Topics in the Design of Life History Studies

Substantial investments are being made in health research to support the conduct of large cohort studies with the objective of improving understanding of the relationships between diverse features (e.g. exposure to toxins, genetic biomarkers, demographic variables) and disease incidence, progression, and mortality. Longitudinal cohort studies are commonly used to study life history processes, that is patterns of disease onset, progression, and death in a population. While primary interest often lies in estimating the effect of some factor on a simple time-to-event outcome, multistate modelling offers a convenient and powerful framework for the joint consideration of disease onset, progression, and mortality, as well as the effect of one or more covariates on these transitions. Longitudinal studies are typically very costly, and the complexity of the follow-up scheme is often not fully considered at the design stage, which may lead to inefficient allocation of study resources and/or underpowered studies. In this thesis, several aspects of study design are considered to guide the design of complex longitudinal studies, with the general aim being to obtain efficient estimates of parameters of interest subject to cost constraints. Attention is focused on a general $K$ state model where states $1, \ldots, K-1$ represent different stages of a chronic disease and state $K$ is an absorbing state representing death. In Chapter 2, we propose an approach to design efficient tracing studies to mitigate the loss of information stemming from attrition, a common feature of prospective cohort studies. Our approach exploits observed information on state occupancy prior to loss-to-followup, covariates, and the time of loss-to-followup to inform the selection of individuals to be traced, leading to more judicious allocation of resources. Two settings are considered. In the first there are only constraints on the expected number of individuals to be traced, and in the second the constraints are imposed on the expected cost of tracing. In the latter, the fact that some types of data may be more costly to obtain via tracing than other types of data is dealt with. In Chapter 3, we focus on two key aspects of longitudinal cohort studies with intermittent assessments: sample size and the frequency of assessments. We derive the Fisher information as the basis for studying the interplay between these factors and to identify features of minimum-cost designs to achieve desired power. Extensions which accommodate the possibility of misclassification of disease status at the intermittent assessments times are developed. These are useful to assess the impact of imperfect screening or diagnostic tests in the longitudinal setting. In Chapter 4, attention is turned to state-dependent sampling designs for prevalent cohort studies. While incident cohorts involve recruiting individuals before they experience some event of interest (e.g. onset of a particular disease) and prospectively following them to observe this event, prevalent cohorts are obtained by recruiting individuals who have already experienced this event at some point in the past. Prevalent cohort sampling yields length-biased data which has been studied extensively in the survival setting; we demonstrate the impact of this in the multistate setting. We start with observation schemes in which data are subject to left- or right-truncation in the failure-time setting. We then generalize these findings to more complex multistate models. While the distribution of state occupancy at recruitment in a prevalent cohort sample may be driven by the prevalences in the population, we propose approaches for state-dependent sampling at the design stage to improve efficiency and/or minimize expected study cost. Finally, Chapter 5 features an overview of the key contributions of this research and outlines directions for future work

Cohort study design for illness-death processes with disease status under intermittent observation

Cohort studies are routinely conducted to learn about the incidence or progression rates of chronic diseases. The illness-death model offers a natural framework for joint consideration of non-fatal events in the semi-competing risks setting. We consider the design of prospective cohort studies where the goal is to estimate the effect of a marker on the risk of a non-fatal event which is subject to interval-censoring due to an intermittent observation scheme. The sample size is shown to depend on the effect of interest, the number of assessments, and the duration of follow-up. Minimum-cost designs are also developed to account for the different costs of recruitment and follow-up examination. We also consider the setting where the event status of individuals is observed subject to misclassification; the consequent need to increase the sample size to account for this error is illustrated through asymptotic calculations.This research was supported by an Alexander Graham Bell Canada Graduate Scholarship and an Ontario Graduate Scholarship to N. Moon, Discovery Grants from the Natural Science and Engineering Research Council of Canada to L. Zeng (RGPIN 115928) and R.J. Cook (RGPIN 155849) and from the Canadian Institutes for Health Research to R.J. Cook (FRN 13887). This work was conducted while R.J. Cook held a Canada Research Chair in Statistical Methods for Health Research

Tracing studies in cohorts with attrition: Selection models for efficient sampling

This is the peer reviewed version of the following article: Nathalie C. Moon, Leilei Zeng and Richard J. Cook, Tracing studies in cohorts with attrition: Selection models for efficient sampling, Statistics in Medicine (2018), 37(15): 2354–2366 which has been published in final form at https://doi.org/10.1002/sim.7646. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Cohort studies of chronic diseases involve recruitment and longitudinal followup of affected individuals with a view to studying the effect of risk factors on disease progression and death. When the time to withdrawal from the cohort is conditionally independent of the disease process the primary consequence is a loss of information on the parameters of interest. This loss can sometimes be mitigated through the conduct of tracing studies in which a subsample of those lost to follow up are contacted and some information is obtained on their disease and survival status. We describe the use of selection models to sample individuals for tracing who will yield more efficient estimators than those obtained by simple random sampling. Efficient sampling schemes featuring cost constraints are also developed and shown to perform well. An application to data from the University of Toronto Psoriatic Arthritis Cohort illustrates how to apply the method in a real setting.Natural Science and Engineering Research Council of Canada, Grant/Award Numbers: RGPIN 115928 and RGPIN 155849; Canadian Institutes for Health Research, Grant/Award Number: FRN 1388

The IMPROVE guidelines (Ischaemia Models: Procedural Refinements Of in Vivo Experiments)

Most in vivo models of ischaemic stroke target the middle cerebral artery and a spectrum of stroke severities, from mild to substantial, can be achieved. This review describes opportunities to improve the in vivo modelling of ischaemic stroke and animal welfare. It provides a number of recommendations to minimise the level of severity in the most common rodent models of middle cerebral artery occlusion, while sustaining or improving the scientific outcomes. The recommendations cover basic requirements pre-surgery, selecting the most appropriate anaesthetic and analgesic regimen, as well as intraoperative and post-operative care. The aim is to provide support for researchers and animal care staff to refine their procedures and practices, and implement small incremental changes to improve the welfare of the animals used and to answer the scientific question under investigation. All recommendations are recapitulated in a summary poster (see supplementary information)

Biochemical and antiparasitic properties of inhibitors of the Plasmodium falciparum calcium-dependent protein kinase PfCDPK1.

PfCDPK1 is a Plasmodium falciparum calcium-dependent protein kinase, which has been identified as a potential target for novel antimalarial chemotherapeutics. In order to further investigate the role of PfCDPK1, we established a high-throughput in vitro biochemical assay and used it to screen a library of over 35,000 small molecules. Five chemical series of inhibitors were initially identified from the screen, from which series 1 and 2 were selected for chemical optimization. Indicative of their mechanism of action, enzyme inhibition by these compounds was found to be sensitive to both the ATP concentration and substitution of the amino acid residue present at the "gatekeeper" position at the ATP-binding site of the enzyme. Medicinal chemistry efforts led to a series of PfCDPK1 inhibitors with 50% inhibitory concentrations (IC50s) below 10 nM against PfCDPK1 in a biochemical assay and 50% effective concentrations (EC50s) less than 100 nM for inhibition of parasite growth in vitro. Potent inhibition was combined with acceptable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and equipotent inhibition of Plasmodium vivax CDPK1. However, we were unable to correlate biochemical inhibition with parasite growth inhibition for this series overall. Inhibition of Plasmodium berghei CDPK1 correlated well with PfCDPK1 inhibition, enabling progression of a set of compounds to in vivo evaluation in the P. berghei rodent model for malaria. These chemical series have potential for further development as inhibitors of CDPK1

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study

Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks