Quantitative Bias Analysis for Collaborative Science

Remote blood collection from older adults in the Brain Health Registry for plasma biomarker and genetic analysis

INTRODUCTION

Use of online registries to efficiently identify older adults with cognitive decline and Alzheimer's disease (AD) is an approach with growing evidence for feasibility and validity. Linked biomarker and registry data can facilitate AD clinical research.

METHODS

We collected blood for plasma biomarker and genetic analysis from older adult Brain Health Registry (BHR) participants, evaluated feasibility, and estimated associations between demographic variables and study participation.

RESULTS

Of 7150 participants invited to the study, 864 (12%) enrolled and 629 (73%) completed remote blood draws. Participants reported high study acceptability. Those from underrepresented ethnocultural and educational groups were less likely to participate.

DISCUSSION

This study demonstrates the challenges of remote blood collection from a large representative sample of older adults. Remote blood collection from > 600 participants within a short timeframe demonstrates the feasibility of our approach, which can be expanded for efficient collection of plasma AD biomarker and genetic data.

The confounder matrix: A tool to assess confounding bias in systematic reviews of observational studies of etiology

Systematic reviews and meta-analyses are essential for drawing conclusions regarding etiologic associations between exposures or interventions and health outcomes. Observational studies comprise a substantive source of the evidence base. One major threat to their validity is residual confounding, which may occur when component studies adjust for different sets of confounders, fail to control for important confounders, or have classification errors resulting in only partial control of measured confounders. We present the confounder matrix-an approach for defining and summarizing adequate confounding control in systematic reviews of observational studies and incorporating this assessment into meta-analyses. First, an expert group reaches consensus regarding the core confounders that should be controlled and the best available method for their measurement. Second, a matrix graphically depicts how each component study accounted for each confounder. Third, the assessment of control adequacy informs quantitative synthesis. We illustrate the approach with studies of the association between short interpregnancy intervals and preterm birth. Our findings suggest that uncontrolled confounding, notably by reproductive history and sociodemographics, resulted in exaggerated estimates. Moreover, no studies adequately controlled for all core confounders, so we suspect residual confounding is present, even among studies with better control. The confounder matrix serves as an extension of previously published methodological guidance for observational research synthesis, enabling transparent reporting of confounding control and directly informing meta-analysis so that conclusions are drawn from the best available evidence. Widespread application could raise awareness about gaps across a body of work and allow for more valid inference with respect to confounder control.

A systematic review of quantitative bias analysis applied to epidemiological research

BACKGROUND

Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19.

METHODS

We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility.

RESULTS

Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale.

CONCLUSIONS

QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.

Long-term community noise exposure in relation to dementia, cognition, and cognitive decline in older adults

INTRODUCTION

Exposure to noise might influence risk of Alzheimer's disease (AD) dementia.

METHODS

Participants of the Chicago Health and Aging Project (≥65 years) underwent triennial cognitive assessments. For the 5 years preceding each assessment, we estimated 5227 participants' residential level of noise from the community using a spatial prediction model, and estimated associations of noise level with prevalent mild cognitive impairment (MCI) and AD, cognitive performance, and rate of cognitive decline.

RESULTS

Among these participants, an increment of 10 A-weighted decibels (dBA) in noise corresponded to 36% and 29% higher odds of prevalent MCI (odds ratio [OR] = 1.36; 95% confidence interval [CI], 1.15 to 1.62) and AD (OR = 1.29, 95% CI, 1.08 to 1.55). Noise level was associated with worse global cognitive performance, principally in perceptual speed (-0.09 standard deviation per 10 dBA, 95% CI: -0.16 to -0.03), but not consistently associated with cognitive decline.

DISCUSSION

These results join emerging evidence suggesting that noise may influence late-life cognition and risk of dementia.

Effects of sex, race, ethnicity, and education on online aging research participation

INTRODUCTION

This study aimed to identify the relationship of sociodemographic variables with older adults participation in an online registry for recruitment and longitudinal assessment in cognitive aging.

METHODS

Using Brain Health Registry (BHR) data, associations between sociodemographic variables (sex, race, ethnicity, education) and registry participation outcomes (task completion, willingness to participate in future studies, referral/enrollment in other studies) were examined in adults aged 55+ (N = 35,919) using logistic regression. All models included sex, race, ethnicity, education, age, and subjective memory concern.

RESULTS

Non-white race, being Latino, and lower educational attainment were associated with decreased task completion and enrollment in additional studies. Results for sex were mixed.

DISCUSSION

The findings provide novel information about engagement in online aging-related registries, and highlight a need to develop improved engagement strategies targeting underrepresented sociodemographic groups. Increasing registry diversity will allow researchers to refer more representative populations to Alzheimer's and related dementias prevention and treatment trials.

Identification of environmental chemicals targeting miscarriage genes and pathways using the comparative toxicogenomics database

BACKGROUND

Miscarriage is a prevalent public health issue and many events occur before women are aware of their pregnancy, complicating research design. Thus, risk factors for miscarriage are critically understudied. Our goal was to identify environmental chemicals with a high number of interactions with miscarriage genes, based on known toxicogenomic responses.

METHODS

We used miscarriage (MeSH: D000022) and chemical gene lists from the Comparative Toxicogenomics Database in human, mouse, and rat. We assessed enrichment for gene ontology biological processes among the miscarriage genes. We prioritized chemicals (n = 25) found at Superfund sites or in the blood or urine pregnant women. For chemical-disease gene sets of sufficient size (n = 13 chemicals, n = 20 comparisons), chi-squared enrichment tests and proportional reporting ratios (PRR) were calculated. We cross-validated enrichment results.

RESULTS

Miscarriage was annotated with 121 genes and overrepresented in inflammatory response (q = 0.001), collagen metabolic process (q = 1 × 10-13), cell death (q = 0.02), and vasculature development (q = 0.005) pathways. The number of unique genes annotated to a chemical ranged from 2 (bromacil) to 5607 (atrazine). In humans, all chemicals tested were highly enriched for miscarriage gene overlap (all p < 0.001; parathion PRR = 7, cadmium PRR = 6.5, lead PRR = 3.9, arsenic PRR = 3.5, atrazine PRR = 2.8). In mice, highest enrichment (p < 0.001) was observed for naphthalene (PRR = 16.1), cadmium (PRR = 12.8), arsenic (PRR = 11.6), and carbon tetrachloride (PRR = 7.7). In rats, we observed highest enrichment (p < 0.001) for cadmium (PRR = 8.7), carbon tetrachloride (PRR = 8.3), and dieldrin (PRR = 5.3). Our findings were robust to 1000 permutations each of variable gene set sizes.

CONCLUSION

We observed chemical gene sets (parathion, cadmium, naphthalene, carbon tetrachloride, arsenic, lead, dieldrin, and atrazine) were highly enriched for miscarriage genes. Exposures to chemicals linked to miscarriage, and thus linked to decreased probability of live birth, may limit the inclusion of fetuses susceptible to adverse birth outcomes in epidemiology studies. Our findings have critical public health implications for successful pregnancies and the interpretation of adverse impacts of environmental chemical exposures on pregnancy.

Retention of Alzheimer Disease Research Participants

INTRODUCTION

Participant retention is important to maintaining statistical power, minimizing bias, and preventing scientific error in Alzheimer disease and related dementias research.

METHODS

We surveyed representative investigators from NIH-funded Alzheimer's Disease Research Centers (ADRC), querying their use of retention tactics across 12 strategies. We compared survey results to data from the National Alzheimer's Coordinating Center for each center. We used a generalized estimating equation with independent working covariance model and empirical standard errors to assess relationships between survey results and rates of retention, controlling for participant characteristics.

RESULTS

Twenty-five (83%) responding ADRCs employed an average 42 (SD=7) retention tactics. In a multivariable model that accounted for participant characteristics, the number of retention tactics used by a center was associated with participant retention (odds ratio=1.68, 95% confidence interval: 1.42, 1.98; P<0.001 for the middle compared with the lowest tertile survey scores; odds ratio=1.59, 95% confidence interval: 1.30, 1.94; P<0.001 for the highest compared with the lowest tertile survey scores) at the first follow-up visit. Participant characteristics such as normal cognition diagnosis, older age, higher education, and Caucasian race were also associated with higher retention.

CONCLUSIONS

Retention in clinical research is more likely to be achieved by employing a variety of tactics.

A Future for Observational Epidemiology: Clarity, Credibility, Transparency

Observational studies are ambiguous, difficult, and necessary for epidemiology. Presently, there are concerns that the evidence produced by most observational studies in epidemiology is not credible and contributes to research waste. I argue that observational epidemiology could be improved by focusing greater attention on 1) defining questions that make clear whether the inferential goal is descriptive or causal; 2) greater utilization of quantitative bias analysis and alternative research designs that aim to decrease the strength of assumptions needed to estimate causal effects; and 3) promoting, experimenting with, and perhaps institutionalizing both reproducible research standards and replication studies to evaluate the fragility of study findings in epidemiology. Greater clarity, credibility, and transparency in observational epidemiology will help to provide reliable evidence that can serve as a basis for making decisions about clinical or population-health interventions.