Aggregate-level Data Research Articles

Return of Illiberalism: The Results of the 2023 General Election in Slovakia

After a general overview of the context of the 2023 general election in Slova- kia, the article provides a detailed analysis of the electoral results and support for political parties. Using both aggregate and individual level data, we show that the results were influenced by a combination of long-term features defin- ing electoral support and voting behaviour in Slovakia and a more recent trend including weaker ideological attachment and the increased importance of leadership personality for political party preference. Also, it seems that the illib- eral mobilization may be traced in the dynamic of electoral shifts in the context of different behaviour of various societal groups. Moreover, the return of illib- eral actors to power in Slovakia seems to have been characterized by a surge of votes for conservative or populist left-wing forces in the larger Slovak cities. Although this is not reflected in the turnout, at the individual level we can as- sume that liberal voters turned out in smaller numbers, while Smer voters may have been more mobilized, or some voters may have switched from other po- pulist parties such as OĽaNO

Vote choice in the context of lethal police violence: A research note

Do police-caused deaths predict presidential vote choice in the US? This study explores the relationship between lethal police violence and vote choice in the 2020 presidential election using individual-level data from Harvard University's Cooperative Election Study (CES), which has been linked with aggregate-level data on police killings, civilian homicide rates, and socioeconomic, political, and public health characteristics. Consistent with the issue ownership theory of voting, this study finds that voters living in areas characterized by lethal police violence are significantly more likely to vote for the Democratic Party above and beyond their individual-level attributes and other contextual conditions. These findings and supplemental analyses raise critical questions concerning the political salience of fatal force in presidential races in the post-2020 era. Findings also support the need for further criminological inquiry into the effects of different forms of violence on American political behavior.

Matching-adjusted indirect comparisons of zanubrutinib (MAGNOLIA, BGB-3111-AU-003) versus ibrutinib (PCYC-1121) and rituximab (CHRONOS-3) in relapsed/refractory marginal zone lymphoma.

In the absence of head-to-head randomized trials, unanchored matching-adjusted indirect comparisons were conducted to estimate the relative efficacy of zanubrutinib versus ibrutinib and zanubrutinib versus rituximab in relapsed or refractory marginal zone lymphoma (MZL). Logistic propensity score models were used to estimate weights for the patient-level data from two phase II single-arm trials, MAGNOLIA and BGB-3111-AU-003, such that their characteristics matched the ibrutinib and rituximab aggregate-level data from PCYC-1121 and CHRONOS-3, respectively. The base case model for each comparison incorporated four key prognostic factors: prior lines of therapy, MZL subtype, response to prior therapy, and age. A sensitivity analysis incorporating additional prognostic factors was also conducted for the ibrutinib comparison. The impact of each covariate was explored via a leave-one-out analysis. Compared with ibrutinib and rituximab, zanubrutinib demonstrated significant benefits in terms of both overall response and progression-free survival in patients with previously treated MZL.

Beyond GARCH: Intraday Insights Into the Exchange Rate and Stock PriceVolatility Dynamics in Borsa Istanbul Sectors

This study investigated the impact of exchange rate volatility on sectoral stockvolatility by employing the intraday volatility measure directly calculated fromthe original data, using daily data from 27 Borsa Istanbul sectors between April 29, 2003, and April 25, 2023. In the literature, GARCH models are commonly usedtostudy the volatility spillovers between exchange rates and stock prices, typicallyusing aggregate data. However, the GARCH family models provide inefficient andbiased estimates if they are misspecified. Moreover, using aggregate-level data maylead to biased and misleading conclusions. The research used intraday volatilitymeasures to overcome the shortcomings of GARCH models. The ordinary least squares (OLS), GARCH (1,1) methods, and Garman and Klass (1980) volatilityestimator are used. The empirical results showed that the estimates fromeachmethod vary significantly, and these disparities in the results might be due tomisspecification in GARCH (1,1) models. The intraday volatility model estimationresults showed that although stock price volatilities in all sectors are positively andsignificantly affected by exchange rate volatility, their magnitudes varysignificantly. Taken together, this implies the presence of vast heterogeneities inthe responses of sectoral stock price volatilities to exchange rate volatility. The results encourage policymakers to pay special attention to these heterogeneities toprevent capital flights and underinvestment. Additionally, the findings assist investors in making more effective decisions by helping themadapt their investment strategies to factor in exchange rate fluctuations and mitigate the impact of unexpected events in the exchange rate market

Flexibility Stigma Across Europe: How National Contexts can Shift the Extent to which Flexible Workers are Stigmatised

Although flexible working has expanded rapidly, especially during the pandemic, biased views against flexible workers – namely, flexibility stigma - are still prevalent and returning. Flexibility stigma hinders worker’s take up of flexible working arrangements and can make flexible working arrangements result in negative outcomes for worker’s well-being and productivity. This study examines how national cultural and policy contexts shape flexibility stigma levels within a country. We use a multilevel approach using the Eurobarometer dataset of 2018, covering 28 European countries, matched with national level aggregate data on policy and culture. Results show that in countries with a more work-life balance work culture and egalitarian gender norms, we see less prevalence of flexibility stigma. Similarly, in countries with generous family-friendly policies, workers are less likely to have negative perception towards flexible working. Finally, stronger bargaining positions of workers, may it be through stronger union power or through better labour market conditions, helps remove stigmatised views around workers who use flexible working arrangements. This study evidences the importance of contexts that shape views around flexible working, to help us better understand policy changes needed to ensure better flexible working practices.

Comparative efficacy of ciltacabtagene autoleucel versus idecabtagene vicleucel in the treatment of patients with relapsed or refractory multiple myeloma previously treated with 2–4 prior lines of therapy: a matching-adjusted indirect comparison

Objective To estimate the comparative efficacy of ciltacabtagene autoleucel (cilta-cel) versus idecabtagene vicleucel (ide-cel) in patients with relapsed/refractory multiple myeloma (RRMM) treated with 2–4 prior lines of therapy. Methods Matching adjusted indirect comparison (MAICs) were performed using individual patient-level data (IPD) for cilta-cel from CARTITUDE-1 and CARTITUDE-4 and published aggregated data for ide-cel from KarMMa-3. Cilta-cel patients who met inclusion criteria from KarMMa-3 were selected, and outcomes were compared against data for ide-cel using simulated IPD derived from aggregate-level data from KarMMa-3. Patient characteristics were adjusted by reweighting cilta-cel IPD to match the distribution of prognostic factors in KarMMa-3. Comparative efficacy was estimated for response outcomes using a weighted logistic regression analysis and for progression-free survival using a weighted Cox proportional hazards model. Results Patients treated with cilta-cel were 1.2 times more likely to achieve overall response (relative response ratio [RR]: 1.18 [95% confidence interval: 1.03–1.34]; p = 0.04), 1.3 times more likely to achieve very good partial response or better (RR: 1.34 [1.15–1.57]; p = 0.003), and 1.9 times more likely to achieve complete response or better (RR: 1.91 [1.54–2.37]; p < 0.0001) versus ide-cel patients from KarMMa-3. Cilta-cel was associated with a significant 49% reduction in risk of disease progression or death versus ide-cel (hazard ratio: 0.51 [95% confidence interval: 0.31, 0.84]; p = 0.0078). Conclusion For patients with triple-class exposed RRMM treated with 2–4 prior lines of treatment, cilta-cel was found to provide superior clinical benefit over ide-cel in terms of response and progression-free survival.

PT21 A Generalized Framework for Eliciting Unreported Subgroup-Specific Events and Survival Outcomes from Aggregate Level Data: Analyses & Insights from Adjuvant & Advanced Stage Gastrointestinal Cancers

PT21 A Generalized Framework for Eliciting Unreported Subgroup-Specific Events and Survival Outcomes from Aggregate Level Data: Analyses & Insights from Adjuvant & Advanced Stage Gastrointestinal Cancers

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model.

Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

Who Gets Blamed When Congress and the President Collide?

Previous research suggests that vetoes can be politically costly for the president. To date, however, these inferences have been made based on either descriptive or aggregate-level data. While prior work demonstrates a correlation between vetoes and evaluations of the president, they have not explored how the public evaluates or responds to veto politics. We seek to address this gap through an original survey experiment that assesses how the public responds to the heightened interbranch conflict that surrounds a veto. Our results indicate that although a veto can cast the president in a negative light for some, there are conditions under which Congress’ decision to pass legislation it knows the president would veto can backfire. Citizens who are informed that Congress provoked a veto and those who are more politically knowledgeable are more likely to blame Congress for the legislation’s failure, even after controlling for partisanship and approval.

Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re-invigorated?

Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non-linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation-based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation-based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

Meta-Analysis and Metaregression of the Treatment Effect of Intravenous Iron in Iron-Deficient Heart Failure

BackgroundGuidelines recommend that intravenous iron should be considered to improve symptoms of heart failure (HF) and reduce the risk for HF admissions in patients after acute HF. ObjectivesThis study sought to analyze the effect of intravenous iron on cardiovascular (CV) death and HF admissions in a broad population of HF patients with iron deficiency and the relation with baseline transferrin saturation (TSAT). MethodsA systematic review of all published randomized controlled trials assessing the effect of intravenous iron in patients with iron deficiency and HF between January 1, 2000, and August 26, 2023, was performed. The overall treatment effect was estimated using a fixed effect model for: 1) CV death; 2) CV death and HF admission; 3) first HF admission; and 4) total HF admissions. Metaregression through a mixed effect model was used to explore the impact of baseline TSAT in case of heterogeneity among trial results. ResultsA total of 14 randomized controlled trials were identified in the systematic review and retained in the meta-analysis. Aggregate-level data were included on 6,624 HF patients, 3,407 of whom were randomized to intravenous iron and 3,217 to placebo. Treatment with intravenous iron resulted in a lower risk for CV death (OR: 0.867 [95% CI: 0.755-0.955]; P = 0.0427), combined CV death and HF admission (OR: 0.838 [95% CI: 0.751-0.936]; P = 0.0015), first HF admission (OR: 0.855 [95% CI: 0.744-0.983]; P = 0.0281), and total HF admissions (rate ratio: 0.739 [95% CI: 0.661-0.827]; P < 0.0001). Significant heterogeneity among trial results was observed for first and total HF admissions. Metaregression suggested that some of the heterogeneity was related to the baseline TSAT of the enrolled population, with trials enrolling patients with lower TSAT exhibiting a large effect size on HF-related events. ConclusionsThe totality of data suggests that treatment with intravenous iron reduces both CV death and HF-related events in a broad population with HF. A lower baseline TSAT might be important for the effect on HF-related events.

Abstract B073: Leveraging genomic diversity identifies additional germline risk loci in SMAD6 for Langerhans cell histiocytosis

Abstract Background: Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasia characterized by lesions including pathogenic CD207+ dendritic cells among an inflammatory infiltrate with recurrent, mutually exclusive somatic activating mutations in MAPK pathway genes in ~85% of LCH lesions; however, germline risk factors remain largely unknown. A SEER analysis reported that incidence of high-risk LCH was higher in Hispanic populations than non-Hispanic populations, and lowest in non-Hispanic Black populations. Our group reported that Hispanic mothers/couples were more likely to have children with LCH compared to non-Hispanic white mothers/couples. In the first GWAS of LCH, our group identified and validated a risk variant in SMAD6 associated with a 3-fold increase in risk. Our current objective is to leverage the genomic diversity of our patient population to identify additional SMAD6 LCH risk loci. Methods: Cases diagnosed with LCH were recruited at Texas Children’s Hospital (n=466). Targeted sequencing of SMAD6 was conducted among cases with ~200x coverage at the Avera Institute of Human Genetics. We accessed aggregate-level SMAD6 sequence data in SMAD6 from a median of 15,694 non-cancer controls in gnomAD v2.1.1. Fisher’s exact test was applied to 154 SMAD6 variants (Bonferroni critical P-value of 3.2 x 10-4) to assess association between SMAD6 variation and case status. Analyses were conducted with SAS and population genetics were assessed in Ensembl. Results: In this case-control analysis, 49 variants were significantly associated with LCH risk, including our previous risk locus. Leveraging population genetics, we found that the top 4 SNPs have low MAF globally (range 2-9%) but are enriched (MAF range 15-23%) in Hispanic/Latinx populations (MXL, CLM, PEL). The top two hits are in high LD in some of the Hispanic/Latinx and Black (ASW) populations (r2: MXL= 0.71, ASW = 1.0, PEL = 0.92), but not globally. The third SNP is in high LD with the peak SNP in these populations, as well (r2: MXL= 0.28, ASW = 0.83, PEL = 0.51). The fourth hit, which is our previously identified risk locus, has moderate LD with the peak SNP in Hispanic/Latinx populations (r2: MXL= 0.31, CLM = 0.3, PEL = 0.13). Conversely, the LD between the 1st and 5th SNPs was low globally and within different populations. Conclusion: We identified additional support for the role of SMAD6 variation in LCH susceptibility across and within diverse ancestral populations. These findings suggest that the top four SMAD6 variants identified in this study vary in how they function as independent signals across populations, especially in Hispanic/Latinx patients. The 5th hit in SMAD6 appears to be the next independent variant across all populations. These results indicate that there are common risk variants across populations and ancestry-specific variants for LCH, which may contribute to the differences in ancestry-specific LCH risk. Broadly, our work emphasizes the benefits of leveraging in, rather than adjusting out, the genomic richness of diverse patient populations. Citation Format: Priya B. Shetty, Erin Peckham-Gregory, Erik Stricker, Howard Lin, Brooks Scull, Erik Ehli, Christel Davis, Kenneth McClain, Philip Lupo, Carl Allen, Michael Scheurer. Leveraging genomic diversity identifies additional germline risk loci in SMAD6 for Langerhans cell histiocytosis [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B073.

Statistical Fallacies in Claims about ‘Massive and Widespread Fraud’ in the 2020 Presidential Election: Examining Claims Based on Aggregate Election Results 1,2

ABSTRACT Years after the election, a substantial portion of the electorate, including a significant majority of Republican voters and numerous Republican officials, continue to believe that the 2020 election was stolen. This essay reviews claims of alleged massive electoral fraud in the 2020 U.S. presidential election. These claims are based on analyses of aggregate-level election data, which are universally accepted as accurate. Although the underlying data in each of the thirteen claims we review are accurately described, our review reveals that the interpretations of the election data, which suggest massive fraud, are based on invalid statistical or illogical reasoning. In summary, the conclusions about fraud derived from these statistical analyses are categorically incorrect. We believe this article will serve as a valuable educational tool for the press, the public, and students. It underscores the dangers of misusing statistical inference and emphasizes the importance of accurate statistical analysis in political discourse. By discussing statistical fallacies in a non-technical manner, we aim to make our critiques accessible to a broad, non-specialist audience. This significantly contributes to the understanding of misinformation and its impact on democracy and public trust in electoral processes.

Systematic Literature Review and Meta-Analysis of Comparative Clinical Evidence Investigating Daratumumab, Lenalidomide, and Dexamethasone (DRd) Versus Bortezomib, Lenalidomide, and Dexamethasone (VRd) As First-Line Treatment for Transplant-Ineligible Newly Diagnosed Multiple Myeloma

Background: Daratumumab in combination with lenalidomide and dexamethasone (DRd) and bortezomib in combination with lenalidomide and dexamethasone (VRd) are currently the only guideline-recommended preferred regimens for primary treatment of transplant-ineligible (TIE) patients with newly diagnosed multiple myeloma (NDMM). The lack of direct head-to-head trials comparing these two preferred regimens makes it difficult for the treating physicians to select optimal frontline treatment for TIE NDMM patients. A number of network meta-analyses comparing the existing therapies in this patient population have been published; however, these studies have certain limitations such as not sufficiently accounting for heterogeneity among trials (MAIA trial included TIE patients only while SWOG S0777 had a mix of TIE and transplant not intended patients), variable duration of follow-up, inclusion of treatments not used in routine clinical practice in the US, and use of aggregate level data from the pivotal trials. Therefore, the current systematic literature review and meta-analysis (MA) aims to identify and aggregate clinical evidence specifically comparing DRd versus VRd as first-line (1L) treatment for TIE patients with NDMM across a wider evidence base, including indirect treatment comparisons and real-world evidence studies. Methods: The protocol was developed in alignment with the PRISMA for systematic review protocols (PRISMA-P) statement. The Population, Intervention, Comparator, Outcome, Study design (PICOS) framework was used to develop the search strategy and structure the reporting of the eligibility criteria. The search strategy was developed by a medical information specialist and peer reviewed. Ovid MEDLINE ®, Embase, and the Cochrane Library databases were searched from January 2019 to June 2023 along with key congresses (International Myeloma Society, American Society of Hematology, European Hematology Association, American Society of Clinical Oncology) from January 2018 to June 2023. These dates were implemented to identify recent literature and to align with the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) approvals of DRd for TIE NDMM. Study eligibility was assessed according to pre-specified criteria. Randomized clinical trials (RCTs) and adequately adjusted, non-randomized comparative studies comparing DRd versus VRd for treatment of adult TIE patients with NDMM were identified for inclusion. The primary outcome of interest was progression-free survival (PFS). Risk of bias was assessed using the National Institute for Health and Care Excellence (NICE) checklist. The feasibility of conducting a MA of the included studies was assessed. A frequentist approach was conducted for the MA considering the unit of analysis, varying treatment effects, and risk of bias. The study protocol is available on PROSPERO (CRD42023435119). Results: Of 863 records screened, 4 were included (1 publication and 3 conference abstracts), representing 3 unique studies. Two studies were indirect treatment comparisons and 1 was a retrospective chart review. In each study, TIE NDMM patients treated with DRd had a significantly lower risk of progression/death compared to those treated with VRd (Table 1). After qualitatively reviewing the evidence base and assessing between-trial clinical and methodological heterogeneity, it was considered feasible to conduct a MA of PFS including all 3 identified studies. Results from the MA (Figure 1) suggest that patients treated with DRd had a lower risk of progression/death compared to those treated with VRd (hazard ratio: 0.59, 95% confidence interval: [0.42-0.82]). Conclusions: Findings from the MA indicate that DRd is associated with a lower risk of disease progression/death compared to VRd as 1L treatment for TIE patients with NDMM. These findings could help inform the selection of optimal 1L treatment for these patients in the absence of head-to-head clinical trials.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Matching-Adjusted Indirect Comparisons of Brexucabtagene Autoleucel with Alternative Standard Therapies for Relapsed/Refractory B-Cell Acute Lymphoblastic Leukemia in Adult Patients.

Brexucabtagene autoleucel (brexu-cel), a CD19-directed chimeric antigen receptor T-cell therapy, is approved for relapsed/refractory B-cell precursor acute lymphoblastic leukemia in adults aged 18+/26+ years in the US/European Union (EU), based on efficacy results from the single-arm ZUMA-3 trial. This study aimed to estimate the relative treatment effects of brexu-cel versus inotuzumab ozogamicin (InO), blinatumomab (blina), and chemotherapies using unanchored matching-adjusted indirect comparison (MAIC) methods. Individual patient data from ZUMA-3 and published aggregate level data from two randomized controlled trials, INO-VATE (InO versus chemotherapy) and TOWER (blina versus chemotherapy), were used. Patient-level data from ZUMA-3 were weighted to match the mean of the following prognostic variables at baseline, which were pre-specified based on clinical input, for each comparator population: primary refractory disease, duration of first remission < 12months, prior stem-cell transplantation, age, performance status, salvage status, bone marrow blast, complex karyotype, and Philadelphia chromosome status. The base case analysis was conducted using the modified intention-to-treat population (i.e., received brexu-cel) from ZUMA-3. Relative treatment effects for overall survival (OS) and event-free survival (EFS) were expressed as hazard ratios (HR) and differences in restricted mean survival time (RMST) with 95% confidence intervals (CI). The base case MAIC results suggested brexu-cel improved OS and EFS compared to blina (OS HR 0.46 [95% CI 0.28, 0.75]; EFS HR 0.37 [95% CI 0.25, 0.56]) and pooled INO-VATE/TOWER chemotherapy (OS HR 0.32 [95% CI 0.18, 0.56]; EFS HR 0.27 [0.18, 0.40]). Brexu-cel also improved OS compared to InO (HR 0.45 [95% CI 0.24, 0.85]). The point estimate for EFS favored brexu-cel over Ino but the difference was not statistically significant (HR 0.67 [95% CI 0.41, 1.10]). Findings were consistent between the HR and RMST analyses. Despite limitations, these MAIC results suggest that brexu-cel may improve OS and EFS versus currently used therapies in this population.

