Introduction: While randomized controlled trials remain the reference standard for evaluating treatment efficacy, there is an increased interest in the use of external control arms (ECA), namely in oncology, using real-world data (RWD). Challenges related to measurement of real-world oncology endpoints, like progression-free survival (PFS), are one factor limiting the use and acceptance of ECAs as comparators to trial populations. Differences in how and when disease assessments occur in the real-world may introduce measurement error and limit the comparability of real-world PFS (rwPFS) to trial progression-free survival. While measurement error is a known challenge when conducting an externally-controlled trial with real-world data, there is limited literature describing key contributing factors, particularly in the context of multiple myeloma (MM).Methods: We distinguish between biases attributed to how endpoints are derived or ascertained (misclassification bias) and when outcomes are observed or assessed (surveillance bias). We further describe how misclassification of progression events (i.e., false positives, false negatives) and irregular assessment frequencies in multiple myeloma RWD can contribute to these biases, respectively. We conduct a simulation study to illustrate how these biases may behave, both individually and together.Results: We observe in simulation that certain types of measurement error may have more substantial impacts on comparability between mismeasured median PFS (mPFS) and true mPFS than others. For instance, when the observed progression events are misclassified as either false positives or false negatives, mismeasured mPFS may be biased towards earlier (mPFS bias = −6.4 months) or later times (mPFS bias = 13 months), respectively. However, when events are correctly classified but assessment frequencies are irregular, mismeasured mPFS is more similar to the true mPFS (mPFS bias = 0.67 months).Discussion: When misclassified progression events and irregular assessment times occur simultaneously, they may generate bias that is greater than the sum of their parts. Improved understanding of endpoint measurement error and how resulting biases manifest in RWD is important to the robust construction of ECAs in oncology and beyond. Simulations that quantify the impact of measurement error can help when planning for ECA studies and can contextualize results in the presence of endpoint measurement differences.

Introduction: Progression-free survival (PFS), a common endpoint used in multiple myeloma (MM) trials, is defined as the time to the earliest occurrence of disease progression or death. Disease progression in MM is determined by the International Myeloma Working Group (IMWG) Uniform Response Criteria based on imaging and biomarkers from blood, urine, and bone marrow biopsy testing. In clinical trials, IMWG-recommended assessments are protocol-based. However, in routine care, they are often performed and recorded differently based on a multitude of factors. Algorithms to ascertain progression events using routinely collected biomarker data are important to classify disease progression and align with trial-defined endpoints. The aim of this study was to construct and evaluate an optimized real-world Progression (rwP) algorithm using real-world data (RWD) and demonstrate how biomarker data are captured and used to derive endpoints in routine practice. Method: A cohort of 270 transplant ineligible newly diagnosed MM (NDMM) patients starting on lenalidomide and dexamethasone (Rd) and meeting select MAIA (NCT02252172) trial eligibility criteria were included from the nationwide Flatiron Health electronic health record-derived deidentified database from 2015 to 2023. A rwP algorithm was developed using key IMWG-recommended biomarkers, including serum protein electrophoresis (SPEP), urine protein electrophoresis (UPEP), and serum free light chains (FLCs). The initial baseline value for each biomarker was identified, if available, during either the pre-treatment period (45 days prior through 7 days after first line treatment (1L) start) or the on-treatment period (8 days after 1L start through 1L end). Available biomarkers were followed for disease assessment. If any subsequent lab value during 1L treatment was less than the initial baseline value, the subsequent “on-treatment baseline” value was adopted as the new lowest value; otherwise, the initial value was used as the “on-treatment baseline” value for assessing progression events. Three hierarchies, which describe the order of utilizing key IMWG-recommended biomarkers to define rwP events, were explored, including: i) strict hierarchy (SH) [SPEP > UPEP > FLCs], ii) partial hierarchy (PH) [SPEP = UPEP > FLCs], and iii) no hierarchy (NH) [SPEP = UPEP = FLCs]. The PH option closely resembles the IMWG criteria, with FLCs only used for rwP assessments when no measurable SPEP/UPEP are identified. The NH approach considers all available biomarkers equally and resembles how they are utilized in real-world clinical practice. The feasibility of requiring confirmatory events following the first suggestion of progression was also explored. Assays following the progression event up to the end of 1L were considered as confirmatory progression events if they met IMWG criteria. Results: We applied the rwP algorithm with each hierarchy to all eligible NDMM patients (N = 270). The SH, PH, and NH approaches respectively identified 106 (39.3%), 106 (39.3%), and 123 (45.6%) patients with progression events. Results from the SH and PH approach are consistent likely due to the rarity of UPEP usage in clinical practice. The NH approach captured more progression events because FLCs are used in the presence of measurable SPEP or UPEP, resulting in 73 progression events captured by FLCs in comparison to 32 from other approaches, potentially increasing false-positive capture due to the volatility of FLC results. Among the 106 PH-identified patients who progressed, 84 (79.2%) had IMWG-recommended tests following their progression event, and 74 (69.8%) had their progression confirmed before the end of 1L, with 35 (33.0%) receiving their confirmatory reading within 2 months after progression. These results suggest that requiring confirmatory testing as part of the rwP is feasible, though it may introduce false negatives for patients receiving clinical progression confirmation instead of getting confirmatory labs in routine clinical practice. Conclusion: We present a comprehensive assessment of important design choices for an rwP algorithm for NDMM, including a hierarchy of key assays and the inclusion of confirmatory testing. Further validation of the algorithm with health care provider assessments and the inclusion of bone marrow biopsy and imaging data is important for evaluating the performance of the algorithm in RWD.

Radiology imaging is critical to diagnose and monitor cancer. However, real-world radiographic imaging data availability and timing for patients may vary, and selecting real-world patients based on imaging availability may introduce biases, potentially impacting generalizability of results. Therefore, we assessed the representativeness of imaging-derived cohorts relative to a broader real-world oncology target population.

Generalizability methods are increasingly used to make inferences about the effect of interventions in target populations using a study sample. Most existing methods to generalize effects from sample to population rely on the assumption that subgroup-specific effects generalize directly. However, researchers may be concerned that in fact subgroup-specific effects differ between sample and population. In this brief report, we explore the generalizability of subgroup effects. First, we derive the bias in the sample average treatment effect estimator as an estimate of the population average treatment effect when subgroup effects in the sample do not directly generalize. Next, we present a Monte Carlo simulation to explore bias due to unmeasured heterogeneity of subgroup effects across sample and population. Finally, we examine the potential for bias in an illustrative data example. Understanding the generalizability of subgroup effects may lead to increased use of these methods for making externally valid inferences of treatment effects using a study sample.

227 Background: Recent studies have demonstrated a decline in cancer screening and diagnosis during the COVID-19 pandemic. This study explored trends in the diagnosis and management of eBC at a sample of cancer clinics across the US early on in the pandemic. Methods: Patients were selected from the Flatiron Health Research Database (FHRD), an electronic health record-derived de-identified database comprising approximately 280 US cancer clinics (̃800 sites of care). Eligible patients had an ICD code for breast cancer, at least two clinical encounters, and a confirmed eBC (Stage I-III) diagnosis from unstructured documents. Patients were selected into two cohorts based on diagnosis date: a) COVID-19 era cohort diagnosed between February 1, 2020 through June 30, 2020 and b) pre-COVID-19 era cohort diagnosed from February 1, 2019 through June 30, 2019. Descriptive statistics were used to assess diagnosis trends in each time frame. Initial treatment received following eBC diagnosis was categorized as surgery, radiation or systemic therapy and was compared between the two cohorts. Initial treatment modalities for each cohort were further stratified by clinical stage and biomarker subtype (HER2+, HR+/HER2-, triple negative [TN] or unknown). Results: A total of 278 and 253 patients were selected for the pre-COVID-19 era and COVID-19 era cohorts, with a median age at diagnosis of 65 and 64 years, respectively. A 35% decrease in the number of eBC diagnoses was observed in April/May 2020 compared to March 2020, yet this reduction in diagnoses was not observed during the equivalent months in the pre-COVID-19 era cohort. Compared to the pre-COVID-19 era, a greater proportion of patients diagnosed with eBC during the COVID-19 era initiated systemic therapy as their first treatment modality (16.5% vs 29.6%) including patients with HER2+ (27.5% vs. 60%), HR+/HER2- (13.5% vs. 24.9%) and TN (30.8% vs. 40.0%) disease. This trend was observed in patients with stage I (11.7% vs. 24.1%) or II (55.9% vs. 73.0%) but not in patients with stage III (81.2% vs. 77.3%) eBC. Notably, among patients with HR+/HER2- eBC who received systemic therapy as their first treatment, endocrine therapy was most commonly used in keeping with recent recommendations from professional societies due to COVID-related anticipated surgical delays. Conclusions: This study demonstrates that COVID-19 was associated with a decreased incidence of eBC which could be, at least in part, attributed to previously reported delays in routine screening and pandemic healthcare utilization. Further efforts are required to understand who was affected by these delays and the impact on cancer outcomes. Follow-up data are needed to understand if the observed trends in cancer screening and treatment persist and their impact on long-term cancer outcomes.

