Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states.

Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

The social transmission of empathy relies on observational reinforcement learning.

Theories of moral development propose that empathy is transmitted across individuals. However, the mechanisms through which empathy is socially transmitted remain unclear. Here, we combine computational learning models and functional MRI to investigate whether, and if so, how empathic and non-empathic responses observed in others affect the empathy of female observers. The results of three independent studies showed that watching empathic or non-empathic responses generates a learning signal that respectively increases or decreases empathy ratings of the observer. A fourth study revealed that the learning-related transmission of empathy is stronger when observing human rather than computer demonstrators. Finally, we show that the social transmission of empathy alters empathy-related responses in the anterior insula, i.e., the same region that correlated with empathy baseline ratings, as well as its functional connectivity with the temporoparietal junction. Together, our findings provide a computational and neural mechanism for the social transmission of empathy that accounts for changes in individual empathic responses in empathic and non-empathic social environments.

High-dimensional generalized median adaptive lasso with application to omics data.

Recently, there has been a growing interest in variable selection for causal inference within the context of high-dimensional data. However, when the outcome exhibits a skewed distribution, ensuring the accuracy of variable selection and causal effect estimation might be challenging. Here, we introduce the generalized median adaptive lasso (GMAL) for covariate selection to achieve an accurate estimation of causal effect even when the outcome follows skewed distributions. A distinctive feature of our proposed method is that we utilize a linear median regression model for constructing penalty weights, thereby maintaining the accuracy of variable selection and causal effect estimation even when the outcome presents extremely skewed distributions. Simulation results showed that our proposed method performs comparably to existing methods in variable selection when the outcome follows a symmetric distribution. Besides, the proposed method exhibited obvious superiority over the existing methods when the outcome follows a skewed distribution. Meanwhile, our proposed method consistently outperformed the existing methods in causal estimation, as indicated by smaller root-mean-square error. We also utilized the GMAL method on a deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and the severity of AD.

Sensitivity analysis of individual treatment effects: A robust conformal inference approach.

We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the Γ-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data are confounded. Under the marginal sensitivity model of [Z. Tan, J. Am. Stat. Assoc. 101, 1619-1637 (2006)], we characterize the shift between the distribution of the observations and that of the counterfactuals. We first develop a general method for predictive inference of test samples from a shifted distribution; we then leverage this to construct covariate-dependent prediction sets for counterfactuals. No matter the value of the shift, these prediction sets (resp. approximately) achieve marginal coverage if the propensity score is known exactly (resp. estimated). We describe a distinct procedure also attaining coverage, however, conditional on the training data. In the latter case, we prove a sharpness result showing that for certain classes of prediction problems, the prediction intervals cannot possibly be tightened. We verify the validity and performance of the methods via simulation studies and apply them to analyze real datasets.

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes.

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.

Variation in Secondary Prevention of Coronary Heart Disease: The INTERASPIRE Study.

BACKGROUND AND AIMS: INTERASPIRE is an international study of coronary heart disease (CHD) patients, designed to measure if guideline standards for secondary prevention and cardiac rehabilitation are being achieved in a timely manner. METHODS: Between 2020-2023, adults hospitalized in the preceding 6-24 months with incident or recurrent CHD were sampled in 14 countries from all 6 World Health Organization regions and invited for a standardized interview and examination. Direct age and sex standardization was used for country-level prevalence estimation. RESULTS: Overall, 4548 (21.1% female) CHD patients were interviewed a median of 1.05 (interquartile range 0.76-1.45) years after index hospitalization. Among all participants, 24% were obese (40% centrally). Only 38.5% achieved a blood pressure (BP) <130/80 mmHg and 19.2% a low-density lipoprotein cholesterol (LDL-C) of <1.4 mmol/l. Of those smoking at hospitalization, 48% persisted at interview. Of those with known diabetes, 56% achieved glycated hemoglobin (HbA1c) of <7.0%. A further 9.8% had undetected diabetes and 26.9% impaired glucose tolerance. Females were less likely to achieve targets: BP (females 37.4% males 38.6%), LDL-C (females 13.7% males 18.6%) and HbA1c in diabetes (females 47.7% males 57.5%). Overall, just 9.0% (inter-country range 3.8%-20.0%) reported attending cardiac rehabilitation and 1.0% (inter-country range 0.0%-2.4%) achieved the study definition of optimal guideline adherence. CONCLUSIONS: INTERASPIRE demonstrates inadequate and heterogeneous international implementation of guideline standards for secondary prevention in the first year after CHD hospitalization, with geographic and sex disparity. Investment aimed at reducing between-country and between-individual variability in secondary prevention will promote equity in global efforts to reduce the burden of CHD.

Alzheimer incidence and prevalence with and without asthma: A Medicare cohort study.

BACKGROUND: International data suggest that asthma, like other inflammatory diseases, might increase Alzheimer disease (AD) risk. OBJECTIVE: We sought to explore risk pathways and future mitigation strategies by comparing diagnostic claims-based AD incidence and prevalence among US patients with asthma with those without asthma. METHODS: This cohort study included a national Medicare 20% random sample (2013-2015). Adult patients with asthma with more than 12 months continuous Medicare were compared with subjects without asthma overall and as matched. Asthma was defined by 1 inpatient or 2 outpatient codes for asthma. The main outcomes were 2-year incident or prevalent AD defined by International Classification of Diseases, Ninth Revision code 331.0 or Tenth Revision code G30.0, G30.1, G30.8, or G30.9. RESULTS: Among 5,460,732 total beneficiaries, 678,730 patients were identified with baseline asthma and more often identified as Black or Hispanic, were Medicaid eligible, or resided in a highly disadvantaged neighborhood than those without asthma. Two-year incidence of AD was 1.4% with asthma versus 1.1% without asthma; prevalence was 7.8% versus 5.4% (both P ≤ .001). Per 100,000 patients over 2 years, 303 more incident AD diagnoses occurred in those with asthma, with 2,425 more prevalent cases (P < .001). Multivariable models showed that asthma had greater odds of 2-year AD incidence (adjusted odds ratio, 1.33 [95% CI, 1.29-1.36]; matched 1.2 [95% CI, 1.17-1.24]) and prevalence (adjusted odds ratio, 1.48 [95% CI, 1.47-1.50]; matched 1.25 [95% CI, 1.22-1.27]). CONCLUSIONS: Asthma was associated with 20% to 33% increased 2-year incidence and 25% to 48% increased prevalence of claims-based AD in this nationally representative US sample. Future research should investigate risk pathways of underlying comorbidities and social determinants as well as whether there are potential asthma treatments that may preserve brain health.

Trajectories of BMI before and after diagnosis of type 2 diabetes in a real-world population.

AIMS/HYPOTHESIS: Few studies have examined the clinical characteristics associated with changes in weight before and after diagnosis of type 2 diabetes. Using a large real-world cohort, we derived trajectories of BMI before and after diabetes diagnosis, and examined the clinical characteristics associated with these trajectories, including assessing the impact of pre-diagnosis weight change on post-diagnosis weight change. METHODS: We performed an observational cohort study using electronic medical records from individuals in the Scottish Care Information Diabetes Collaboration database. Two trajectories were calculated, based on observed BMI measurements between 3 years and 6 months before diagnosis and between 1 and 5 years after diagnosis. In the post-diagnosis trajectory, each BMI measurement was time-dependently adjusted for the effects of diabetes medications and HbA1c change. RESULTS: A total of 2736 individuals were included in the study. There was a pattern of pre-diagnosis weight gain, with 1944 individuals (71%) gaining weight overall, and 875 (32%) gaining more than 0.5 kg/m2 per year. This was followed by a pattern of weight loss after diagnosis, with 1722 individuals (63%) losing weight. Younger age and greater social deprivation were associated with increased weight gain before diagnosis. Pre-diagnosis weight change was unrelated to post-diagnosis weight change, but post-diagnosis weight loss was associated with older age, female sex, higher BMI, higher HbA1c and weight gain during the peri-diagnosis period. When considering the peri-diagnostic period (defined as from 6 months before to 12 months after diagnosis), we identified 986 (36%) individuals who had a high HbA1c at diagnosis but who lost weight rapidly and were most aggressively treated at 1 year; this subgroup had the best glycaemic control at 5 years. CONCLUSIONS/INTERPRETATION: Average weight increases before diagnosis and decreases after diagnosis; however, there were significant differences across the population in terms of weight changes. Younger individuals gained weight pre-diagnosis, but, in older individuals, type 2 diabetes is less associated with weight gain, consistent with other drivers for diabetes aetiology in older adults. We have identified a substantial group of individuals who have a rapid deterioration in glycaemic control, together with weight loss, around the time of diagnosis, and who subsequently stabilise, suggesting that a high HbA1c at diagnosis is not inevitably associated with a poor outcome and may be driven by reversible glucose toxicity.