SAT443 Prevalence Of Immune Checkpoint Inhibitor-related Endocrinopathies In Patients With Cancers

Abstract Disclosure: S. Pittampalli: None. S.R. Lubin: None. R. Acharya: None. Background: Immune checkpoint inhibitors (ICIs), emerged as the primary treatment modality for a wide range of cancers improving overall survival. Nevertheless, improved overall survival is complicated by endocrine dysfunctions including thyroid dysfunction, adrenalitis, diabetes mellitus, and hypophysitis. Such adverse events offset the benefits of ICI therapy and are associated with significant morbidity and mortality. Limited data exists concerning the burden of the ICI-related endocrinopathies in cancer treatment.Objective: To identify the prevalence and timing of onset of various endocrinopathies in patients with cancers receiving ICIs. Methods: A retrospective study was conducted at SUNY Upstate Medical University, NY comprising of adult patients. The study population consisted of all patients age 18 and above seen at Upstate Medical University between 1 January 2019 and 31 December 2021. Anonymized aggregate-level data was collected using an EPIC Slicer Dicer to identify all adult patients with cancer who underwent ICI therapy and a total of 199 patients were included. Demographic, clinical, histopathological, laboratory data were examined. The study was reviewed by IRB and deemed exempt. The following ICI-related endocrinopathies were considered: Hypothyroidism, hyperthyroidism, diabetes mellitus, adrenal insuffiency, and hypophysitis. ICI treatments included: anti-PD-1 (nivolumab, pembrolizumab, cemiplimab), anti-PD-L1 (atezolizumab, avelumab, durvalumab), and combination therapy (ipilimumab and nivolumab, or any other ICI combinations). Statistical analysis including descriptive statistics, ANOVA was conducted using IBM SPSS software.Results: A total of fifty patients (25%) developed ICI-related endocrinopathy (n=28 M, 22 F) with a mean age of onset 63.69 years. Majority of the patients were being treated for lung cancers (51%) and had hypothyroidism (88%) followed by adrenal insufficiency (9%) and diabetes (2%). There were no cases of hyperthyroidism or hypophysitis observed. The average time of onset of endocrinopathies after initiation of ICI therapy was 22 weeks (anti-PD-1=122 days, anti-PD-L1=180 days and combination therapy= 252 days) with a significantly later onset with combined therapy when compared to anti-PD-1 inhibitors (252 vs 122 days, p= 0.004). Conclusion: With the increasing use of immune checkpoint inhibitors in cancer therapy, there is increased prevalence of endocrinopathies. Periodic hormonal assessment along with close monitoring of new symptoms results in early detection of endocrinopathies as the time of onset varies significantly with the type of ICI agent and the use of single vs combination therapy. Presentation: Saturday, June 17, 2023

The role of micro data in understanding structural transformation

ABSTRACT This paper reviews the use of micro-level data for research on structural transformation. We survey the literature on the topics of cross-country productivity gaps, within-country gaps, labor markets, land markets, and infrastructure, and summarize how the use of micro-level data enhances our understanding of structural transformation that is otherwise hard to achieve with aggregate-level data. We suggest several areas that may benefit from more use of micro-level data. Our recommendations on data effort include collecting more panel data over longer years, especially from developing countries, complementing current time use surveys with data from developing countries, and improving the measurement of non-agricultural output. Relatedly, better measurement of physical and business capital is desired. Lastly, we note the rising trend of joining experimental data with structural models and encourage more studies to take advantage of exploiting the strength of both approaches.

Crimes committed by recent immigrants: Characteristics and community patterns

The notion persists that recent immigrants commit substantively more serious crimes than citizens, but prior research has only used aggregate-level data or youth-focused samples. We address this gap using individual-level 2017 crime arrest data from the Houston Police Department (HPD) that include citizenship, supplemented by Houston Super Neighborhoods data and American Community Survey estimates. We conduct bivariate and multilevel multinomial analyses to compare crime characteristics and neighborhood-level influences on offending by citizenship, finding that non-U.S. citizen arrestees were less likely to have been arrested for felonies, drug crimes, and Part I property crimes than U.S. citizen arrestees. An exploration of neighborhood contextual influences and interaction effects reveals further nuances. Directions for future research and implications for evidence-based policy are discussed.