Many lifestyle intervention trials depend on collecting self-reported outcomes, such as dietary intake, to assess the intervention's effectiveness. Self-reported outcomes are subject to measurement error, which impacts treatment effect estimation. External validation studies measure both self-reported outcomes and accompanying biomarkers, and can be used to account for measurement error. However, in order to account for measurement error using an external validation sample, an assumption must be made that the inferences are transportable from the validation sample to the intervention trial of interest. This assumption does not always hold. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample, and we also formally investigate when bias due to poor transportability may arise. Lastly, we examine the performance of the methods using simulation, and illustrate them using PREMIER, a lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study measuring both self-reported diet and urinary biomarkers.

Transgender and gender non-conforming (GNC) individuals are known to have inferior healthcare outcomes compared to their cisgender peers. However, studying this population in EHR-derived data is challenging as gender identity indicators (e.g., ICD codes, identity status drop-downs) are not reliably populated. We sought to remedy this by developing an NLP-based approach to detect transgender and GNC patients in a real-world dataset, benchmarking model performance against the use of ICD codes.

Background: The coronavirus pandemic has necessitated a range of population-based measures to stem the spread of infection. These measures may be associated with disruptions to other health services including for gay, bisexual, and other men who have sex with men (MSM) at risk for or living with HIV. Here, we assess the relationship between stringency of COVID-19 control measures and interruptions to HIV prevention and treatment services for MSM. Setting: Data for this study were collected between April 16, 2020, and May 24, 2020, as part of a COVID-19 Disparities Survey implemented by the gay social networking app, Hornet. Pandemic control measures were quantified using the Oxford Government Response Tracker Stringency Index: each country received a score (0–100) based on the number and strictness of 9 indicators related to restrictions, closures, and travel bans. Methods: We used a multilevel mixed-effects generalized linear model with Poisson distribution to assess the association between stringency of pandemic control measures and access to HIV services. Results: A total of 10,654 MSM across 20 countries were included. Thirty-eight percent (3992/10,396) reported perceived interruptions to in-person testing, 55% (5178/9335) interruptions to HIV self-testing, 56% (5171/9173) interruptions to pre-exposure prophylaxis, and 10% (990/9542) interruptions to condom access. For every 10-point increase in stringency, there was a 3% reduction in the prevalence of perceived access to in-person testing (aPR: 0·97, 95% CI: [0·96 to 0·98]), a 6% reduction in access to self-testing (aPR: 0·94, 95% CI: [0·93 to 0·95]), and a 5% reduction in access to pre-exposure prophylaxis (aPR: 0·95, 95% CI: [0·95 to 0·97]). Among those living with HIV, 20% (218/1105) were unable to access their provider; 65% (820/1254) reported being unable to refill their treatment prescription remotely. Conclusions: More stringent responses were associated with decreased perceived access to services. These results support the need for increasing emphasis on innovative strategies in HIV-related diagnostic, prevention, and treatment services to minimize service interruptions during this and potential future waves of COVID-19 for gay men and other MSM at risk for HIV acquisition and transmission.

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to improve generalizability by combining trials and population data, and weighting the trial to resemble the population on baseline covariates. Large-scale surveys in fields such as health and education with complex survey designs are a logical source for population data; however, there is currently no best practice for incorporating survey weights when generalizing trial findings to a complex survey. We propose and investigate ways to incorporate survey weights in this context. We examine the performance of our proposed estimator through simulations in comparison to estimators that ignore the complex survey design. We then apply the methods to generalize findings from two trials-a lifestyle intervention for blood pressure reduction and a web-based intervention to treat substance use disorders-to their respective target populations using population data from complex surveys. The work highlights the importance in properly accounting for the complex survey design when generalizing trial findings to a population represented by a complex survey sample.

In their paper titled "Matching Methods for Observational Studies Derived from Large Administrative Databases," authors Ruoqi

Policymakers use results from randomized controlled trials to inform decisions about whether to implement treatments in target populations. Various methods-including inverse probability weighting, outcome modeling, and Targeted Maximum Likelihood Estimation-that use baseline data available in both the trial and target population have been proposed to generalize the trial treatment effect estimate to the target population. Often the target population is significantly larger than the trial sample, which can cause estimation challenges. We conduct simulations to compare the performance of these methods in this setting. We vary the size of the target population, the proportion of the target population selected into the trial, and the complexity of the true selection and outcome models. All methods performed poorly when the trial size was only 2% of the target population size or the target population included only 1,000 units. When the target population or the proportion of units selected into the trial was larger, some methods, such as outcome modeling using Bayesian Additive Regression Trees, performed well. We caution against generalizing using these existing approaches when the target population is much larger than the trial sample and advocate future research strives to improve methods for generalizing to large target populations.

