FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4251039891> ?p ?o ?g. }

• W4251039891 endingPage "415" @default.
• W4251039891 startingPage "409" @default.
• W4251039891 abstract "Vol. 119, No. 3 ResearchOpen AccessStatistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants Brisa Ney Sánchez, Howard Hu, Heather J. Litman, and Martha Maria Téllez-Rojo Brisa Ney Sánchez Address correspondence to B.N. Sánchez, University of Michigan School of Public Health, Department of Biostatistics, 1415 Washington Heights, Ann Arbor, MI 48109 USA. Telephone: (734) 763-2451. Fax: (734) 763-2215. E-mail: E-mail Address: [email protected] Department of Biostatistics and Search for more papers by this author , Howard Hu Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author , Heather J. Litman New England Research Institutes, Watertown, Massachusetts, USA Search for more papers by this author , and Martha Maria Téllez-Rojo Division of Statistics, Center for Evaluation Research and Surveys, National Institute of Public Health, Cuernavaca, Morelos, Mexico Search for more papers by this author Published:1 March 2011https://doi.org/10.1289/ehp.1002453Cited by:100AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackgroundIdentifying windows of vulnerability to environmental toxicants is an important area in children’s health research.ObjectiveWe compared and contrasted statistical approaches that may help identify windows of vulnerability by formally testing differences in exposure effects across time of exposure, incorporating continuous time metrics for timing of exposure, and efficiently incorporating incomplete cases.MethodsWe considered four methods: 1) window-specific and simultaneously adjusted regression; 2) multiple informant models; 3) using features of individual exposure patterns to predict outcomes; and 4) models of population exposure patterns depending on the outcome. We illustrate them using a study of prenatal vulnerability to lead in relation to Bayley’s Mental Development Index at 24 months of age (MDI24).ResultsThe estimated change in MDI24 score with a 1-loge-unit increase in blood lead during the first trimester was −2.74 [95% confidence interval (CI), −5.78 to 0.29] based on a window-specific regression. The corresponding change in MDI24 was −4.13 (95% CI, −7.54 to −0.72) based on a multiple informant model; estimated effects were similar across trimesters (p = 0.23). Results from method 3 suggested that blood lead levels in early pregnancy were significantly associated with reduced MDI24, but decreasing blood leads over the course of pregnancy were not. Method 4 results indicated that blood lead levels before 17 weeks of gestation were lower among children with MDI24 scores in the 90th versus the 10th percentile (p = 0.08).ConclusionsMethod 2 is preferred over method 1 because it enables formal testing of differences in effects across a priori–defined windows (e.g., trimesters of pregnancy). Methods 3 and 4 are preferred over method 2 when there is large variability in the timing of exposure assessments among participants. Methods 3 and 4 yielded smaller p-values for tests of the hypothesis that not only level but also timing of lead exposure are relevant predictors of MDI24; systematic power comparisons are warranted.Identifying critical windows of vulnerability to environmental toxicants is an important area in children’s health research. Critical windows of vulnerability are defined as periods during life when an exposure causes a stronger deficit in health later in life compared with other periods when exposure (could have) occurred (Barr et al. 2000; Selevan et al. 2000; West 2002). The research question is often formulated as “Is the exposure more strongly associated with the health outcome if it occurred in time window 1, 2, …, or n?” Formulating the question in terms of discrete time windows is advantageous from a clinical and practical perspective. However, considering timing of exposure in a continuous fashion may be more advantageous from an analytical standpoint.We discuss three statistical approaches that may be useful in studies of windows, or timing, of vulnerability; two of these use time of exposure as a continuous variable. We compare the proposed methods with commonly used statistical approaches such as fitting separate regression models for each potential window and fitting simultaneously adjusted multiple regressions that