Trends in mortality in people with HIV from 1999 to 2020: a multi-cohort collaboration.

BACKGROUND: Mortality among people with HIV declined with the introduction of combination antiretroviral therapy. We investigated trends over time in all-cause and cause-specific mortality in people with HIV from 1999-2020. METHODS: Data were collected from the D:A:D cohort from 1999 through January 2015 and RESPOND from October 2017 through 2020. Age-standardized all-cause and cause-specific mortality rates, classified using Coding Causes of Death in HIV (CoDe), were calculated. Poisson regression models were used to assess mortality trends over time. RESULTS: Among 55716 participants followed for a median of 6 years (IQR 3-11), 5263 participants died (crude mortality rate [MR] 13.7/1000 PYFU; 95%CI 13.4-14.1). Changing patterns of mortality were observed with AIDS as the most common cause of death between 1999- 2009 (n = 952, MR 4.2/1000 PYFU; 95%CI 4.0-4.5) and non-AIDS defining malignancy (NADM) from 2010 -2020 (n = 444, MR 2.8/1000 PYFU; 95%CI 2.5-3.1). In multivariable analysis, all-cause mortality declined over time (adjusted mortality rate ratio [aMRR] 0.97 per year; 95%CI 0.96, 0.98), mostly from 1999 through 2010 (aMRR 0.96 per year; 95%CI 0.95-0.97), and with no decline shown from 2011 through 2020 (aMRR 1·00 per year; 95%CI 0·96-1·05). Mortality due all known causes except NADM also declined over the entire follow-up period. CONCLUSION: Mortality among people with HIV in the D:A:D and/or RESPOND cohorts decreased between 1999 and 2009 and was stable over the period from 2010 through 2020. The decline in mortality rates was not fully explained by improvements in immunologic-virologic status or other risk factors.

Measures of Longitudinal Immune Dysfunction and Risk of AIDS and Non-AIDS Defining Malignancies in Antiretroviral-Treated People With Human Immunodeficiency Virus.

BACKGROUND: Human immunodeficiency virus (HIV) infection leads to chronic immune activation/inflammation that can persist in virally suppressed persons on fully active antiretroviral therapy (ART) and increase risk of malignancies. The prognostic role of low CD4:CD8 ratio and elevated CD8 cell counts on the risk of cancer remains unclear. METHODS: We investigated the association of CD4:CD8 ratio on the hazard of non-AIDS defining malignancy (NADM), AIDS-defining malignancy (ADM) and most frequent group of cancers in ART-treated people with HIV (PWH) with a CD4 and CD8 cell counts and viral load measurements at baseline. We developed Cox proportional hazard models with adjustment for known confounders of cancer risk and time-dependent cumulative and lagged exposures of CD4:CD8 ratio to account for time-evolving risk factors and avoid reverse causality. RESULTS: CD4:CD8 ratios below 0.5, compared to above 1.0, were independently associated with a 12-month time-lagged higher risk of ADM and infection-related malignancies (adjusted hazard ratio 2.61 [95% confidence interval {CI }1.10-6.19] and 2.03 [95% CI 1.24-3.33], respectively). CD4 cell counts below 350 cells/µL were associated with an increased risk of NADMs and ADMs, as did infection, smoking, and body mass index-related malignancies. CONCLUSIONS: In ART-treated PWH low CD4:CD8 ratios were associated with ADM and infection-related cancers independently from CD4 and CD8 cell counts and may alert clinicians for cancer screening and prevention of NADM.

