A general framework for developing computable clinical phenotype algorithms.

OBJECTIVE: Present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data. MATERIALS/METHODS: Drawing on extensive prior phenotyping experiences and insights derived from three algorithm development projects conducted specifically for this purpose, our team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and healthcare data science methods conceptualized stages of development and corresponding sets of principles, strategies, and practical guidelines for improving the algorithm development process. RESULTS: We propose five stages of algorithm development and corresponding principles, strategies, and guidelines: 1) assessing fitness-for-purpose, 2) creating gold standard data, 3) feature engineering, 4) model development, and 5) model evaluation. DISCUSSION/CONCLUSION: This framework is intended to provide practical guidance and serve as a basis for future elaboration and extension.

Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features.

In the ENSEMBLE randomized, placebo-controlled phase 3 trial (NCT04505722), estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were determined from 484 vaccine and 1,067 placebo recipients who acquired COVID-19. In this set of prespecified analyses, we show that in Latin America, VE was significantly lower against Lambda vs. Reference and against Lambda vs. non-Lambda [family-wise error rate (FWER) p < 0.05]. VE differed by residue match vs. mismatch to the vaccine-insert at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20); significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 antibody-epitope escape scores and 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccinee sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against the most distant viruses.

Flexible variable selection in the presence of missing data.

In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexible panels in the presence of missing-at-random data. We outline strategies based on the proposed algorithm that achieve control of commonly used error rates. Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified. Finally, we use the proposed method to develop biomarker panels for separating pancreatic cysts with differing malignancy potential in a setting where complicated missingness in the biomarkers arose due to limited specimen volumes.

Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features.

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations.

Engaging staff to improve COVID-19 vaccination response at long-term care facilities (ENSPIRE): A cluster randomized trial of co-designed, tailored vaccine promotion materials.

BACKGROUND: COVID-19 vaccination rates among long-term care center (LTCC) workers are among the lowest of all frontline health care workers. Current efforts to increase COVID-19 vaccine uptake generally focus on strategies that have proven effective for increasing influenza vaccine uptake among health care workers including educational and communication strategies. Experimental evidence is lacking on the comparative advantage of educational strategies to improve vaccine acceptance and uptake, especially in the context of COVID-19. Despite the lack of evidence, education and communication strategies are recommended to improve COVID-19 vaccination rates and decrease vaccine hesitancy (VH), especially strategies using tailored messaging for disproportionately affected populations. METHODS: We describe a cluster-randomized comparative effectiveness trial with 40 LTCCs and approximately 4000 LTCC workers in 2 geographically, culturally, and ethnically distinct states. We compare the effectiveness of two strategies for increasing COVID-19 booster vaccination rates and willingness to promote COVID-19 booster vaccination: co-design processes for tailoring educational messages vs. an enhanced usual care comparator. Our study focuses on the language and/or cultural groups that are most disproportionately affected by VH and low COVID-19 vaccine uptake in these LTCCs. CONCLUSION: Finding effective methods to increase COVID-19 vaccine uptake and decrease VH among LTCC staff is critical. Beyond COVID-19, better approaches are needed to improve vaccine uptake and decrease VH for a variety of existing vaccines as well as vaccines created to address novel viruses as they emerge.

Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

OBJECTIVES: Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. MATERIALS AND METHODS: PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. RESULTS: Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. DISCUSSION: Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. CONCLUSION: PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.

A general framework for inference on algorithm-agnostic variable importance.

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.

Considerations for Subgroup Analyses in Cluster-Randomized Trials Based on Aggregated Individual-Level Predictors.

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.

An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.

Background: Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals. Methods: The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence. Results: In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping. Conclusion: These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.

A causal roadmap for generating high-quality real-world evidence.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research.

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.

A health-system-embedded deprescribing intervention targeting patients and providers to prevent falls in older adults (STOP-FALLS trial): study protocol for a pragmatic cluster-randomized controlled trial.

BACKGROUND: Central nervous system (CNS) active medications have been consistently linked to falls in older people. However, few randomized trials have evaluated whether CNS-active medication reduction reduces falls and fall-related injuries. The objective of the Reducing CNS-active Medications to Prevent Falls and Injuries in Older Adults (STOP-FALLS) trial is to test the effectiveness of a health-system-embedded deprescribing intervention focused on CNS-active medications on the incidence of medically treated falls among community-dwelling older adults. METHODS: We will conduct a pragmatic, cluster-randomized, parallel-group, controlled clinical trial within Kaiser Permanente Washington to test the effectiveness of a 12-month deprescribing intervention consisting of (1) an educational brochure and self-care handouts mailed to older adults prescribed one or more CNS-active medications (aged 60 + : opioids, benzodiazepines and Z-drugs; aged 65 + : skeletal muscle relaxants, tricyclic antidepressants, and antihistamines) and (2) decision support for their primary health care providers. Outcomes are examined over 18-26 months post-intervention. The primary outcome is first incident (post-baseline) medically treated fall as determined from health plan data. Our sample size calculations ensure at least 80% power to detect a 20% reduction in the rate of medically treated falls for participants receiving care within the intervention (n = 9) versus usual care clinics (n = 9) assuming 18 months of follow-up. Secondary outcomes include medication discontinuation or dose reduction of any target medications. Safety outcomes include serious adverse drug withdrawal events, unintentional overdose, and death. We will also examine medication signetur fields for attempts to decrease medications. We will report factors affecting implementation of the intervention. DISCUSSION: The STOP-FALLS trial will provide new information about whether a health-system-embedded deprescribing intervention that targets older participants and their primary care providers reduces medically treated falls and CNS-active medication use. Insights into factors affecting implementation will inform future research and healthcare organizations that may be interested in replicating the intervention. TRIAL REGISTRATION: ClinicalTrial.gov NCT05689554. Registered on 18 January 2023, retrospectively registered.