include all exposure measures (time windows) in one model.As exposition, we use data from a study of lead exposure during pregnancy and mental development. We refer readers to published studies of lead exposure for information regarding the toxicologic effects of lead on development and the relevance of time windows of vulnerability in that context (Hu et al. 2006; Schnaas et al. 2006). We focus on statistical modeling issues and apply all methods to the same data so that the inferences and interpretations across methods can be compared.Materials and MethodsData sourceWe use data from an ELEMENT (Early Life Exposures in Mexico to Environmental Toxicants) study cohort of mother–child pairs recruited during pregnancy or before conception (Téllez-Rojo et al. 2004). We use maternal blood lead concentrations ascertained during study visits scheduled within each trimester. Low birth weight (< 2,500 g) and preterm (< 37 weeks) children were excluded. Children’s mental development was measured at 24 months using Bayley’s Mental Development Index (MDI24) (Bayley 1993). We also collected other participant characteristics (Table 1); data collection procedures are reported elsewhere (Hu et al. 2006; Téllez-Rojo et al. 2004). This analysis was restricted to n = 169 participants with complete covariates (mother’s age and IQ, breast-feeding duration, and child’s sex, height, weight, and blood lead level at 24 months) and at least one measure of prenatal lead exposure.Table 1 Participant characteristics, ELEMENT study data.VariablenMean ± SDMin, maxMaternal characteristics Ln(blood lead) (μg/dL), T11391.90 ± 0.550.095, 3.57 Ln(blood lead) (μg/dL), T21591.78 ± 0.450.095, 3.01 Ln(Blood lead) (μg/dL), T31471.85 ± 0.530.18, 3.64 Age (years)16926.7 ± 5.218, 42 IQ16989 ± 12.855, 120Child Sex (percent male)16950.9% Weight at 24 months (kg)16912 ± 1.59.40, 19.30 Height z-score at 24 months169−0.1 ± 0.93−3.78, 3.22 Ln(blood lead) at 24 months1691.39 ± 0.63−0.22, 3.60 MDI2416991.8 ± 11.668, 122 Breast-feeding duration (months)1696.4 ± 5.70, 24Timing of sample collection (weeks) T113913.7 ± 3.43.9, 20.4 T215924.5 ± 2.818.0, 33.7 T314735.2 ± 1.929.0, 39.0Correlations among ln(blood lead) levelsT1T2T3 Ln(blood lead) T11 Ln(blood lead) T20.661 Ln(blood lead) T30.560.611 Ln(blood lead) of child at 24 months0.170.240.27Abbreviations: max, maximum; min, minimum; T, trimester.Participants gave written informed consent before data collection. The study was approved by the institutional review boards of the hospitals where participants were recruited, the National Institute of Public Health of Mexico, Brigham and Women’s Hospital, Harvard School of Public Health, and the University of Michigan.NotationFor each subject i, assume X1i, X2i, … XKi are measures of exposure taken during Ki time points t1i, t2i, …, tKi; Yi is the health outcome, and Zi are mean-centered covariates. For the lead study, Yi is MDI24, X1i, X2i, … XKi are loge(maternal blood lead levels, micrograms per deciliter), and Zi are maternal age, maternal IQ, child’s sex, and child’s 24-month weight and height-for-age z-score (Kuczmarski et al. 2002). For methods 1 and 2, we assume the Ki measures of exposure fall within K time windows (i.e., K = three trimesters).Method 1: separate and simultaneously adjusted multiple linear regression modelsA commonly used statistical approach in studies of prenatal windows of vulnerability is to fit regression models for each potential window (Aguilera et al. 2009; Bell et al. 2007; Hu et al. 2006; Meyer et al. 2007; Mohorovic 2004; Parker et al. 2005). The magnitude and significance of the regression coefficients are compared to draw conclusions about which window of exposure may be more important.This approach is intuitive, but it has two assumptions that are not always discussed. First, the exposure is assumed constant within the window. For example, a single maternal blood lead measure during the first trimester is assumed to be representative of average exposure during the entire trimester. Second, although it is recognized that “the closer we look, the more evident it is that often there is not a uniform response within a given window” (Barr et al. 2000), the exposure effect (e.g., the effect of prenatal lead exposure on mental development) is assumed to be constant within a time window. When these assumptions are violated, the estimated effects within a predefined window may be biased