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Human growth begins at conception and continues into adult life. Growth is usually classified as normal or abnormal using the expected attained size at a given age. The statistical analysis of growth data has been of interest to many academics from a wide range of disciplines over the last century. The study of fetal and neonatal growth is complex, and the many remaining clinical questions require complex statistical methodology. In this thesis, I have focused on growth in the prenatal period, namely fetal and newborn growth. The thesis highlights the potential pitfalls in methodology, statistical methods, and reporting of studies aimed at creating fetal charts. I propose a checklist for evaluating the methodological quality of studies that provides a rough guide of the minimum information that should be reported in these studies. This checklist is not intended to commend or discard studies, but rather to act as a consensus guideline to improve consistency and as a guide for evaluating similar studies for future research in human growth studies. The thesis assesses approaches for developing charts of attained size at a given age and the velocity gain (rate or speed of growth) of a fetus. It aims to address some of the statistical issues that relate to fetal and neonatal growth studies. The thesis also focuses on fetal velocity. This work is novel as there are currently no fetal velocity standards in existence. The methodologies discussed have previously been applied to child growth data, but not to fetal data, to the best of my knowledge. In this thesis, I construct the first fetal growth velocity standards. Such standards were not yet available largely due to lack of appropriate, good quality longitudinal fetal data. All of the work and research carried out as part of the thesis was embedded within the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st Project). The primary aims of the INTERGROWTH-21st project were to produce international growth standards for practical clinical applications and to monitor trends in populations using three sets of data describing fetal growth (n=4,321 women), postnatal growth of preterm infants (n=201 preterm infants), and newborn size (weight, length, and head circumference) for gestational age (n=20,486 newborns). The design and conduct of these studies are detailed elsewhere and have already resulted in numerous publications, including four papers published in The Lancet. I was the responsible statistician for these publications and undertook all of the statistical analyses.

Background Selective reporting of outcomes within a published study based on their nature or direction can result in systematic differences between reported and unreported data. Direct evidence of outcome reporting bias is limited to case reports. Objective To study empirically the nature of outcome reporting bias in randomised controlled trials (RCTs). Methods Three cohorts of RCTs were identified: PubMed-indexed RCTs published in December 2000; trial protocols approved by a Danish ethics committee from 1994-95; and trial protocols funded by a government agency in Canada from 1990-98. Data on reported and unreported outcomes were recorded from all trial publications and a survey of authors. An outcome was considered incompletely reported if insufficient data were presented for meta-analysis. Odds ratios relating the completeness of outcome reporting to statistical significance were calculated for each trial, and then pooled using a random effects meta-analysis. Protocols and publications were also reviewed for discrepancies in primary outcome reporting. Results 519 trials with 10,557 outcomes, 102 trials with 3613 outcomes, and 48 trials with 1390 outcomes were identified for the PubMed, ethics committee, and funding agency cohorts respectively. 22%-35% of outcomes per parallel group study were, on average, incompletely reported for meta-analysis. Fully reported outcomes had a two- to three-fold higher odds of being statistically significant compared to incompletely reported outcomes. The most common reasons given for omitting outcomes included a lack of clinical importance, lack of statistical significance, and space constraints. Major discrepancies between primary outcomes in protocols and publications were found in one half of trials. Discussion and conclusions The reporting of trial outcomes is frequently inadequate for meta-analysis; is biased to favour statistical significance; and is inconsistent with pre-specified protocol outcomes. Unacknowledged modifications to outcomes specified in trial protocols constitute scientific misconduct. Meta-analyses may therefore produce inflated and unreliable estimates of treatment effect.