Participant disposition by randomization group, showing modified intent-to-treat (mITT) and modified per-protocol (mPP) cohorts.

<p>Participant disposition by randomization group, showing modified intent-to-treat (mITT) and modified per-protocol (mPP) cohorts.</p

Proportion of babies reporting infectious disease symptoms by week over the first year (mITT cohort).

<p>Proportion of babies reporting infectious disease symptoms by week over the first year (mITT cohort).</p

Maternal and household characteristics at enrollment, by intervention group (ITT cohort^a).

<p>Maternal and household characteristics at enrollment, by intervention group (ITT cohort<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199298#t001fn002" target="_blank">^a</a>).</p

Appendices – Supplemental material for Association between physical health and cardiovascular diseases: Effect modification by chronic conditions

<p>Supplemental material, Appendices for Association between physical health and cardiovascular diseases: Effect modification by chronic conditions by Nazmus Saquib, Robert Brunner, Manisha Desai, Matthew Allison, Lorena Garcia and Marcia L Stefanick in SAGE Open Medicine</p

Baseline characteristics before and after inverse probability of selection weighting (IOPW).

Baseline characteristics before and after inverse probability of selection weighting (IOPW).</p

Fig 2 -

Kaplan Meier Curves in a) IPTW adjusted (for confounding only) cohort of second-line patients b) IPTW and IOPW adjusted (for both confounding and non-representativeness) cohort of first-line patients. Time is shown in days. Note that sample size was impacted by weighting in our analyses.</p

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.

Baseline characteristics before and after inverse probability of treatment weighting (IPTW) among the second-line population.</p

Comparison of variables involved in Step 1 and Step 2 analyses.

Comparison of variables involved in Step 1 and Step 2 analyses.</p

Directed acyclic graph to illustrate our two-step approach to adjust for confounding with inverse probability of weighting (IPTW) and non-representativeness with inverse probability of selection weighting (IOPW).

A = treatment assignment; Y = outcome; S = selection into original population from target population; C = confounders; E = treatment effect modifiers.</p

Effects of Varying Epoch Lengths, Wear Time Algorithms, and Activity Cut-Points on Estimates of Child Sedentary Behavior and Physical Activity from Accelerometer Data

<div><p>Objective</p><p>To examine the effects of accelerometer epoch lengths, wear time (WT) algorithms, and activity cut-points on estimates of WT, sedentary behavior (SB), and physical activity (PA).</p><p>Methods</p><p>268 7–11 year-olds with BMI ≥ 85^th percentile for age and sex wore accelerometers on their right hips for 4–7 days. Data were processed and analyzed at epoch lengths of 1-, 5-, 10-, 15-, 30-, and 60-seconds. For each epoch length, WT minutes/day was determined using three common WT algorithms, and minutes/day and percent time spent in SB, light (LPA), moderate (MPA), and vigorous (VPA) PA were determined using five common activity cut-points. ANOVA tested differences in WT, SB, LPA, MPA, VPA, and MVPA when using the different epoch lengths, WT algorithms, and activity cut-points.</p><p>Results</p><p>WT minutes/day varied significantly by epoch length when using the NHANES WT algorithm (p < .0001), but did not vary significantly by epoch length when using the ≥ 20 minute consecutive zero or Choi WT algorithms. Minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA varied significantly by epoch length for all sets of activity cut-points tested with all three WT algorithms (all p < .0001). Across all epoch lengths, minutes/day and percent time spent in SB, LPA, MPA, VPA, and MVPA also varied significantly across all sets of activity cut-points with all three WT algorithms (all p < .0001).</p><p>Conclusions</p><p>The common practice of converting WT algorithms and activity cut-point definitions to match different epoch lengths may introduce significant errors. Estimates of SB and PA from studies that process and analyze data using different epoch lengths, WT algorithms, and/or activity cut-points are not comparable, potentially leading to very different results, interpretations, and conclusions, misleading research and public policy.</p></div