toward or away from the null, depending on the true nature of the exposure effect and the time at which exposures are measured. Specific examples are shown in the Supplemental Material, Figures 1 and 2 (doi:10.1289/ehp.1002453).Figure 1 (A) Maternal blood lead pattern across gestational period (weeks) for children in the 10th percentile (solid) and 90th percentile (dashed) of the covariate-adjusted MDI24 distribution with 95% pointwise CIs. (B) Relative exposure comparing those in the 10th percentile of the MDI distribution with those in the 90th percentile, with 95% pointwise CIs.The approach is also subject to other statistical limitations. First, estimating separate regressions for each time window precludes formal testing of the differences in effects across time windows. Interpreting nonoverlapping confidence intervals (CIs) as proof that effects differ is not valid, because the estimated coefficients are not statistically independent. This issue is analogous to the incorrect choice of a two-sample t-test over a (correct) paired t-test for testing differences between repeated measures within an individual. Second, it is possible that not all regressions will be based on the same group of subjects because of missing data. For example, a popular approach to maximize the sample size for each regression is to include all observations with data for a given time window in the regression for that time window, even though some may be excluded from models of other time windows because of missing data. However, the available case approach is valid only if data are missing completely at random (MCAR) (Little and Rubin 2002), and it is not possible to verify this assumption based on available (nonmissing) data (for example, not finding significant differences in bivariate analyses comparing subjects with complete versus incomplete data is not sufficient proof of MCAR). Third, because the same outcome is used in all regressions, the hypotheses tested are not independent, and precision may be over- or underestimated because the correlations among the residual errors from the regressions are ignored.A straightforward alternative to fitting separate regressions for each time window is to fit a single simultaneously adjusted regression model that includes all time windows. An advantage of this approach is that estimates of the independent effect of exposure during each window may be obtained (Ha et al. 2007; Lubin et al. 1997). However, the approach is not always feasible because of collinearity issues (e.g., inflated standard errors, unstable regression coefficients). Further, models are restricted to observations that have complete data for all variables.We estimated separate and simultaneously adjusted multiple linear regression models to estimate trimester-specific associations between MDI24 and loge-transformed maternal blood lead levels.Method 2: multiple informantsMultiple informant data refers to information gathered from different individuals or sources used to measure the same construct (Horton et al. 1999; Litman et al. 2007b). One classic example is where mother and child both respond to questions regarding the physical activity of the child, so that the mother and child are the informants. Methods for multiple informant data also can be applied when exposure information is obtained for the same individual at different time points (windows) by treating the exposure windows as informants. Multiple informant methods can be used to test whether the information relayed by different informants (in this case, whether exposure is measured during different time windows) relates in the same manner to an outcome of interest. Although this approach retains the interpretation (and assumptions) of a set of separate multiple regressions (by providing a single estimate of effect for exposure in each time window), it also provides a way to test differences in associations between the exposure and the outcome across time windows.ModelThe associations of primary interest are estimated by β1k from window-specific multiple regressions Yi= β0k+ β1kXki+ β2kZi+ ɛki, for window k = 1,2, …, K. The multiple informants approach jointly estimates the regression models.Joint estimation enables us to impose and test constraints on the regression coefficients across exposure windows. That is, test whether the exposure coefficients are equal across time windows, Ho: β11= β12= … = β1K versus