential elements in the mother's ability to deal with the new, stressful situation. This multifaceted supportive approach considerably improved mothers' perceived support.