The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2011.568786

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.

/pdf/marginal-structural-models-and-causal-inference-in-4stwde17mg.pdf

Marginal Structural Models and Causal Inference in Epidemiology

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

/pdf/an-atlas-of-genetic-correlations-across-human-diseases-and-11fyqwnh4c.pdf

An atlas of genetic correlations across human diseases and traits.

The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher-order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of 'best practice' when using IPTW to estimate causal treatment effects using observational data.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.6607

Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.

/pdf/a-structural-approach-to-selection-bias-3a11zn8a2a.pdf

A structural approach to selection bias.

We explored the relation between various potential sources of maternal periconceptional pregnancy exposures to pesticides and congenital anomalies in offspring. Data were derived from a case-control study of fetuses and liveborn infants with orofacial clefts, neural tube defects, conotruncal defects, or limb anomalies, among 1987-1989 California births and fetal deaths. We conducted telephone interviews with mothers of 662 (85% of eligible) orofacial cleft cases, 265 (84%) neural tube defect cases, 207 (87%) conotruncal defect cases, 165 (84%) limb cases, and 734 (78%) nonmalformed controls. The odds ratio (OR) estimates did not indicate increased risk for any of the studied anomaly groups among women whose self-reported occupational tasks were considered by an industrial hygienist likely to involve pesticide exposures. Paternal occupational exposure to pesticides, as reported by the mother, revealed elevated ORs for only two of the cleft phenotypes [OR = 1.7 [95% confidence interval (CI) = 0.9-3.4] for multiple cleft lip with/without cleft palate and OR = 1.6 [95% CI = 0.7-3.4] for multiple cleft palate]. Use of pesticide products for household gardening, by mothers or by professional applicators, was associated with ORs > or =1.5 for most of the studied anomalies. Use of pesticide products for the control of pests in or around homes was not associated with elevated risks for most of the studied anomalies, although women who reported that a professional applied pesticides to their homes had increased risks for neural tube defect-affected pregnancies [OR = 1.6 (95% CI = 1.1-2.5)] and limb anomalies [OR = 1.6 (95% CI = 1.0-2.7)]. Having a pet cat or dog and treating its fleas was not associated with increased anomaly risk. Women who reported living within 0.25 miles of an agricultural crop revealed increased risks for offspring with neural tube defects [OR = 1.5 (95%CI = 1.1-2.1)]. For many of the comparisons, data were sparse, resulting in imprecise effect estimation. Despite our investigating multiple sources of potential pesticide exposures, without more specific information on chemical and level of exposure, we could not adequately discriminate whether the observed effects are valid, whether biased exposure reporting contributed to the observed elevated risks, or whether nonspecific measurement of exposure was responsible for many of the observed estimated risks not being elevated.

Maternal pesticide exposure from multiple sources and selected congenital anomalies.

BackgroundThe occurrence of hypospadias has been reported to be increasing. The objectives of this study were to extend the literature on the descriptive epidemiology of hypospadias and to determine whether its birth prevalence increased in California in recent years. We used actively ascertained, p

Hypospadias in California: trends and descriptive epidemiology.

Schizencephaly is a rare congenital brain defect characterized by gray matter lined clefts of the cerebral mantle, frequently accompanied by other defects of the CNS such as absence of the corpus callosum. This study in a California population of >4 million births from 1985-2001 found a population prevalence of 1.54/100,000. Among 63 cases, there was an association with young parental age in isolated schizencephaly (RR 3.9 mothers; 5.8 fathers), which was also seen in mothers but not fathers of non-isolated cases (RR 3.2). Monozygotic twins may also be at increased risk for schizencephaly (RR 2.1). One third of cases had a non-CNS abnormality, over half of which could be classified as secondary to vascular disruption, including gastroschisis, bowel atresias, and amniotic band disruption sequence. Other apparent rare causes included chromosomal aneuploidy, non-random associations, and unusual syndromes. Our observations suggest that schizencephaly has heterogeneous etiologies many of which are vascular disruptive in origin.

Schizencephaly: heterogeneous etiologies in a population of 4 million California births.

Neural tube defects associated with maternal periconceptional dietary intake of simple sugars and glycemic index

Studies of socio-economic status (SES) have figured prominently in research related to a variety of health outcomes, although the question remains as to whether SES contributes to the aetiologies of congenital anomalies. This study examines the association of SES with risks of conotruncal heart defects and orofacial clefts, using interview data from 696 case mothers (86% of eligible) and 734 (78%) control mothers from a population-based case-control study. Socio-economic measures from maternal interview included mother's education and employment. Reported addresses were linked with the US census to characterise six measures of neighbourhood SES (education, poverty, unemployment, occupation, crowding and rental occupancy). Results were adjusted for race-ethnicity, multivitamin/mineral supplement intake, cigarette smoking and binge drinking. Results for individual and neighbourhood measures suggested that low SES was associated with increased risk of d-transposition of the great arteries (dTGA), reduced risk of tetralogy of Fallot (TOF), but was not associated with risk of orofacial clefts. For example, when examining odds ratios (OR) that compared risks among women whose neighbourhoods were in the lowest vs. highest quartile of the census-based SES measures, ORs for five of the six measures were> 1.4 for dTGA, and ORs for all six measures were < 0.7 for TOF. ORs for clefts tended to be closer to 1. This study suggests that SES risks are birth defect specific.

Verne Nelson

Papers

Maternal pesticide exposure from multiple sources and selected congenital anomalies.

Hypospadias in California: trends and descriptive epidemiology.

Schizencephaly: heterogeneous etiologies in a population of 4 million California births.

Neural tube defects associated with maternal periconceptional dietary intake of simple sugars and glycemic index

Socio-economic status and risk of conotruncal heart defects and orofacial clefts.