Comparison of At-Home Versus In-Clinic Receipt of Long-Acting Injectable Cabotegravir/Rilpivirine.

BACKGROUND: The need for frequent travel to a clinic could impair access to injectable antiretroviral therapy for persons living with human immunodeficiency virus type 1 (HIV-1) infection. We hypothesized that allowing persons receiving treatment with long-acting injectable cabotegravir plus rilpivirine (LA CAB/RPV) to receive and store the medication in their own refrigerator prior to in-home administration by a healthcare provider would be as safe and effective as receiving treatment in a clinic. METHODS: Persons prescribed LA CAB/RPV in the Infectious Diseases clinic at the Medical University of South Carolina were offered enrollment in this non-randomized, observational study between August 2021 and December 2022. After in-clinic receipt of the initial LA CAB/RPV injection, participants chose to receive each subsequent injection over the following 12-months either in clinic or at home. RESULTS: The 33 enrolled participants were primarily Black (64%), male (73%), and had a median age of 46. Three participants stopped LA CAB/RPV and transitioned to oral antiretroviral therapy due to allergy (n = 1), loss of virologic suppression (n = 1), and visit adherence (n = 1) concerns. A comparable number of participants received treatment primarily in clinic (n = 18) relative to at home (n = 15). Injection site pain/soreness was common (52% of injections) but did not differ between groups. There were no differences in safety or efficacy between groups and both groups reported high treatment satisfaction. All participants were virologically suppressed and retained in care at the end of the study. CONCLUSIONS: At-home administration of LA CAB/RPV by a healthcare provider was comparably safe, effective, and associated with high participant satisfaction relative to in-clinic administration.

Strategic Lacunes Associated With Mild Cognitive Impairment in Rural Chinese Older Adults: A Population-Based Study.

BACKGROUND: Lacunes are associated with cognitive impairment. We sought to identify strategic lacune locations associated with mild cognitive impairment (MCI) and subtypes of MCI among older adults, and further to examine the role of white matter hyperintensities and perivascular spaces in the association. METHODS: This population-based cross-sectional study included 1230 dementia-free participants in the brain magnetic resonance imaging substudy (2018-2020) in MIND-China (Multimodal Interventions to Delay Dementia and Disability in Rural China). Lacunes were visually identified in frontal lobe, parieto-occipital lobe, temporal lobe, insula, basal ganglia, thalamus, cerebellum, and brainstem. MCI, amnestic MCI (aMCI), and nonamnestic MCI (naMCI) were defined following the Petersen's criteria. Data were analyzed using logistic regression models. RESULTS: Of the 1230 participants (age, ≥60 years; mean age, 69.40; SD, 4.30 years; 58.5% women), lacunes were detected in 357 people and MCI was defined in 286 individuals, including 243 with aMCI and 43 with naMCI. Lacunes in the supratentorial area, internal capsula, putamen/pallidum, and insula was significantly associated with increased odds ratio of MCI (multivariable-adjusted odds ratio ranged 1.40-3.21; P<0.05) and aMCI (multivariable-adjusted odds ratio ranged 1.46-3.36; P<0.05), whereas lacunes in the infratentorial area and brainstem were significantly associated with naMCI (multivariable-adjusted odds ratio ranged 2.68-3.46; P<0.01). Furthermore, the associations of lacunes in insula and internal capsula with MCI and aMCI, as well as the associations of lacunes in infratentorial area and brainstem with naMCI were present independent of white matter hyperintensities volume and perivascular spaces number. CONCLUSIONS: Lacunes in the internal capsula, putamen/pallidum, insula, and brainstem may represent the strategic lacunes that are independently associated with MCI, aMCI, or naMCI in Chinese older adults. REGISTRATION: URL: https://www.chictr.org.cn; Unique identifier: ChiCTR1800017758.

Proton pump inhibitors and the risk of acute kidney injury in cancer patients receiving immune checkpoint inhibitors: A Danish population-based cohort study.