Comparing antibody assays as correlates of protection against COVID-19 in the COVE mRNA-1273 vaccine efficacy trial.

The best assay or marker to define mRNA-1273 vaccine-induced antibodies as a correlate of protection (CoP) is unclear. In the COVE trial, participants received two doses of the mRNA-1273 COVID-19 vaccine or placebo. We previously assessed IgG binding antibodies to the spike protein (spike IgG) or receptor binding domain (RBD IgG) and pseudovirus neutralizing antibody 50 or 80% inhibitory dilution titer measured on day 29 or day 57, as correlates of risk (CoRs) and CoPs against symptomatic COVID-19 over 4 months after dose. Here, we assessed a new marker, live virus 50% microneutralization titer (LV-MN50), and compared and combined markers in multivariable analyses. LV-MN50 was an inverse CoR, with a hazard ratio of 0.39 (95% confidence interval, 0.19 to 0.83) at day 29 and 0.51 (95% confidence interval, 0.25 to 1.04) at day 57 per 10-fold increase. In multivariable analyses, pseudovirus neutralization titers and anti-spike binding antibodies performed best as CoRs; combining antibody markers did not improve correlates. Pseudovirus neutralization titer was the strongest independent correlate in a multivariable model. Overall, these results supported pseudovirus neutralizing and binding antibody assays as CoRs and CoPs, with the live virus assay as a weaker correlate in this sample set. Day 29 markers performed as well as day 57 markers as CoPs, which could accelerate immunogenicity and immunobridging studies.

Conduction system pacing in prosthetic heart valves.

BACKGROUND: There has been increasing interest in physiologic pacing techniques that directly activate the specialized conduction system. We aimed to assess outcomes of conduction system pacing (CSP) in patients with prosthetic heart valves. METHODS: This systematic review was performed according to PRISMA guidelines. Freeman-Tukey double arcsine transformation with the random-effect model was used to summarize the data. Outcomes studied were 1) implant success (defined as ability to recruit the His-Purkinje system or the distal Purkinje system); (2) lead parameters at implant and follow-up; and (3) procedure-related complications. RESULTS: This systematic review of 7 studies included 267 unique patients in whom CSP was attempted with either HBP or LBBAP for pacing indications after a prosthetic valve. HBP was attempted in 38% (n = 108), while LBBAP in 62% (n = 175) patients. The overall success rate of CSP was 87%, while in patients post-TAVR, the overall success rate was 83.2%. In the subgroup analysis, LBBAP had a significant higher overall success rate compared to HBP (94.3% vs. 76.5%, p interaction = 0.02) and post-TAVR patients (94.3 vs. 66.9%, p interaction < 0.01), respectively. The LBBAP thresholds were significantly lower compared to HBP both at implant (0.67 ± 0.4 @ 0.44 ms vs. 1.35 ± 1 @ 0.85 ms, p interaction < 0.01) and at a mean follow-up of 12.4 ± 8 months (0.73 ± 0.1 @ 0.44 ms vs. 1.39 ± 1 @ 0.85 ms, p interaction < 0.01), respectively. CONCLUSION: CSP is safe and feasible in patients with a prosthetic valve, with a significantly higher success rate and superior lead parameters with LBBAP than HBP, especially in patients post-TAVR.

Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens.

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 infection. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory concentration < 1 µg/mL. For continuous outcomes, the CP approach performs at least as well as the PC approach, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials.

Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens.

BACKGROUND: The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders. METHODS: This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered. FINDINGS: Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status. INTERPRETATION: The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power. FUNDING: National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources.

Diaphragmatic Pacing as an Initial Presentation of Delayed Ventricular Lead Perforation.

Ventricular lead perforation is an infrequent and potentially fatal complication of pacemakers and implantable cardioverter-defibrillators that typically presents shortly following device implantation. Delayed lead perforations occurring 1 month after implantation are not widely reported and can have a wide range of presentations ranging from asymptomatic to potentially fatal cardiac tamponade. We describe a case of successful percutaneous lead extraction and revision in a patient who presented 9 months following implantation with an active fixation right ventricular pacing lead with apical perforation. Perforation was suspected when device interrogation showed ventricular sensing without ventricular capture, but with diaphragm stimulation. After an initial X-ray and transthoracic echocardiogram failed to detect it, computed tomography angiography confirmed the myocardial perforation. This case demonstrates the importance of recognizing such a complication following cardiac implantable electronic device implantation regardless of the timeline of presentation. It also serves to highlight the importance of clinical suspicion and awareness of the limitations of imaging for perforation. Transvenous percutaneous lead extraction and revision remains a favored approach due to reduced patient trauma when compared to the open surgical approach.

Combining information to estimate adherence in studies of pre-exposure prophylaxis for HIV prevention: Application to HPTN 067.

In trials of oral HIV pre-exposure prophylaxis (PrEP), multiple approaches have been used to measure adherence, including self-report, pill counts, electronic dose monitoring devices, and biological measures such as drug levels in plasma, peripheral blood mononuclear cells, hair, and/or dried blood spots. No one of these measures is ideal and each has strengths and weaknesses. However, accurate estimates of adherence to oral PrEP are important as drug efficacy is closely tied to adherence, and secondary analyses of trial data within identified adherent/non-adherent subgroups may yield important insights into real-world drug effectiveness. We develop a statistical approach to combining multiple measures of adherence and show in simulated data that the proposed method provides a more accurate measure of true adherence than self-report. We then apply the method to estimate adherence in the ADAPT study (HPTN 067) in South African women.

A multiview model for relative and absolute microbial abundances.

The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances.