Search

Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post‐randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent or with multiple agents. These complications have led to ambiguity in choice of models but there have been no systematic efforts to identify appropriate analytic models for these study designs. To address this gap, we undertook a simulation study to examine the performance of candidate analytic models in the presence of complex clustering arising from multiple membership, single membership, and single agent settings, in both nested and crossed designs and for a continuous outcome. With nested designs, substantial type I error rate inflation was observed when analytic models did not account for multiple membership and when analytic model weights characterizing the association with multiple agents did not match the data generating mechanism. Conversely, analytic models for crossed designs generally maintained nominal type I error rates unless there was notable imbalance in the number of participants that interact with each agent. [ABSTRACT FROM AUTHOR]

Individually randomized group treatment (IRGT) trials, in which the clustering of outcome is induced by group‐based treatment delivery, are increasingly popular in public health research. IRGT trials frequently incorporate longitudinal measurements, of which the proper sample size calculations should account for correlation structures reflecting both the treatment‐induced clustering and repeated outcome measurements. Given the relatively sparse literature on designing longitudinal IRGT trials, we propose sample size procedures for continuous and binary outcomes based on the generalized estimating equations approach, employing the block exchangeable correlation structures with different correlation parameters for the treatment arm and for the control arm, and surveying five marginal mean models with different assumptions of time effect: no‐time constant treatment effect, linear‐time constant treatment effect, categorical‐time constant treatment effect, linear time by treatment interaction, and categorical time by treatment interaction. Closed‐form sample size formulas are derived for continuous outcomes, which depends on the eigenvalues of the correlation matrices; detailed numerical sample size procedures are proposed for binary outcomes. Through simulations, we demonstrate that the empirical power agrees well with the predicted power, for as few as eight groups formed in the treatment arm, when data are analyzed using the matrix‐adjusted estimating equations for the correlation parameters with a bias‐corrected sandwich variance estimator. [ABSTRACT FROM AUTHOR]

Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll‐out. While a complete design requires clusters to collect data in all periods, resource and patient‐centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance‐stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric. [ABSTRACT FROM AUTHOR]

Objective: This manuscript describes the Day‐in‐the‐Life (DIL) method for assessing child caregiving activities, its implementation, and findings regarding family members' roles and associations with maternal depression symptoms. Background: Infant caregiving activities are most commonly performed by the mother, although there is increasing acknowledgement of others' contribution. Few methods exist to measure the diverse caregiving activities that mothers and others perform. Method: Method development occurred within the Bachpan Cohort Study in rural Pakistan (N = 1154 maternal–child dyads) when the child was 3 months old. The DIL was designed as a semi‐structured interview in which the mother describes her child's day from their perspective. Regression analyses were then used to explore the correlation between the DIL and depression symptoms, using the Patient Health Questionnaire‐9 (PHQ‐9) measure. Results: The DIL method was easy to administer and displayed excellent interrater agreement. The findings indicated that instrumental caregiving was mostly provided by the mother alone, others in the household tended to contribute more to infant social interactions, and there was more support from others when the mother was less able to provide care (e.g., when ill). Depression symptoms were higher among women who experienced less contribution from family members when the mother was less able to provide care. Conclusions: The DIL can be deployed to measure infant caregiving activities and associations with maternal mental health. Implications: This method is promising for researchers interested in disentangling the contribution of multiple family members toward child caregiving and its impacts on maternal and child health. [ABSTRACT FROM AUTHOR]

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small‐sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right‐censored, time‐to‐event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small‐sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias‐corrected sandwich variance estimators when clustered time‐to‐event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias‐corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small‐sample properties. Our results indicate that the optimal choice of bias‐corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real‐world CRT where the conclusion about intervention effectiveness differs depending on the use of small‐sample bias corrections. The proposed sandwich variance estimators are implemented in an R package CoxBcv. [ABSTRACT FROM AUTHOR]

A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization overcomes this issue by restricting the allocation to a subset of randomization schemes where sufficient overall covariate balance across comparison arms is achieved. However, for multi‐arm cRCTs, several design and analysis issues pertaining to constrained randomization have not been fully investigated. Motivated by an ongoing multi‐arm cRCT, we elaborate the method of constrained randomization and provide a comprehensive evaluation of the statistical properties of model‐based and randomization‐based tests under both simple and constrained randomization designs in multi‐arm cRCTs, with varying combinations of design and analysis‐based covariate adjustment strategies. In particular, as randomization‐based tests have not been extensively studied in multi‐arm cRCTs, we additionally develop most‐powerful randomization tests under the linear mixed model framework for our comparisons. Our results indicate that under constrained randomization, both model‐based and randomization‐based analyses could gain power while preserving nominal type I error rate, given proper analysis‐based adjustment for the baseline covariates. Randomization‐based analyses, however, are more robust against violations of distributional assumptions. The choice of balance metrics and candidate set sizes and their implications on the testing of the pairwise and global hypotheses are also discussed. Finally, we caution against the design and analysis of multi‐arm cRCTs with an extremely small number of clusters, due to insufficient degrees of freedom and the tendency to obtain an overly restricted randomization space. [ABSTRACT FROM AUTHOR]