Background Globally, the coronavirus pandemic has necessitated a range of population-based measures in order to stem the spread of infection and reduce COVID-19-related morbidity and mortality. These measures may be associated with disruptions to other health services including for gay, bisexual, and other men who have sex with men (MSM) at risk for or living with HIV. Here, we assess the relationship between stringency of COVID-19 mitigation strategies and interruptions to HIV prevention and treatment services for MSM. Methods Data for this study were collected as part of a COVID-19 Disparities Survey implemented by the gay social networking app Hornet, with data collected between April 16th, 2020 and May 24th, 2020. Data were assessed for countries where at least 50 participants completed the survey, to best evaluate country-level heterogeneity. We used a modified Poisson regression model, with clustering at the country-level, to assess the association between stringency of pandemic control measures and access to HIV services. Pandemic control measures were quantified using the Oxford Government Response Tracker Stringency Index; each country received a score (0-100) based on the number and strictness of nine indicators related to school and workplace closures and travel bans. Results A total of 10,654 MSM across 20 countries were included in these analyses. The mean age was 34.2 (standard deviation: 10.8), and 12% (1264/10540) of participants reported living with HIV. The median stringency score was 82.31 (Range:[19.44, Belarus]-[92.59, Ukraine]). For every ten-point increase in stringency, there was a 3% reduction in the prevalence of access to in-person testing (aPR: 0.97, 95% CI:[0.96, 0.98]), a 6% reduction in the prevalence of access to self-testing (aPR: 0.94, 95% CI:[0.93, 0.95]), and a 5% reduction in access to PrEP (aPR: 0.95, 95% CI:[0.95, 0.97]). Among those living with HIV, close to one in five (n=218/1105) participants reported being unable to access their provider either in-person or via telemedicine during the COVID-19 pandemic, with a greater proportion of interruptions to treatment services reported in Belarus and Mexico. Almost half (n=820/1254) reported being unable to refill their HIV medicine prescription remotely. Conclusions More stringent government responses were associated with decreased access to HIV diagnostic, prevention, and treatment services. To minimize increases in HIV-related morbidity and mortality, innovative strategies are needed to facilitate minimize service interruptions to MSM communities during this and potential future waves of COVID-19.

There is an urgent need to measure the impacts of COVID-19 among gay men and other men who have sex with men (MSM). We conducted a cross-sectional survey with a global sample of gay men and other MSM (n = 2732) from April 16, 2020 to May 4, 2020, through a social networking app. We characterized the economic, mental health, HIV prevention and HIV treatment impacts of COVID-19 and the COVID-19 response, and examined whether sub-groups of our study population are disproportionately impacted by COVID-19. Many gay men and other MSM not only reported economic and mental health consequences, but also interruptions to HIV prevention and testing, and HIV care and treatment services. These consequences were significantly greater among people living with HIV, racial/ethnic minorities, immigrants, sex workers, and socio-economically disadvantaged groups. These findings highlight the urgent need to mitigate the negative impacts of COVID-19 among gay men and other MSM.

