A discussion of statistical methods to characterize early growth and its impact on bone mineral content later in childhood

Background Many statistical methods are available to model longitudinal growth data and relate derived summary measures to later outcomes. Aim To apply and compare commonly used methods to a realistic scenario including pre- and postnatal data, missing data and confounders. Subjects and methods Data were collected from 753 offspring in the Southampton Women’s Survey with measurements of bone mineral content (BMC) at age 6 years. Ultrasound measures included crown-rump length (11 weeks’ gestation) and femur length (19 and 34 weeks’ gestation); postnatally, infant length (birth, 6 and 12 months) and height (2 and 3 years) were measured. A residual growth model, two-stage multilevel linear spline model, joint multilevel linear spline model, SITAR and a growth mixture model were used to relate growth to 6-year BMC. Results Results from the residual growth, two-stage and joint multilevel linear spline models were most comparable: an increase in length at all ages was positively associated with BMC, the strongest association being with later growth. Both SITAR and the growth mixture model demonstrated that length was positively associated with BMC. Conclusions Similarities and differences in results from a variety of analytic strategies need to be understood in the context of each statistical methodology

Orienting the causal relationship between imprecisely measured traits using GWAS summary data - Fig 5

<p>a) Outcome <i>y</i> was simulated to be caused by exposure <i>x</i> as shown in the graph, with varying degrees of measurement error applied to both. CIT and MR were used to infer evidence for causality between the exposure and outcome, and to infer the direction of causality. The columns of graphs denote intervals for he value of <i>d</i> = <i>ρ</i>_{<i>x</i>,<i>x</i>_<i>o</i>} − <i>ρ</i>_{<i>x</i>,<i>y</i>}<i>ρ</i>_{<i>y</i>,<i>y</i>_<i>o</i>}, such that when <i>d</i> is negative we expect the MR Steiger test to be more likely to be wrong about the direction of causality. Rows of graphs represent the sample size used in the simulations. For the CIT method, outcome 1 denoted evidence for causality with correct model, outcomes 2 or 3 denoted evidence for causality with incorrect model, and outcome 4 denoted no evidence for causality. b) As in (a) except the simulated model was non-causal, and a genetic confounder induces an association between <i>x</i> and <i>y</i>. Neither CIT nor MR are able to identify this model, so any significant associations in MR are deemed to be incorrect, while outcomes 1 or 2 for the CIT are deemed to be incorrect.</p

DAG demonstrating the issue of collider bias in studies with participants selected according to disease status.

<p>In this situation, collider bias can induce an association (dashed line) between any factors (A, C, and U) that affect disease incidence (or other study selection criteria). When 1 or more of these factors are also associated with disease progression (C, U), a path is opened up from A to disease progression through the induced association. If A is a genetic risk factor, it can appear that there is an association between genetic risk factor A and disease progression only because of the induced association with C or U. If C is measured and can be adjusted for, then the induced association is blocked, but unmeasured U cannot be adjusted for in the analysis. Only when the genetic risk factor for progression is not also a risk factor for incidence (i.e., B) will it not be affected by selection bias. The arrows in Figure 2 show causal paths between variables—e.g., that variable A causes disease incidence. A collider is a variable which has 2 paths entering it, e.g., disease incidence. A path is blocked by a collider—i.e., the path from A to disease progression is blocked by disease incidence. If a collider is conditioned on, then that path is unblocked—i.e., if disease incidence is conditioned upon, then the path from A to disease progression becomes unblocked (i.e., collider bias may occur). Abbreviation: DAG, directed acyclic graph.</p

The CIT was performed on simulated variables where the exposure influenced the outcome and the exposure was instrumented by a SNP.

<p>The test statistic from CIT when testing if the exposure caused the outcome (the true model) is in red, and the test for the outcome causing the exposure (false model) is in green. Rows of plots represent the sample sizes used for the simulations. As measurement imprecision increases (decreasing values on x-axis) the test statistic for the incorrect model gets stronger and the test statistic for the correct model gets weaker.</p

Using 458 putative associations between DNA methylation and gene expression we used the MR Steiger test to infer the direction of causality between them.

