Inverse probability weighting is an effective method to address selection bias during the analysis of high dimensional data

Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies

Background

Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation.

Methods

A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394).

Results

Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported.

Conclusions

Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

The impact of prenatal mercury on neurobehavioral functioning longitudinally assessed from a young age to pre-adolescence in a Spanish birth cohort

The objective is to investigate the relation between cord blood mercury concentrations and child neurobehavioural functioning assessed longitudinally during childhood until pre-adolescence.

METHODS

The study involves mothers and their offspring engaged in the Spanish INMA birth cohort (n = 1147). Total mercury (THg) was determined in cord blood. Behavioural problems were assessed several times during childhood using the ADHD-DSM-IV at age 4, SDQ at ages 7 and 11, CPRS-R:S and the CBCL at ages 7, 9 and 11. Covariates were obtained through questionnaires during the whole period. Multivariate generalised negative binomial (MGNB) models or mixed-effects MGNB (for those tests with information at one or more time points, respectively) were used to investigate the relation between cord blood THg and the children's punctuations. Models were adjusted for prenatal fish intake. Effect modification by sex, prenatal and postnatal fish intake, prenatal fruit and vegetable intake, and maternal polychlorinated biphenyl concentrations (PCBs) was assessed by interaction terms.

RESULTS

The geometric mean ± standard deviation of cord blood THg was 8.22 ± 2.19 μg/L. Despite adjusting for fish consumption, our results did not show any statistically significant relationship between prenatal Hg and the children's performance on behavioural tests conducted between the ages of 4 and 11. Upon assessing the impact of various factors, we observed no statistically significant interaction.

CONCLUSION

Despite elevated prenatal THg exposure, no association was found with children's behavioural functioning assessed from early childhood to pre-adolescence. The nutrients in fish could offset the potential neurotoxic impact of Hg. Further birth cohort studies with longitudinal data are warranted.

To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice

OBJECTIVES

To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.

MATERIALS AND METHODS

We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results.

RESULTS

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

DISCUSSION

Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.

CONCLUSION

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

Effect of high-frequency repetitive transcranial magnetic stimulation on cognitive impairment in WD patients based on inverse probability weighting of propensity scores

Background

Hepatolenticular degeneration [Wilson disease (WD)] is an autosomal recessive metabolic disease characterized by copper metabolism disorder. Cognitive impairment is a key neuropsychiatric symptom of WD. At present, there is no effective treatment for WD-related cognitive impairment.

Methods

In this study, high-frequency repetitive transcranial magnetic stimulation (rTMS) was used to treat WD-related cognitive impairment, and inverse probability weighting of propensity scores was used to correct for confounding factors. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Boston Naming Test (BNT), Clock Drawing Test (CDT) and Trail Making Test (TMT) were used to evaluate overall cognition and specific cognitive domains.

Results

The MMSE, MoCA and CDT scores after treatment were significantly different from those before treatment (MMSE: before adjustment: OR = 1.404, 95% CI: 1.271-1.537; after adjustment: OR = 1.381, 95% CI: 1.265-1.497, p < 0.001; MoCA: before adjustment: OR = 1.306, 95% CI: 1.122-1.490; after adjustment: OR = 1.286, 95% CI: 1.104; AVLT: OR = 1.161, 95% CI: 1.074-1.248; after adjustment: OR = 1.145, 95% CI: 1.068-1.222, p < 0.05; CDT: OR = 1.524, 95% CI: 1.303-1.745; after adjustment: OR = 1.518, 95% CI: 1.294-1.742, p < 0.001). The BNT and TMT scores after adjustment were not significantly different from those before adjustment (BNT: before adjustment: OR = 1.048, 95% CI: 0.877-1.219; after adjustment: OR = 1.026, 95% CI: 0.863-1.189, p > 0.05; TMT: before adjustment: OR = 0.816, 95% CI: 1.122-1.490; after adjustment: OR = 0.791, 95% CI: 0.406-1.176, p > 0.05).

Conclusion

High-frequency rTMS can effectively improve cognitive impairment, especially memory and visuospatial ability, in WD patients. The incidence of side effects is low, and the safety is good.

Pancreatic Cancer Multidisciplinary Clinic is Associated with Improved Treatment and Elimination of Socioeconomic Disparities

OBJECTIVE

To identify the association between multidisciplinary clinic (MDC) management and disparities in treatment for patients with pancreatic cancer.

BACKGROUND

Socioeconomic status (SES) predicts treatment and survival for pancreatic cancer. Multidisciplinary clinics (MDCs) may improve surgical management for these patients.

METHODS

This is a retrospective cohort study (2010-2018) of all pancreatic cancer patients within a large, regional hospital system with a high-volume pancreatic cancer MDC. The primary outcome was receipt of treatment (surgery, chemotherapy, radiation, clinical trial participation, and palliative care); the secondary outcomes were overall survival and MDC management. Multiple logistic regressions were used for binary outcomes. Survival was analyzed using Kaplan-Meier survival analysis, Cox proportional hazards, and inverse probability of treatment weighting (IPTW).

RESULTS

Of the 4141 patients studied, 1420 (34.3%) were managed by the MDC. MDC management was more likely for patients who were younger age, married, and privately insured, while less likely for low SES patients (all p < 0.05). MDC patients were more likely to receive all treatments, including neoadjuvant chemotherapy (OR 3.33, 95% CI 2.82-3.93), surgery (OR 1.39, 95% CI 1.15-1.68), palliative care (OR 1.21, 95% CI 1.05-1.38), and clinical trial participation (OR 3.76, 95% CI 2.86-4.93). Low SES patients were less likely to undergo surgery outside of the MDC (OR 0.47, 95% CI 0.31-0.73) but there was no difference within the MDC (OR 1.10, 95% CI 0.68-1.77). Across multiple survival analyses, low SES predicted inferior survival outside of the MDC, but there was no association among MDC patients.

CONCLUSION

Multidisciplinary team-based care increases rates of treatment and eliminates socioeconomic disparities for pancreatic cancer patients.

To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

Objective

To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses.

Materials and methods

We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results.

Results

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

Discussion

Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals.

Conclusion

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Birthweight, gestational age, and early school trajectory

BACKGROUND

Birthweight and gestational age are important factors of not only newborn health by also child development and can contribute to delayed cognitive abilities. However, no study has analyzed the association of birthweight and gestational age with school trajectory measured simultaneously by school entry, grade repetition, and school dropout. This study aims, first, to analyze the association of birthweight or gestational age with school entry, and second, to explore the relationship between birthweight or gestational age and grade repetition and school dropout among children in Ouagadougou, Burkina Faso.

METHODS

This study used longitudinal data from the Ouagadougou Health and Demographic Surveillance System. Our samples consisted of children born between 2008 and 2014 who were at least three years old at the beginning of the 2017-18 school year. Samples included 13,676, 3152, and 3498 children for the analysis of the school entry, grade repetition, and dropout, respectively. A discrete-time survival model was used to examine the relationship between birthweight or gestational age and school entry, grade repetition, and dropout. The association between birthweight or gestational age and age at school entry were assessed using a Poisson regression.

RESULTS

The incidence rate of school entry was 18.1 per 100 people-years. The incidence of first repetition and dropout were 12.6 and 5.9, respectively. The probability of school entry decreased by 31% (HR:0.69, 95%CI: 0.56-0.85) and 8% (HR:0.92, 95%CI: 0.85-0.99) for children weighing less than 2000 g and those weighing between 2000 and 2499 g, respectively, compared to those born with a normal weight (weight ≥ 2500 g). The age at school entry of children with a birthweight less than 2000 g and between 2000 and 2499 g was 7% (IRR: 1.07, 95%CI: 1.06-1.08) and 3% (IRR: 1.03, 95%CI: 1.00-1.06) higher than children born at a normal birthweight, respectively. Gestational age was not associated with school entry or age at school entry. Similarly, birthweight and gestational age were not associated with grade repetition or dropout.

CONCLUSION

This study shows that low birthweight is negatively associated with school entry and age at school entry in Ouagadougou. Efforts to avoid low birthweights should be part of maternal and prenatal health care because the associated difficulties may be difficult to overcome later in the child's life. Further longitudinal studies are needed to better understand the relationship between development at birth and school trajectory.