a : First median absolute percent bias of β1 was calculated for each simulation scenario, then summarized across scenarios.b : This is the number of simulation scenarios used to calculate the information.

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

a : Median absolute percent bias of σ2u was calculated for each simulation scenario, then summarized across scenarios.b : This is the number of simulation scenarios used to calculate the information.

*The dependency value ranges from 0.000 to 1.000. A value of “0.000” shows complete independence of the Consensus Index from the Delphi survey characteristic examined (e.g., the number of questions) whereas a value of “1.000” shows complete dependence. The dependency value is the maximum numeric difference observed for each consensus index when the number of questions in a simulated Delphi survey varied from 6 to 40.All Delphi consensus indices (the left column) typically take a value ranging from 0.000 to 1.000, except the Interquartile Range (IQR). For example, in the case of Fleiss’ Kappa, a maximum difference of 0.025 can be anticipated when the number of Delphi survey questions vary from 6 to 40.For the Interquartile Range, the dependency data were normalized by dividing the difference observed in simulations by the maximum possible difference (9.000), i.e., the length of the Likert scale from 1 to 10 used in the simulations.RANK ORDER of the Dependency of Consensus Indices’ on the NUMBER OF QUESTIONS (6–40) in a Delphi Survey

IQR, interquartile range; no, number; SD, standard deviation; In total, 103 patients used systemic immunomodulating therapy after starting dupilumab. Eighteen of these patients were using systemic immunomodulating therapy from start dupilumab till study cut-off point: ciclosporin (n=6), azathioprine (n=2), methotrexate (n=4), mycophenolic acid/mycophenolate mofetil (n=3), systemic corticosteroids (n=3). Two patients stopped for respectively 14 and 33 days with systemic corticosteroids treatment and then restarted with systemic corticosteroids which they were still using at the end of study. This table displays the 83 patients that discontinued systemic concomitant immunomodulating therapy during the study.

Code

• 01TextGrid.praat
  Segment and label the sound files in all folders under the directory
• 02Extract voice quality data.praat
  Extract voice quality parameters, including jitter, shimmer, HNR, CPP, H1-H2, H1-A1, H1-A2, and H1-A3
• 03Extract pitch data.praat
  Extract pitch data, including maximum, minimum, range, mean, upper quartile, lower quartile, pitch inter-quartile range, and median absolute deviation
• 04Correlation Analysis and Mantel Test.R
  Correlation tests between different variables and create correlation plots.
• 05GAMM_Voice quality~Climate factors.R
  Examine the relationship between climate factors and voice quality
• 06GAMM_Tone ~ Voice quality.R
  Examine the relationship between voice quality and the number of tones
• 07GAMM_Tone~Climate factors.R
  Examine the relationship between climate factors and the number of tones
• 08GAMM_Pitch~Climate factors.R
  Examine the relationship between pitch variation, the number of tones, and climate factors

Data

All extracted data files are in the data folder.

• 1525dataset.csv
  The file includes data for 1,525 language varieties with the following information: geographic location names (column A), linguistic classification and ASJP name information (columns B-E), longitude and latitude and information (columns F-G), number of tones (column H), Pitch information (columns I-J), voice quality information (columns K-R), climate information (columns S-X)
• Geographical distance.csv
  The geographic distances between 1,525 language varieties were calculated using the Delaunay-Dijkstra method
• Language distance.csv
  The language distances between 1,525 language varieties were calculated using the ASJP method.
• Specifichumiditydif.csv
  Specific humidity difference dataset for the locations of 1,525 language varieties
• Tonedif.csv
  Tone difference dataset among 1,525 language varieties
• Voice quality data extracted using different methods.csv
  Voice quality data for 1,115 dialectal variants, analyzed at both the lexical level and the vowel "a" level. Columns B–I present voice quality parameters extracted from the vowel, while columns J–Q provide data extracted from the lexical items.

Standardized test descriptive statistics: number of participants, mean, standard deviation, median and interquartile range.

BackgroundIndividual patient data meta-analyses (IPDMAs) prevail as the gold standard in clinical evaluations. We investigated the distribution and epidemiological characteristics of published IPDMA articles.Methodology/Principal FindingsIPDMA articles were identified through comprehensive literature searches from PubMed, Embase, and Cochrane library. Two investigators independently conducted article identification, data classification and extraction. Data related to the article characteristics were collected and analyzed descriptively. A total of 829 IPDMA articles indexed until 9 August 2012 were identified. An average of 3.7 IPDMA articles was published per year. Malignant neoplasms (267 [32.2%]) and circulatory diseases (179 [21.6%]) were the most frequently occurring topics. On average, each IPDMA article included a median of 8 studies (Interquartile range, IQR 5 to 15) involving 2,563 patients (IQR 927 to 8,349). Among 829 IPDMA articles, 229 (27.6%) did not perform a systematic search to identify related studies. In total, 207 (25.0%) sought and included individual patient data (IPD) from the “grey literature”. Only 496 (59.8%) successfully obtained IPD from all identified studies.Conclusions/SignificanceThe number of IPDMA articles exhibited an increasing trend over the past few years and mainly focused on cancer and circulatory diseases. Our data indicated that literature searches, including grey literature and data availability were inconsistent among different IPDMA articles. Possible biases may arise. Thus, decision makers should not uncritically accept all IPDMAs.

Median values, interquartile range (IQR) and Number of outliers.

Descriptive statistics of non-temporal variables: The median and interquartile range are shown for non-categorical variables, and the number of patients in each category is shown for categorical variables.

Baseline characteristics of participants with and without incident cancer in the DHS (data are reported as median (interquartile range) or number (%), as appropriate).

Unless otherwise indicated, data are number (percentage). IQR, interquartile range; classification of complications according to STS criteria.

Factors associated with CD4 recovery to > 350 cells/μL following a CD4

Adult females, adult males, and All adults with first CD4 count test

Demographic, clinical and laboratory data of iCCA patients enrolled in the study. Data are presented as median (interquartile range: IQR) or number (%).

Patient characteristics by exacerbation count levels. Shown are the characteristics of patients according to their number of exacerbations during the study period. For each exacerbation level, the number and percentage of patients are shown for categorical variables, and the Median and Interquartile Range (IQR) are shown for continuous variables.

IQR: interquartile range; n/N: number of studies with the condition/total number of studies;*Switzerland and sub-Saharan area.**Calculated from 17 studies enrolling HIV-uninfected individuals.†Data available for 34 studies;‡Only HIV-infected individuals.

Mean and interquartile range (in bracket) on the MAIA.

Summary statistics (mean, standard deviation, median, interquartile range, number of subjects) for “ln_adducts” in cases, controls, and total population.

Abbreviation: IQR, interquartile range.*Data are number (percentage) except where indicated.+Group comparisons by the chi-square test.