<p>a) The rightmost bar shows the proportion of associations for each of the two possible causal directions (colour key) assuming no measurement error in either gene expression or DNA methylation levels. The proportions change when we assume different levels of measurement error in gene expression levels (x-axis) or DNA methylation levels (columns of boxes). If there is systematically higher measurement error in one platform than the other it will appear to be less likely to be the causal factor. b) The relationship between the Pearson correlation between DNA methylation and gene expression levels (x-axis) and the causal estimate (scaled to be in standard deviation units, y-axis). c) Distribution of estimated causal effect sizes, stratified into associations inferred to be due to DNA methylation causing expression (blue) and expression causing DNA methylation (red).</p

DAG to demonstrate how the introduction of collider bias through the selection of cases (grey paths) can impact an MR analysis between an exposure and disease progression as an outcome.

<p>Associations are induced because SNP causes disease (via exposure), and thus conditioning on disease induces an association between all variables causing disease. In a model not adjusting for exposure (e.g., relating SNP to progression), there is an association between SNP and the confounders, which biases the SNP-progression association. Abbreviations: DAG, direct acyclic graph; MR, Mendelian randomization.</p

Estimated effects of the risk factor for incidence only (A) and the risk factor for incidence and progression (C) from Fig 2 under different degrees of unmeasured confounding of incidence and progression.

<p>Estimated effects of the risk factor for incidence only (A) and the risk factor for incidence and progression (C) from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006944#pgen.1006944.g002" target="_blank">Fig 2</a> under different degrees of unmeasured confounding of incidence and progression.</p

Data_Sheet_1_Regression discontinuity design for the study of health effects of exposures acting early in life.pdf

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables’ cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.</p

Sedentary time in relation to cardio-metabolic risk factors: differential associations for self-report vs accelerometry in working age adults

Background Sedentary behaviour has been proposed to be detrimentally associated with cardio-metabolic risk independently of moderate to vigorous physical activity (MVPA). However, it is unclear how the choice of sedentary time (ST) indicator may influence such associations. The main objectives of this study were to examine the associations between ST and a set of cardio-metabolic risk factors [waist, body mass index (BMI), systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol, glycated haemoglobin] and whether these associations differ depending upon whether ST is assessed by self-report or objectively by accelerometry. Methods Multiple linear regression was used to examine the above objectives in a cross-sectional study of 5948 adults (2669 men) aged 16–65 years with self-reported measures of television time, other recreational sitting and occupational sitting or standing. In all, 1150 (521 men) participants had objective (accelerometry) data on ST as well. Results Total self-reported ST showed multivariable-adjusted (including for MVPA) associations with BMI [(unstandardized beta coefficients corresponding to the mean difference per 10 min/day greater ST: 0.035 kg/m2; 95% CI: 0.027–0.044), waist circumference (0.083 cm; 0.062–0.105), systolic (0.024 mmHg; 0.000–0.049) and diastolic blood pressure (0.023 mmHg; 0.006–0.040) and total cholesterol (0.004 mmol/l; 0.001–0.006)]. Similar associations were observed for TV time, whereas non-TV self-reported ST showed consistent associations with the two adiposity proxies (BMI/waist circumference) and total cholesterol. Accelerometry-assessed ST was only associated with total cholesterol (0.010 mmol/l; 0.001–0.018). Conclusions In this study, ST was associated consistently with cardio-metabolic risk only when it was measured by self-report

Adjusting for collider bias in genetic association studies using instrumental variable methods

Genome-wide association studies have provided many genetic markers that can be used as instrumental variables to adjust for confounding in epidemiological studies. Recently, the principle has been applied to other forms of bias in observational studies, especially collider bias that arises when conditioning or stratifying on a variable that is associated with the outcome of interest. An important case is in studies of disease progression and survival. Here, we clarify the links between the genetic instrumental variable methods proposed for this problem and the established methods of Mendelian randomisation developed to account for confounding. We highlight the critical importance of weak instrument bias in this context and describe a corrected weighted least-squares procedure as a simple approach to reduce this bias. We illustrate the range of available methods on two data examples. The first, waist-hip ratio adjusted for body-mass index, entails statistical adjustment for a quantitative trait. The second, smoking cessation, is a stratified analysis conditional on having initiated smoking. In both cases, we find little effect of collider bias on the primary association results, but this may propagate into more substantial effects on further analyses such as polygenic risk scoring and Mendelian randomisation