ABSTRACTBackgroundGlobally, the coronavirus pandemic has necessitated a range of population-based measures in order to stem the spread of infection and reduce COVID-19-related morbidity and mortality. These measures may be associated with disruptions to other health services including for gay, bisexual, and other men who have sex with men (MSM) at risk for or living with HIV. Here, we assess the relationship between stringency of COVID-19 mitigation strategies and interruptions to HIV prevention and treatment services for MSM.MethodsData for this study were collected as part of a COVID-19 Disparities Survey implemented by the gay social networking app Hornet, with data collected between April 16th, 2020 and May 24th, 2020. Data were assessed for countries where at least 50 participants completed the survey, to best evaluate country-level heterogeneity. We used a modified Poisson regression model, with clustering at the country-level, to assess the association between stringency of pandemic control measures and access to HIV services. Pandemic control measures were quantified using the Oxford Government Response Tracker Stringency Index; each country received a score (0-100) based on the number and strictness of nine indicators related to school and workplace closures and travel bans.ResultsA total of 10,654 MSM across 20 countries were included in these analyses. The mean age was 34.2 (standard deviation: 10.8), and 12% (1264/10540) of participants reported living with HIV. The median stringency score was 82.31 (Range:[19.44, Belarus]-[92.59, Ukraine]). For every ten-point increase in stringency, there was a 3% reduction in the prevalence of access to in-person testing (aPR: 0.97, 95% Cl:[0.96, 0.98]), a 6% reduction in the prevalence of access to self-testing (aPR: 0.94, 95% Cl:[0.93, 0.95]), and a 5% reduction in access to PrEP (aPR: 0.95, 95% Cl:[0.95, 0.97]). Among those living with HIV, close to one in five (n = 218/1105) participants reported being unable to access their provider either in-person or via telemedicine during the COVID-19 pandemic, with a greater proportion of interruptions to treatment services reported in Belarus and Mexico. Almost half (n = 820/1254) reported being unable to refill their HIV medicine prescription remotely.ConclusionsMore stringent government responses were associated with decreased access to HIV diagnostic, prevention, and treatment services. To minimize increases in HIV-related morbidity and mortality, innovative strategies are needed to facilitate minimize service interruptions to MSM communities during this and potential future waves of COVID-19.

Background Immune stimulating antibody conjugates (ISACs) covalently attach TLR7/8 immune stimulants to tumor-targeting antibodies. ISACs can be delivered systemically and act locally in the tumor microenvironment by requiring the following biological steps to elicit immune activation: 1) tumor antigen recognition, 2) Fc receptor mediated phagocytosis by myeloid antigen presenting cells (APCs), and 3) activation of endosomal TLR7 and TLR8. Here, we demonstrate that covalent attachment of our TLR7/8 agonist to tumor-targeting antibodies not only enables the resulting ISACs to be safely administered systemically in preclinical models, but also unexpectedly promotes synergy between the FcgR and TLR pathways that results in amplified anti-tumor immunity in mice and robust immune activation in human leukocytes as compared to the co-administration of the components. Methods ISAC activity and mechanistic studies were analyzed via flow cytometry, ELISA and CyTOF following in vitro coculture of human leukocytes with tumor cell lines. In vivo efficacy of HER2-targeting ISACs following systemic administration was assessed in a trastuzumab-resistant HER2+ human tumor xenograft model. Safety and tolerability were assessed in tumor-bearing mice and healthy non-human primates (NHP). Results While co-administration of intratumoral TLR7/8 agonist and intraperitoneal trastuzumab failed to control tumor growth, systemic administration of the same TLR7/8 agonist and trastuzumab in our ISAC format was efficacious and induced complete tumor regression in an Fc- and TLR-dependent manner. Analysis of primary human leukocytes stimulated with ISACs in tumor co-culture assays indicated that ISACs elicit amplified and sustained phosphorylation of Fc and TLR signaling pathways, such as pERK1/2 and pIRF-7, as compared to the unconjugated mixture of the same TLR7/8 agonist and tumor targeted antibody. ISAC stimulation was largely restricted to antigen presenting cells such as dendritic cells and plasmacytoid dendritic cells that express the relevant Fc receptors and TLR7 and/or TLR8. Modifications to the ISAC that reduce FcgR engagement (N297A/Q) or render the agonist inactive halted ISAC-mediated activation and in vivo anti-tumor efficacy. Lastly, our HER2-targeting ISACs were well-tolerated when delivered systemically in mice and NHPs. Conclusions Our ISACs enable potent TLR agonists to be safely administered systemically in preclinical models. ISACs provide distinct and unexpected advantages over unconjugated TLR agonists, notably by driving synergy between FcgR and TLR pathways, leading to robust myeloid activation and anti-tumor efficacy. These data support the evaluation of BDC-1001, a HER2-targeted ISAC in the ongoing Phase 1/2 trial (NCT04278144).

“Target bias” is the difference between an estimate of association from a study sample and the causal effect in the target population of interest. It is the sum of internal and external bias. Given the extensive literature on internal validity, here, we review threats and methods to improve external validity. External bias may arise when the distribution of modifiers of the effect of treatment differs between the study sample and the target population. Methods including those based on modeling the outcome, modeling sample membership, and doubly robust methods are available, assuming data on the target population is available. The relevance of information for making policy decisions is dependent on both the actions that were studied and the sample in which they were evaluated. Combining methods for addressing internal and external validity can improve the policy relevance of study results.

