Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research.

OBJECTIVES: Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the "conditional random slope" (CRS) model, based on a linear-mixed effects model that incorporates a baseline-time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. METHODS: In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RESULTS: RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation (r = 0.65-0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38-0.78). Estimates of the velocity-stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. CONCLUSION: The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses.

Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries.

Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.

The international Perinatal Outcomes in the Pandemic (iPOP) study: protocol.

Preterm birth is the leading cause of infant death worldwide, but the causes of preterm birth are largely unknown. During the early COVID-19 lockdowns, dramatic reductions in preterm birth were reported; however, these trends may be offset by increases in stillbirth rates. It is important to study these trends globally as the pandemic continues, and to understand the underlying cause(s). Lockdowns have dramatically impacted maternal workload, access to healthcare, hygiene practices, and air pollution - all of which could impact perinatal outcomes and might affect pregnant women differently in different regions of the world. In the international Perinatal Outcomes in the Pandemic (iPOP) Study, we will seize the unique opportunity offered by the COVID-19 pandemic to answer urgent questions about perinatal health. In the first two study phases, we will use population-based aggregate data and standardized outcome definitions to: 1) Determine rates of preterm birth, low birth weight, and stillbirth and describe changes during lockdowns; and assess if these changes are consistent globally, or differ by region and income setting, 2) Determine if the magnitude of changes in adverse perinatal outcomes during lockdown are modified by regional differences in COVID-19 infection rates, lockdown stringency, adherence to lockdown measures, air quality, or other social and economic markers, obtained from publicly available datasets. We will undertake an interrupted time series analysis covering births from January 2015 through July 2020. The iPOP Study will involve at least 121 researchers in 37 countries, including obstetricians, neonatologists, epidemiologists, public health researchers, environmental scientists, and policymakers. We will leverage the most disruptive and widespread "natural experiment" of our lifetime to make rapid discoveries about preterm birth. Whether the COVID-19 pandemic is worsening or unexpectedly improving perinatal outcomes, our research will provide critical new information to shape prenatal care strategies throughout (and well beyond) the pandemic.

Power calculations for cluster randomized trials (CRTs) with right-truncated Poisson-distributed outcomes: a motivating example from a malaria vector control trial.

BACKGROUND: Cluster randomized trials (CRTs) are increasingly used to study the efficacy of interventions targeted at the population level. Formulae exist to calculate sample sizes for CRTs, but they assume that the domain of the outcomes being considered covers the full range of values of the considered distribution. This assumption is frequently incorrect in epidemiological trials in which counts of infection episodes are right-truncated due to practical constraints on the number of times a person can be tested. METHODS: Motivated by a malaria vector control trial with right-truncated Poisson-distributed outcomes, we investigated the effect of right-truncation on power using Monte Carlo simulations. RESULTS: The results demonstrate that the adverse impact of right-truncation is directly proportional to the magnitude of the event rate, λ, with calculations of power being overestimated in instances where right-truncation was not accounted for. The severity of the adverse impact of right-truncation on power was more pronounced when the number of clusters was ≤30 but decreased the further the right-truncation point was from zero. CONCLUSIONS: Potential right-truncation should always be accounted for in the calculation of sample size requirements at the study design stage.

Pharmacokinetics and pharmacodynamics of imatinib in a phase I trial with chronic myeloid leukemia patients.

PURPOSE: To evaluate the basic pharmacokinetic (PK) characteristics of imatinib mesylate and assess the relationship between the PK and pharmacodynamic (PD) properties of the drug. PATIENTS AND METHODS: The PK and PD properties of imatinib were investigated during a phase I trial that included 64 adult patients with Philadelphia chromosome-positive leukemias. Patients received imatinib orally once or twice daily. PK parameters of imatinib, derived from the plasma concentration-time curves, were determined. PD response, defined as the WBC after 1 month of treatment with imatinib, was used to develop an efficacy model. A maximum inhibition-effect model was used to describe the relationship between reduction in WBC and drug exposure parameters. RESULTS: Imatinib exposure was dose proportional after oral administration for the dose range of 25 to 1,000 mg. There was a 1.5- to three-fold drug accumulation after repeated once-daily dosing. Mean plasma trough concentration was 0.57 microg/mL (approximately 1 micromol/L) 24 hours after administration of 350 mg of imatinib at steady-state, which exceeds the 50% inhibitory concentration required to inhibit proliferation of Bcr-Abl-positive leukemic cells. Analysis of PK/PD relationships indicates that the initial hematologic response depends on the administered dose for patients with chronic myeloid leukemia. CONCLUSION: Drug exposure (area under the concentration-time curve) is dose proportional for the dose range of 25 to 1,000 mg, and there is a 1.5- to three-fold drug accumulation at steady-state after once-daily dosing. Analysis of the relationship between PD (WBC reduction) and PK parameters at steady-state indicates that a dose of 400 mg or greater is required for maximal PD effect.

Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology.

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well.

Bayesian predictive power for interim adaptation in seamless phase II/III trials where the endpoint is survival up to some specified timepoint.

Integration of a phase II and a phase III clinical trial into a single confirmatory study aims to shorten the development time without compromising the chance of success for a development program. These seamless phase II/III trials involve complex adaptations at the interim analysis, such as treatment selection, sample size reassessment, and stopping for futility. Bayesian methods can support these interim adaptations, and make this decision process more transparent. Use of a frequentist combination test for the final evaluation ensures that the type I error is controlled regardless of the adaptation rule employed at the interim analysis. In this paper, an adaptive seamless phase II/III trial design is proposed for studies where the endpoint is survival up to some specified timepoint and where Bayesian predictive power (PP) guides interim adaptations. For the evaluation of PP at the interim analysis, the event time is modelled as a piecewise exponential distribution, with informative priors for the hazard rates. As an illustrative example, regimen selection at interim in a four-arm trial with an active control is considered, where both non-inferiority and superiority to the control arm are tested. Frequentist properties of the adaptation criterion based on Bayesian PP are assessed by simulations.