Ha: at least one β1K differs. Parameter estimation can be conducted using generalized estimating equations (GEE) or maximum likelihood (ML) estimation (Horton et al. 1999; Litman et al. 2007a; Pepe et al. 1999). Details regarding data structure, model fitting, hypothesis testing, and macros to analyze the data using this method are provided in the Supplemental Material (doi:10.1289/ehp.1002453).ML estimation proceeds by modeling the exposure at different windows and the outcome as having a K + 1 multivariate normal distribution (i.e., exposure is considered a random variable) and subsequently obtaining the distribution of the outcome conditional on the exposure. The test of equal association between exposure and outcome across time windows can also be performed on standardized regression coefficients (i.e., adjusted correlation coefficient denoted ρ1k = σXkβ1k/σY, k = 1, …, K, where σXk is the SD of the exposure at window k, and σY is the SD of the outcome. Testing adjusted correlation coefficients may be desirable when the variability of the exposure changes over time or if protocols for measuring exposure change across time windows. ML can account for exposure and outcome data missing at random (MAR) (Little and Rubin 2002) and is robust to distributional assumptions when missing data are MCAR (Litman et al. 2007b). MAR means that the fact the data are missing is independent of the actual missing value, after accounting for other observed participant characteristics. MCAR means that the fact the data are missing is independent of the missing value as well as other observed data and hence is more restrictive than MAR.The GEE approach embeds separate linear regression models for each time window into a unified set of estimating equations. In contrast to the ML approach, the exposures are not considered random (dependent) variables. However, unless more sophisticated methods are used (Horton and Lipsitz 1999), the GEE method retains the MCAR assumption for the missing data.We applied the multiple informant approach to the ELEMENT study data. For the GEE approach we used a score type test of the hypothesis Ho: β11= β12= β13 [degrees of freedeom (df) = 2]. Because the variance of the exposure during trimester 2 was smaller than during the other two trimesters, in the ML approach we tested for homogeneity of standardized exposure effects across time windows (Ho: ρ11= ρ12= ρ13) using a likelihood ratio test (df = 2). The p-values for these tests are denoted pint, alluding to an interaction between exposure level and timing of exposure.Method 3: individual patterns of exposure in relation to outcomeWhen assumptions regarding the timing of exposure in methods 1 and 2 are violated (i.e., when timing of exposure measurement or the effect of exposure on the outcome varies within time windows), when a large number of time windows are considered, or when the number of measurement occasions for the exposure varies across participants, the multiple informant approach may no longer be feasible. An alternative is to model the pattern of exposure for each individual over time and then relate exposure features to the outcome.ModelThe underlying idea in this approach is to reduce the number of exposure measures for each individual from Ki, which may vary across participants, to a smaller number equal across participants. A simple example where the exposures are summarized into two exposure features is to model the exposures Xik using a random intercepts and random slopes model, Xik= θ0i+ θ1itik+ ɛik, where k = 1, …, Ki represents the kth measurement occasion. The random effects θ0i, and θ1i are person-specific intercepts and slopes that jointly describe the pattern of exposure for individual i and have a population mean θ = (θ0,θ1) and variance Φ. Next, the outcome is modeled in relation to the exposure features θ0i, and θ1i, for example, Yi= β0+ β11 θ0i+ β12 θ1i+ β2Zi+ ɛi. [Details regarding data structure, model fitting, hypothesis testing, and macros to analyze the data using this method are provided in the Supplemental Material (doi:10.1289/ehp.1002453).]For the ELEMENT study, where a few measures of exposure were available, we employed a model where a random intercept and slope were the only subject-specific exposure features. The time variable was centered at 7 weeks, the middle of the first trimester, such that θ0i represents blood lead levels during gestational week 