Association Between Mother and Child Weight Gain: Statistical Methods With Validation
Conditions: Obesity, Childhood
Sponsor: Vanderbilt University Medical Center

Maternal weight trajectories around pregnancy and obesity in early childhood 
 
Study Protocol 
Version 1.1    
2020-03-04 
 
1.0 .  Background 
 
Childhood obesity is becoming a more severe problem in the United States, particularly in the 
southeast region of the nation. Maternal behaviors and characteristics are critical for 
determining the health of their children during the first few years of life. There is keen interest 
in better understanding factors associated with childhood obesity and potential interventions 
to lower its risk. In particular, there is interest in understanding the relative contribution of a 
mother’s weight before, during, and after pregnancy on childhood obesity. 
  
The Vanderbilt University Medical Center (VUMC) includes electronic medical records data for 
over 2 million patients. Linked data from mothers and children delivered at VUMC are available 
and provide us with the opportunity to study factors associated with childhood obesity from a 
large and diverse group of mothers and children. There are also some challenges with using 
electronic health records data for medical research, because the data are often more prone to 
errors. We will validate targeted subsamples of electronic health records and apply novel 
statistical methods to improve estimation. 
 
2.0 . Study Objective:  
 
This study will examine the relative contributions of maternal weight trajectory before, during, 
and after pregnancy on the cumulative probability of childhood obesity at ages 2 and 5 years. 
We will consider whether patterns in mother weight, such as being overweight or obese before 
the pregnancy, gaining excess weight during pregnancy and not losing it, gaining excess weight 
during pregnancy and losing it, or not gaining excess weight affect the risk of childhood obesity. 
Other childhood outcomes in relation to obesity of the child and mother will also be 
considered, such as asthma and cardiovascular outcomes (e.g. high blood pressure).  
 
2.1. Inclusion Criteria:  
 
Mothers and child dyads at VUMC will be included. VUMC has the ability to link mother-child 
EHR, and in an earlier dataset, there were 27,390 mother-child pairs with an existing linkage 
recorded in a Mom-Baby Linkage table.  
 
Maternal inclusion criteria:  
• In a mother-child dyad in the Mom-Baby Linkage table 
• Age > 16 years  
• At least one measure of weight within 273 days prior to birth of the baby 

• At least one height measure 
 
Child inclusion criteria: 
• In a mother-child dyad 
• At least 2 weights, where one weight occurs within 24 hours of birth 
• At least one simultaneous height and weight measure at >730 days after birth and before 
6 years of age 
 
3.0. Outcomes 
 
Primary outcome:  
Time from birth to body mass index (BMI) at or above the 95th percentile based on sex-specific 
reference populations using CDC BMI-for-age growth charts for children ≤5 years  
 
Secondary Outcomes:  
• Child Weight at 2 years and 5 years 
• Incidence of childhood obesity at 2 and 5 years 
• Asthma at age 4-5 years 
 
Exploratory outcomes: 
Childhood comorbidities, including high cholesterol, and high blood pressure, in first five years 
of age 
 
 
4.0. Study Design 
This is a retrospective, observational study using already collected data. All eligible mother-
child dyads in the VUMC Mom-Baby Linkage table will be included for study. We will be 
prospectively validating EHR data for a subsample of patient records from this cohort, further 
details are provided below. 
 
5.0. Statistical Considerations 
 
5.1. Analysis Considerations 
 
Key covariates: Maternal race/ethnicity, maternal age at pregnancy event, maternal diabetes, 
tobacco use, medications, mode of delivery, maternal mental health status, socio-economic 
status, parity, marital status, alcohol use, child sex, child clinical diagnoses, and estimated 
gestational age of the child at delivery. 
 
Proposed analyses: The cumulative probability of childhood obesity according to age will be 
estimated using Kaplan-Meier methods. Follow-up for primary time-to-event analyses will be 
the time from turning 2 years old until the first of childhood obesity, last measurement in the 
EHR, or 6 years. The association between time-to-obesity and maternal weight trajectories 

before, during, and after pregnancy will be examined using Cox regression adjusted for the key 
covariates listed above. Maternal weight trajectories will be summarized using weight during 
the year prior to pregnancy, weight change during pregnancy, and weight change during the 
year after pregnancy. Weight change will be treated as a continuous variable for all analyses, 
although we may consider additional analyses that categorize it.  
 
We anticipate substantial variation in the frequency of measurements across mothers and 
children, and we anticipate the appropriate choice of analysis method will depend on the level 
of completeness of the data.  We may consider joint models of longitudinal maternal weight 
measurements and times-to-childhood obesity. We will also consider functional analysis 
methods to model overall trends in addition to individual weight trajectories.  We will consider 
very carefully methods to adjust for potential biases that could arise if the numbers of 
measurements on a woman or child are informative of health status. We will consider 
propensity score type adjustments, to adjust for the propensity of having data at a specific 
milestone (e.g. child 2yr or 5yr birthday), as well as methods such as outputation (Follmann et 
al 2003) which will equalize the number of measures per individual to explore robustness of 
results across different methods to adjust for these potential biases.  Standard methods, e.g. 
multiple imputation, will be used to handle missing data. 
 
We will assess the association between maternal weight trajectories and childhood asthma at 
age 4 or 5 using logistic regression, limited to the children with data at ages 4 or 5 years.  
Multivariable logistic regression models will adjust for similar covariates to those described 
above for the obesity analyses. 
 
Methods to address error in variables: We anticipate there being a moderate number of errors 
in weight measurements, primarily due to measurement error, but some also due to recording 
errors. We anticipate that date of birth will be largely accurate in the EHR, but that other dates 
and clinical events will be prone to errors. Some of the covariates (specifically, race, diabetes, 
tobacco use, and medications) are known to be particularly error-prone, and we anticipate that 
patient records with errors in one variable will tend to be more likely to have errors in multiple 
variables. We also anticipate a moderate number of errors in the linkage of maternal-child 
records.  
 