Previous studies have suggested that the use of proton pump inhibitors (PPIs) more than doubles the risk of acute kidney injury (AKI) in cancer patients receiving immune checkpoint inhibitors (ICIs). However, this association may be confounded. Therefore, we conducted a register-based cohort study to examine the risk of AKI in users and nonusers of PPIs among cancer patients treated with ICIs in Denmark from 2011 through 2021 while accounting for a comprehensive range of potential confounders. PPI use was determined based on redeemed prescriptions of PPIs before ICI initiation. We identified laboratory-recorded AKI events within the first year after ICI initiation. We estimated the risks and hazard ratios (HRs) of AKI while accounting for a comprehensive range of confounders (including comorbidities and comedication) by propensity score weighting. Furthermore, we performed an additional per-protocol analysis while accounting for informative censoring by weighting. We identified 10 200 cancer patients including 2749 (27%) users, 6214 (61%) nonusers, and 1237 (12%) former users of PPIs. PPI users had an increased risk of AKI compared to nonusers (1-year risk, 24.7% vs 19.9%; HR, 1.42 [95% confidence interval (CI), 1.29-1.56]); however, this association attenuated when accounting for confounders (weighted 1-year risk, 24.2% vs 23.8%; weighted HR, 1.06 [95% CI, 0.93-1.21]). In the per-protocol analysis, the crude HR was 1.86 (95% CI, 1.63-2.12), while the weighted HR was 1.24 (95% CI, 1.03-1.49). Thus, the association between PPI use and AKI could largely be explained by confounding, suggesting that previous studies may have overestimated the association.

Oligometastatic non-small cell lung cancer: Impact of local and contemporary systemic treatment approaches on clinical outcome.

Oligometastatic (OMD) non-small cell lung cancer (NSCLC) is a distinct but heterogeneous entity. Current guidelines recommend systemic therapy and consolidation with local ablative therapy (LAT). However, evidence regarding the optimal choice of multimodal treatment approaches is lacking, in particular with respect to the integration of immunotherapy. This real-world study identified 218 patients with OMD NSCLC (2004-2023, prespecified criteria: ≤5 metastases in ≤2 organ systems) from three major German comprehensive cancer centers. Most patients had one (72.5%) or two (17.4%) metastatic lesions in a single (89.9%) organ system. Overall survival (OS) was significantly longer with a single metastatic lesion (HR 0.54, p = .003), and female gender (HR 0.4, p < .001). Median OS of the full cohort was 27.8 months, with 29% survival at 5 years. Patients who had completed LAT to all NSCLC sites, typically excluding patients with early progression, had a median OS of 34.4 months (37.7% 5-year OS rate) with a median recurrence-free survival (RFS) of 10.9 months (13.3% at 5 years). In those patients, systemic treatment as part of first-line therapy was associated with doubling of RFS (12.3 vs. 6.4 months, p < .001). Despite limited follow-up of patients receiving chemo-immunotherapy (EU approval 2018/2019), RFS was greatly improved by adding checkpoint inhibitors to chemotherapy (HR 0.44, p = .008, 2-year RFS 51.4% vs. 15.1%). In conclusion, patients with OMD NSCLC benefitted from multimodality approaches integrating systemic therapy and local ablation of all cancer sites. A substantial proportion of patients achieved extended OS, suggesting a potential for cure that can be further augmented with the addition of immunotherapy.

Exploring the impact of patient-specific clinical features on osimertinib effectiveness in a real-world cohort of patients with EGFR mutated non-small cell lung cancer.

Osimertinib is prescribed to patients with metastatic non-small cell lung cancer (NSCLC) and a sensitizing EGFR mutation. Limited data exists on the impact of patient characteristics or osimertinib exposure on effectiveness outcomes. This was a Dutch, multicenter cohort study. Eligible patients were ≥18 years, with metastatic EGFRm+ NSCLC, receiving osimertinib. Primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS) and safety. Kaplan-Meier analyses and multivariate Cox proportional hazard models were performed. In total, 294 patients were included. Primary EGFR-mutations were mainly exon 19 deletions (54%) and p.L858R point mutations (30%). Osimertinib was given in first-line (40%), second-line (46%) or beyond (14%), with median PFS 14.4 (95% CI: 9.4-19.3), 13.9 (95% CI: 11.3-16.1) and 8.7 months (95% CI: 4.6-12.7), respectively. Patients with low BMI (<20.0 kg/m2 ) had significantly shorter PFS/OS compared to all other subgroups. Patients with a high plasma trough concentration in steady state (Cmin,SS ; >271 ng/mL) had shorter PFS compared to a low Cmin,SS (<163 ng/mL; aHR 2.29; 95% CI: 1.13-4.63). A significant longer PFS was seen in females (aHR = 0.61, 95% CI: 0.45-0.82) and patients with the exon 19 deletion (aHR = 0.58, 95% CI: 0.36-0.92). A trend towards longer PFS was seen for TP53 wild-type patients, while age did not impact PFS. Patients with a primary EGFR exon 19 deletion had longer PFS, while a low BMI, male sex and a high Cmin,SS were indicative for shorter PFS and/or OS. Age was not associated with effectiveness outcomes of osimertinib.

The impact of different strategies for modeling associations between medications at low doses and health outcomes: a simulation study and practical application to postpartum opioid use.

Pharmacoepidemiological studies commonly examine the association between drug dose and adverse health outcomes. In situations where no safe dose exists, the choice of modeling strategy can lead to identification of an apparent safe low dose range in the presence of a non-linear relationship or due to the modeling strategy forcing a linear relationship through a dose of 0. We conducted a simulation study to assess the performance of several regression approaches to model the drug dose-response curve at low doses in a setting where no safe range exists, including the use of a (1) linear dose term, (2) categorical dose term, and (3) natural cubic spline terms. Additionally, we introduce and apply an expansion of prior work related to modeling dose-response curves at low and infrequently used doses in the setting of no safe dose ("spike-at-zero" and "slab-and-spline"). Furthermore, we demonstrate and empirically assess the use of these regression strategies in a practical scenario examining the association between the dose of the initial postpartum opioid prescribed after vaginal delivery and the subsequent total dose of opioids prescribed in the entire postpartum period among a cohort of opioid-naïve women with a vaginal delivery enrolled in a State Medicaid program (2007-2014).

Assessing Etiologic Heterogeneity for Multinomial Outcome with Two-Phase Outcome-Dependent Sampling Design.

Etiologic heterogeneity occurs when distinct sets of events or exposures give rise to different subtypes of disease. Inference about subtype-specific exposure effects from two-phase outcome-dependent sampling data requires adjustment for both confounding and the sampling design. Common approaches to inference for these effects do not necessarily appropriately adjust for these sources of bias, or allow for formal comparisons of effects across different subtypes. Herein, using inverse probability weighting (IPW) to fit a multinomial model is shown to yield valid inference with this sampling design for subtype-specific exposure effects and contrasts thereof. The IPW approach is compared to common regression-based methods for assessing exposure effect heterogeneity using simulations. The methods are applied to estimate subtype-specific effects of various exposures on breast cancer risk in the Carolina Breast Cancer Study.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

Navigating the landscape of liver cancer management: Study designs in clinical trials and clinical practice.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer death worldwide and its prognosis is highly heterogeneous, being related not only to tumour burden but also to the severity of underlying chronic liver disease. Moreover, advances in systemic therapies for HCC have increased the complexity of patient management. Randomised-controlled trials represent the gold standard for evidence generation across all areas of medicine and especially in the oncology field, as they allow for unbiased estimates of treatment effect without confounders. Observational studies have many problems that could reduce their internal and external validity. However, large prospective (well-conducted) observational real-world studies can detect rare adverse events or monitor the occurrence of long-term adverse events. How best to harness real world data, which refers to data generated from the routine care of patients, and real-world 'evidence', which is the evidence generated from real-world data, represents an open challenge. In this review article, we aim to provide an overview of the benefits and limitations of different study designs, particularly focusing on randomised-controlled trials and observational studies, to address important and not fully resolved questions in HCC research.