7. First, the exposure was modeled as Xik= θ0i+ θ1i (tik −7) + ɛik, with tik being gestational time in weeks. Because week 7 is the midpoint of the first trimester, θ0i can also be interpreted as the average exposure for individual i during the first trimester, so that study participants with a random intercept θ0i higher than the mean θ0 would have a higher than average lead exposure in early pregnancy. The slope θ1i represents the subject’s rate of change in exposure across the pregnancy. For example, if the population slope θ1 was negative (indicating declining lead levels across pregnancy), a study participant with a random slope θ1i larger than the mean θ1 would have a slower than average rate of decline in lead levels over the course of pregnancy. We modeled the outcome (MDI24) as Yi= β0+ β11 θ0i+ β12 θ1i+ β2Zi+ ɛi, where β11 is interpreted as the association between exposure during the first trimester and MDI24, and β12 is the association between changes in exposure over the course of pregnancy and MDI24.We fit the model for the exposures and the outcome model jointly using ML (Wang et al. 2000), which yields consistent and efficient estimates of model parameters. Heuristically, a two-step approach also could be used where empirical Bayes estimates of the exposure features are obtained, for example, θ̂0i, and θ̂1i, and then substituted in the outcome model. However, the two-step approach gives inconsistent (likely attenuated) estimates of the regression coefficients (Wang et al. 2000).Method 3 does not require that participants be measured at the same time points, whereas methods 1 and 2 assume the timing of measurements is similar across participants. Furthermore, because the timing of exposure is not discretized into windows, this approach is more useful than the multiple informant approach when windows are not well defined or when little prior information about the etiologically relevant windows are is available. It requires that each participant has sufficient information such that the features can be reliably estimated (i.e., at least one more measurement than features in most participants). In the lead example, at least three measurements are required for most participants, because two features are estimated. Although the interpretation of model parameters may not be as straightforward because the reference to discrete windows is lost, conclusions about the relative importance of broad time periods can still be drawn. In the lead example, this method helps answer the question “After accounting for first-trimester exposure, does changing the exposure in subsequent trimesters matter?” In the lead example, this approach assumes a constant rate of decline in exposure, and the association of exposure with outcome over the course of pregnancy diminishes (or increases) at a linear rate.Method 4: population pattern of exposure given the outcomeThe fourth method consists of describing the population-average exposure pattern for levels of the outcome. For example, in ELEMENT, this method can be used to compare the pattern of exposure for children who achieve high MDI24 scores with the pattern in children with low scores. This approach differs from methods 1–3 because it models the exposure given the health outcome.ModelThe exposure is modeled as Xik = f0(tik) + f1(tik)Yci + δik, where Yci is the outcome for subject i centered at the sample mean (Yci = Yi − Ȳ) or centered at its predicted value (Yci = Yi − Ŷi, where Ŷi is the predicted outcome given Zi) given factors other than exposure (e.g., suspected confounders and possibly other independent predictors of the outcome). The residuals δik are assumed to have mean zero and covariance Δ within individuals but are independent across individuals. The term f0(tik) represents the exposure pattern over time for those with an average outcome, which can be modeled as a parametric function [e.g., f0(tik) = α00 + α01tik + α02t2ik] or a semiparametric curve (e.g., penalized splines). The term f0(tik)Yci quantifies the differences in exposure over time across levels of the outcome. Both f1(tik) and f0(tik) are modeled in the same fashion [e.g., f1(tik) = α10 + α11tik + α12t2ik if f0(tik) is modeled as a quadratic function]. The coefficients α10, α11, α12 jointly describe the pattern of exposure curve over time.An overall test of association between exposure and outcome involves testing Ho: f1(t) = 0 vs. Ha: f1(t) ≠ 0. When