We will implement a multi-wave, targeted validation sub-study; the validation strategy is 
outlined in the next section. Once we have obtained validation data for a subsample of patient 
records, we will perform the proposed study analyses highlighted above but augmented using 
specific methods to incorporate both the validated and unvalidated data. We will consider 
multiple analysis approaches as what is optimal for the problem may depend on the error 
structure. Namely, we will consider a maximum likelihood, multiple imputation, and 
generalized raking approaches. The optimal raking variable will be constructed using multiple 
imputation, as described in Han et al. (2019).  Single imputation methods, such as regression 
calibration, in combination with raking will also be considered (Oh et al., 2019) These methods 
will take into account the targeted nature of the validation sample (i.e., the fact that the 

validation sample is not a completely random sample, but rather a targeted random sample 
[e.g., weighted or stratified sampling] in the first and second validation waves).  
 
Data Validation Strategy 
Our goal will be to optimize the information learned (i.e., minimize variance of corrected 
estimates) based on our budgetary constraints; we anticipate we will be able to validate 
between 500-1000 records.  
 
Data validators will receive validation training from Drs. Shepherd, Duda, and Heerman, and will 
enter validation data into standardized REDCap validation forms built off of the PCORnet 
Common Data Model. We anticipate validation that will happen in 2 waves. The first wave will 
be treated as a pilot sample and information learned regarding data discrepancies will be used 
to tailor the individuals selected in the second validation wave. 
 
We will design our multi-wave validation sampling with two separate analyses in mind: (1) 
association between maternal weight change during pregnancy and time to childhood obesity 
and (2) association between maternal weight change during pregnancy and probability of 
asthma for child. We will design our validation sample such that we develop two separate 
sampling schemes to optimize these two separate analyses.  As childhood obesity is our primary 
endpoint, approximately 2/3 of our chart reviews will be targeted towards this endpoint, the 
other 1/3 will be targeted towards childhood asthma. Naturally, the charts that are targeted for 
validation to improve estimation of the childhood obesity outcome will still be used for the 
childhood asthma analyses and vice versa, even though they are not targeted for that purpose.  
 
For the first wave of validation sampling, no more than half of the records will be sampled 
according a balanced stratified sample, with randomly selected samples within strata. For the 
childhood obesity endpoint, we will create 6 strata based on all combinations of obesity 
(yes/no) and rate of maternal weight gain (≤25th percentile, 25th-75th percentile, >75th 
percentile). In wave 1 of chart validation, we will validate 40 charts per stratum (total of 240 
charts). For the asthma endpoint, we will create 6 strata based on all combinations of childhood 
asthma (yes/no) and rate of maternal weight gain (≤25th percentile, 25th-75th percentile, >75th 
percentile); note that the only patients included in these analyses will be those children 
followed with at least one visit from ages 4-5 years. In wave 1, we will validate 25 charts per 
stratum (total of 150). Therefore, our first wave of chart reviews will include a total of 390 
charts. For the chosen records, validation will be performed by going through the entire EHR 
(free text and other fields not part of the PCORnet Common Data Model) to validate key study 
variables, which include the primary outcomes, exposures, and key covariates given above. De-
identified validation data will be securely transferred to study investigators (Drs. Shepherd and 
Shaw) who will then use this information to tailor a second wave of data validation.  
 
For the second wave of validation, we anticipate over-sampling/targeting records that are more 
influential, such as those that appear to be more prone to errors (based on our initial wave of 
data validation), records that are more likely to have observed events, and records with 
extreme values or influence on the estimating equations. We plan to target our samples to 

minimize the variance of the estimate of our primary outcome (time-to-obesity) and secondary 
outcome (incidence of asthma). Hence, we plan to sample following adaptations of the mean-
score sampling approach of McIsaac and Cook (2015) to optimize sampling for the discrete 
proportional hazards and logistic regression models. Neyman allocation strategies will be 
employed to assure efficiency and to guarantee sufficient information per stratum so that all 
analysis methods may be employed. If the first wave reveals a large amount of outcome 
misclassification, then these methods may be adjusted so as stratify on predicted true positive 
versus predicted true negative results, versus the error prone outcome alone in the second 
wave.  For example, rather than sampling based on influence functions plugging in the phase 1 
data, we may sample based on influence functions plugging in multiply imputation estimators 
based on the wave 1 validation. We anticipate validating several hundred additional records for 
this wave 2 validation; hopefully at least as many as in the wave 1 validation. Exact numbers to 
validate will be determined based on feasibility and costs learned from the wave 1 validation. 
Data from this second wave of validation will be similarly entered into REDCap and transferred 
to study investigators. 
 
References 
 
Follmann D, Proschan M, Leifer E. Multiple outputation: inference for complex clustered data 
by averaging analyses from independent data. Biometrics. 2003 Jun;59(2):420-9. 
 
Han K, Shaw PA, Lumley T. Combining multiple imputation with raking of weights in the setting 
of nearly-true models. (arXiv:1910.01162[stat.ME]) 
 
McIsaac MA, Cook RJ. Adaptive sampling in two-phase designs: a biomarker study for 
progression in arthritis. Statistics in Medicine. 2015 Sep 20;34(21):2899-912. 
 
Oh E, Shepherd BE, Lumley T, Shaw PA. Raking and Regression Calibration: Methods to Address 
Bias from Correlated Covariate and Time-to-Event Error. (arXiv:1905.08330v1 [stat.ME])