Innate pattern recognition receptor agonists, including Toll-like receptors (TLRs), alter the tumor microenvironment and prime adaptive antitumor immunity. However, TLR agonists present toxicities associated with widespread immune activation after systemic administration. To design a TLR-based therapeutic suitable for systemic delivery and capable of safely eliciting tumor-targeted responses, we developed immune-stimulating antibody conjugates (ISACs) comprising a TLR7/8 dual agonist conjugated to tumor-targeting antibodies. Systemically administered human epidermal growth factor receptor 2 (HER2)-targeted ISACs were well tolerated and triggered a localized immune response in the tumor microenvironment that resulted in tumor clearance and immunological memory. Mechanistically, ISACs required tumor antigen recognition, Fcγ-receptor-dependent phagocytosis and TLR-mediated activation to drive tumor killing by myeloid cells and subsequent T-cell-mediated antitumor immunity. ISAC-mediated immunological memory was not limited to the HER2 ISAC target antigen since ISAC-treated mice were protected from rechallenge with the HER2− parental tumor. These results provide a strong rationale for the clinical development of ISACs. Alonso and colleagues develop immune-stimulating antibody conjugates capable of specific delivery of TLR7/8 agonists to tumors, which induces durable antitumor immunity.

Deficient anti-tumor immunity often results from an immunosuppressive tumor microenvironment (TME) that renders antigen presenting cells (APCs) unable to effectively stimulate T cells. Recent studies indicate that local delivery of immunostimulatory adjuvants can activate tumor resident APCs, driving uptake, processing and presentation of tumor neoantigens to T cells that mediate anti-tumor immunity. To overcome challenges associated with intratumoral delivery of such adjuvants, we developed a novel class of TLR immune-stimulating antibody conjugates (TAC) that comprise a TLR7/8 agonist conjugated to tumor-targeting monoclonal antibodies. In vitro co-cultures with human cancer cell lines and leukocytes revealed that TACs potently activate primary APCs, leading to increased co-stimulatory molecule expression (e.g. CD40, CD86) and secretion of pro-inflammatory cytokines (e.g. TNFα). The TACs also enhanced antibody-mediated effector functions such as ADCC and ADCP. Surprisingly, these constructs also induced dendritic cell (DC) differentiation from monocytes, as measured by changes in cellular morphology and DC surface markers (e.g. CD14 downregulation). CyTOF-based analysis of intracellular signaling in human leukocytes revealed a unique signaling signature of the conjugate compared to a mixture of its components, suggesting a novel biological mechanism by which the conjugate stimulates APCs. Finally, we demonstrated in vivo efficacy in syngeneic tumor models in which TAC treatment led to tumor clearance and development of immunologic memory. These results provide a strong rationale for this technology as a platform for cancer immunotherapy. Citation Format: Shelley E. Ackerman, Joseph C. Gonzalez, Josh D. Gregorio, Jason C. Paik, Felix J. Hartmann, Justin A. Kenkel, Arthur Lee, Angela Luo, Cecelia I. Pearson, Murray L. Nguyen, Benjamin Ackerman, Lauren Y. Sheu, Richard P. Laura, Steven J. Chapin, Brian S. Safina, Sean C. Bendall, David Dornan, Edgar G. Engleman, Michael N. Alonso. TLR7/8 immune-stimulating antibody conjugates elicit robust myeloid activation leading to enhanced effector function and anti-tumor immunity in pre-clinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1559.

Many lifestyle intervention trials depend on collecting self-reported outcomes, like dietary intake, to assess the intervention's effectiveness. Self-reported outcome measures are subject to measurement error, which could impact treatment effect estimation. External validation studies measure both self-reported outcomes and an accompanying biomarker, and can therefore be used for measurement error correction. Most validation data, though, are only relevant for outcomes under control conditions. Statistical methods have been developed to use external validation data to correct for outcome measurement error under control, and then conduct sensitivity analyses around the error under treatment to obtain estimates of the corrected average treatment effect. However, an assumption underlying this approach is that the measurement error structure of the outcome is the same in both the validation sample and the intervention trial, so that the error correction is transportable to the trial. This may not always be a valid assumption to make. In this paper, we propose an approach that adjusts the validation sample to better resemble the trial sample and thus leads to more transportable measurement error corrections. We also formally investigate when bias due to poor transportability may arise. Lastly, we examine the method performance using simulation, and illustrate them using PREMIER, a multi-arm lifestyle intervention trial measuring self-reported sodium intake as an outcome, and OPEN, a validation study that measures both self-reported diet and urinary biomarkers.