f1(t) is given by a quadratic function, then the null hypothesis is Ho: α10 = α11 = α12 = 0 versus Ha: at least one α differs from zero. Testing whether the exposure pattern over time is associated with outcome amounts to testing Ho: α11 = α12 = 0 versus Ha: at least one α differs from zero. For timing of susceptibility (i.e., whether exposure effects vary depending upon the timing of exposure), the more relevant hypothesis is the latter.The estimation of this model can be conducted in two steps. First, a model for the outcome is estimated where only covariates Zi are predictors. The residuals Yci = Yi − Ŷi are then constructed, and the model for Xik (i.e., a model of the exposure conditional on the outcome) is estimated. Details regarding data structure, model fitting, hypothesis testing, and macros to analyze the data using method 4 are provided in the Supplemental Material (doi:10.1289/ehp.1002453).In the ELEMENT data, we obtained Yci = Yi − Ŷi, and interpreted it as the deviation of the individual from the expected MDI24 score given mother’s age, mother’s IQ, duration of breastfeeding, sex, and weight and height z-score at 24 months. For example, Yci will be positive when the MDI24 score of a child is higher than predicted given the characteristics listed above. We then estimated the exposure model using tik as gestational time in weeks and nonparametric and quadratic models for f0(t) and f1(t). Finally, we constructed the predicted exposure pattern (back-transformed to natural units of blood lead) for children in the 10th and 90th percentile of the covariate-adjusted MDI distribution and determined the relative difference in exposure between the 10th and 90th percentiles of the covariate-adjusted MDI distribution across time by exponentiating the difference in the predictions of loge-transformed blood lead.ResultsThe study visits occurred, on average, toward the end of each trimester, with considerable variability in their timing (Table 1). Notably, although the earliest visit among all study participants was at 3.8 weeks of gestation, the average first-trimester visit occurred at 13.7 weeks. The variability in timing of measurements raises concerns about interpretation of regression coefficients as the association between exposure during each trimester and mental development and was the primary motivation for seeking alternative analytical methods for this type of data. Nevertheless, we keep the language of trimesters in the subsequent descriptions of the results for methods 1 and 2.The estimated associations from the separate and simultaneously adjusted regression models were as follows: −5.42 (95% CI, −10.2 to −0.64) and −2.74 (95% CI, −5.78 to 0.29) MDI24 points per 1-loge-unit increase in blood lead in the first trimester, respectively (Table 2). Although not significant, the coefficients for trimesters 2 and 3 from the simultaneously adjusted regression were positive (suggesting higher MDI24 scores with higher lead exposure) and imprecise, which would be consistent with collinearity. The coefficients from separate regressions were all negative and decreased in strength across trimesters, with the strongest association observed at trimester 1. However, none of the estimated effects were statistically significant (p > 0.05).Table 2 Effect of maternal ln(blood lead) on MDI24, estimated from various approaches.Multiple regression (method 1)Multiple informants approach (method 2, n = 169)Simultaneous adjustmentaSeparate regressionsbGEEMLETrimesterβ95% CIβ95% CIβ95% CIβ95% CI1−5.42−10.20 to −0.64−2.74−5.78 to 0.29−2.74−5.82 to 0.33−4.13−7.54 to −0.7220.88−5.34 to 7.09−1.37−4.81 to 2.07−1.37−4.79 to 2.05−2.98−6.86 to 0.9131.22−3.65 to 6.08−1.15−4.20 to 1.90−1.15−4.18 to 1.88−2.04−5.11 to 1.04pintcNANA0.56d0.23eAbbreviations: MLE, maximum likelihood estimates; NA, not available.an = 120.bFor trimester 1, n = 139; trimester 2, n = 159; trimester 3, n = 146.cTest for hypothesis that estimates are equal across trimesters.dScore test of homogeneity of coefficients.eLikelihood ratio tests for homogeneity of standardized estimates.The estimated associations between first-trimester exposure and MDI24 from the multiple informant approach were −2.74 (95% CI, −5.82 to 0.33) and −4.13 (95% CI, −7.54 to −0.72) MDI24 points per 1-loge-unit increase in blood lead from GEE and ML estimation, respectively (Table 2). As is always the case (Litman et al. 2007a), the GEE estimates were equal to the point estimates obtained from separate regressions, but the CIs varied slightly because the GEE method takes into account within-individual correlation across the time windows. Because there were missing data, the ML and GEE approaches do not give the same estimates. The ML approach estimated a stronger association for trimester 1 (e.g., βGEE = −2.74 vs. βML = −4.13). Although a trend of increasing effect of lead in earlier times during pregnancy is suggested, tests of a varying exposure effect were not significant (GEE pint = 0.56, score test of equal regression coefficients; MLE pint = 0.23, likelihood ratio test of equal standardized coefficients). The ML approach detected a significant association at trimester 1 and exhibited a smaller p-value than GEE for the test of differences in association across trimesters. This reflects greater efficiency of ML estimation relative to GEE when data are missing and when differences in standardized regression coefficients are tested, in addition to using score versus likelihood ratio tests.The exposure model parameters from method 3 indicate that the average exposure at 7 weeks of gestation was 1.90 loge(blood lead) units (approximately = 6.69 μg/dL), SD 0.49 (Table 3). This is consistent with the average and SDs of the observed measures during trimester 1 (Table 1). There was a significant average decline across pregnancy (average linear decline is 0.04 ln(blood lead) units per 12 weeks) (Table 3).Table 3 Parameter estimates for method 3.Model parameter or predictorEstimateSEp-ValueExposure model parameters θ0 (average blood lead level, loge)1.900.05< 0.0001 θ1 (average rate of change per 12 weeks)a−0.040.02< 0.01 Random intercept SD0.49 Random slope SD0.17 Correlation of random intercept (θ0i) and slope (θ1i)−0.56 Residual SD0.28Predictors in outcome modelβSEp-ValueBlood lead level at week 7 (θ0i, random intercept)b−2.111.080.05Changes in blood lead level (θ1i, random slope)a,c0.581.680.73Maternal Age (per 5 years)3.040.77< 0.01Maternal IQ (per 10 points)0.760.640.24Sex of child−4.981.71< 0.01Weight at 24 months−2.130.930.02Height z-score at 24 months NA2.821.160.01Breast-feeding duration (per 6 months)−0.630.900.48aAverage rate of change is negative; hence, increases in the rate represent slower rates of blood lead level decline.bChange in MDI24 with a 1-SD increase in blood lead at 7 weeks.cChange in MDI24 with a 1-SD increase in the rate of blood lead level over the course of pregnancy.The regression coefficients for blood lead level at 7 weeks and the average change in blood lead level are expressed in MDI points associated with a 1-SD change in these predictors. A 1-SD increase in loge-transformed blood lead levels at 7 weeks was significantly associated with a 2.11-point decrease in MDI24 (95% CI, −0.01 to 4.23). Although the changes in exposure over the course of pregnancy were not significantly associated with MDI24 (p = 0.73), the positive association, indicating that a slower rate of decline in blood lead (i.e., sustained exposure) was associated with a 0.58 point increase in MDI24, was unexpected. However, this relation may have resulted from collinearity; although the random intercept and slope were less correlated than the raw" @default.
• W4251039891 created "2022-05-12" @default.
• W4251039891 creator A5027965960 @default.
• W4251039891 creator A5064025296 @default.
• W4251039891 creator A5073631691 @default.
• W4251039891 creator A5078987114 @default.
• W4251039891 date "2010-12-08" @default.
• W4251039891 modified "2023-10-16" @default.
• W4251039891 title "Statistical Methods to Study Timing of Vulnerability with Sparsely Sampled Data on Environmental Toxicants" @default.
• W4251039891 cites W1975016497 @default.
• W4251039891 cites W1987442868 @default.
• W4251039891 cites W1992043757 @default.
• W4251039891 cites W2011505534 @default.
• W4251039891 cites W2017614241 @default.
• W4251039891 cites W2035269482 @default.
• W4251039891 cites W2042152636 @default.
• W4251039891 cites W2042191659 @default.
• W4251039891 cites W2046182010 @default.
• W4251039891 cites W2051145078 @default.
• W4251039891 cites W2054379651 @default.
• W4251039891 cites W2067165060 @default.
• W4251039891 cites W2068142058 @default.
• W4251039891 cites W2068319287 @default.
• W4251039891 cites W2073857810 @default.
• W4251039891 cites W2086198494 @default.
• W4251039891 cites W2087416935 @default.
• W4251039891 cites W2095318951 @default.
• W4251039891 cites W2095391850 @default.
• W4251039891 cites W2103970079 @default.
• W4251039891 cites W2104800336 @default.
• W4251039891 cites W2109424626 @default.
• W4251039891 cites W2112964831 @default.
• W4251039891 cites W2147076920 @default.
• W4251039891 cites W2151223134 @default.
• W4251039891 cites W2158555057 @default.
• W4251039891 cites W4242188586 @default.
• W4251039891 cites W4249497568 @default.
• W4251039891 doi "https://doi.org/10.1289/ehp.1102453" @default.
• W4251039891 hasPublicationYear "2010" @default.
• W4251039891 type Work @default.
• W4251039891 citedByCount "14" @default.
• W4251039891 countsByYear W42510398912012 @default.
• W4251039891 countsByYear W42510398912015 @default.
• W4251039891 countsByYear W42510398912017 @default.
• W4251039891 countsByYear W42510398912018 @default.
• W4251039891 countsByYear W42510398912019 @default.
• W4251039891 countsByYear W42510398912020 @default.
• W4251039891 countsByYear W42510398912022 @default.
• W4251039891 crossrefType "journal-article" @default.
• W4251039891 hasAuthorship W4251039891A5027965960 @default.
• W4251039891 hasAuthorship W4251039891A5064025296 @default.
• W4251039891 hasAuthorship W4251039891A5073631691 @default.
• W4251039891 hasAuthorship W4251039891A5078987114 @default.
• W4251039891 hasBestOaLocation W42510398911 @default.
• W4251039891 hasConcept C119709500 @default.
• W4251039891 hasConcept C18903297 @default.
• W4251039891 hasConcept C2776356880 @default.
• W4251039891 hasConcept C38652104 @default.
• W4251039891 hasConcept C39432304 @default.
• W4251039891 hasConcept C41008148 @default.
• W4251039891 hasConcept C71924100 @default.
• W4251039891 hasConcept C86803240 @default.
• W4251039891 hasConcept C95713431 @default.
• W4251039891 hasConcept C99454951 @default.
• W4251039891 hasConceptScore W4251039891C119709500 @default.
• W4251039891 hasConceptScore W4251039891C18903297 @default.
• W4251039891 hasConceptScore W4251039891C2776356880 @default.
• W4251039891 hasConceptScore W4251039891C38652104 @default.
• W4251039891 hasConceptScore W4251039891C39432304 @default.
• W4251039891 hasConceptScore W4251039891C41008148 @default.
• W4251039891 hasConceptScore W4251039891C71924100 @default.
• W4251039891 hasConceptScore W4251039891C86803240 @default.
• W4251039891 hasConceptScore W4251039891C95713431 @default.
• W4251039891 hasConceptScore W4251039891C99454951 @default.
• W4251039891 hasIssue "3" @default.
• W4251039891 hasLocation W42510398911 @default.
• W4251039891 hasLocation W42510398912 @default.
• W4251039891 hasOpenAccess W4251039891 @default.
• W4251039891 hasPrimaryLocation W42510398911 @default.
• W4251039891 hasRelatedWork W2034223291 @default.
• W4251039891 hasRelatedWork W2119151793 @default.
• W4251039891 hasRelatedWork W2972395350 @default.
• W4251039891 hasRelatedWork W2981372145 @default.
• W4251039891 hasRelatedWork W3048170864 @default.
• W4251039891 hasRelatedWork W3103483631 @default.
• W4251039891 hasRelatedWork W3214712827 @default.
• W4251039891 hasRelatedWork W4281716148 @default.
• W4251039891 hasRelatedWork W4320065787 @default.
• W4251039891 hasRelatedWork W4323351184 @default.
• W4251039891 hasVolume "119" @default.
• W4251039891 isParatext "false" @default.
• W4251039891 isRetracted "false" @default.
• W4251039891 workType "article" @default.