In this article, we develop methods for sample size and power calculations in four‐level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs involving four levels are becoming popular in healthcare research, where the effects are measured, for example, from evaluations (level 1) within participants (level 2) in divisions (level 3) that are nested in clusters (level 4). In such multilevel CRTs, we consider three types of intraclass correlations between different evaluations to account for such clustering: that of the same participant, that of different participants from the same division, and that of different participants from different divisions in the same cluster. Assuming arbitrary link and variance functions, with the proposed correlation structure as the true correlation structure, closed‐form sample size formulas for randomization carried out at any level (including individually randomized trials within a four‐level clustered structure) are derived based on the generalized estimating equations approach using the model‐based variance and using the sandwich variance with an independence working correlation matrix. We demonstrate that empirical power corresponds well with that predicted by the proposed method for as few as eight clusters, when data are analyzed using the matrix‐adjusted estimating equations for the correlation parameters with a bias‐corrected sandwich variance estimator, under both balanced and unbalanced designs. [ABSTRACT FROM AUTHOR]

The stepped wedge (SW) design is a type of unidirectional crossover design where cluster units switch from control to intervention condition at different prespecified time points. While a convention in study planning is to assume the cluster‐period sizes are identical, SW cluster randomized trials (SW‐CRTs) involving repeated cross‐sectional designs frequently have unequal cluster‐period sizes, which can impact the efficiency of the treatment effect estimator. In this paper, we provide a comprehensive investigation of the efficiency impact of unequal cluster sizes for generalized estimating equation analyses of SW‐CRTs, with a focus on binary outcomes as in the Washington State Expedited Partner Therapy trial. Several major distinctions between our work and existing work include the following: (i) we consider multilevel correlation structures in marginal models with binary outcomes; (ii) we study the implications of both the between‐cluster and within‐cluster imbalances in sizes; and (iii) we provide a comparison between the independence working correlation versus the true working correlation and detail the consequences of ignoring correlation estimation in SW‐CRTs with unequal cluster sizes. We conclude that the working independence assumption can lead to substantial efficiency loss and a large sample size regardless of cluster‐period size variability in SW‐CRTs, and recommend accounting for correlations in the analysis. To improve study planning, we additionally provide a computationally efficient search algorithm to estimate the sample size in SW‐CRTs accounting for unequal cluster‐period sizes, and conclude by illustrating the proposed approach in the context of the Washington State study. [ABSTRACT FROM AUTHOR]

In malaria‐endemic countries about a quarter of test‐negative individuals take antimalarials (artemisinin‐based combination therapies [ACTs]). ACT overuse depletes scarce resources for subsidies and contributes to parasite resistance. As part of an experiment in Kenya that provided subsidies for rapid diagnostic test and/or for ACTs conditionally on being positive, we studied the association between beliefs on malaria status (prior and posterior the intervention) and decisions to get tested and to purchase ACTs. We find that prior beliefs do not explain the decision of getting tested (conditional on the price) and nonadherence to a negative test. However, test‐negative individuals who purchase ACTs report higher posterior beliefs than those who do not, consistent with a framework in which the formers revise beliefs upward, while the latters do not change or revise downward. We also do not find evidence that prior beliefs on ACT effectiveness and trust in test results play any major role in explaining testing or treatment behavior. Further research is needed to improve adherence to malaria‐negative test results. [ABSTRACT FROM AUTHOR]

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel cluster randomized design, which often involves a limited number of clusters. In recent years, the cluster randomized crossover design has been increasingly used to evaluate the effectiveness of health care policy or programs, and the interest often lies in quantifying the population-averaged intervention effect. In this paper, we consider the two-treatment two-period crossover design, and develop sample size procedures for continuous and binary outcomes corresponding to a population-averaged model estimated by generalized estimating equations, accounting for both within-period and interperiod correlations. In particular, we show that the required sample size depends on the correlation parameters through an eigenvalue of the within-cluster correlation matrix for continuous outcomes and through two distinct eigenvalues of the correlation matrix for binary outcomes. We demonstrate that the empirical power corresponds well with the predicted power by the proposed formulae for as few as eight clusters, when outcomes are analyzed using the matrix-adjusted estimating equations for the correlation parameters concurrently with a suitable bias-corrected sandwich variance estimator. [ABSTRACT FROM AUTHOR]

Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed. [ABSTRACT FROM AUTHOR]

Here, we present a method for incidence estimation of a curable, non-recurring disease when data from a single cross-sectional survey are used together with population-level mortality rates and an assumption of differential mortality of diseased versus non-diseased individuals. The motivating example is cataract, and the VISION2020 goal to eliminate avoidable blindness globally by 2020. Reliable estimates of current and future cataract disease burden are required to predict how many surgeries would need to be performed to meet the VISION2020 goals. However, incidence estimates, needed to derive future burden, are not as easily available, due to the cost of conducting cohort studies. Disease is defined at the person-level in accordance with the WHO person-level definition of blindness. An extension of the standard time homogeneous illness-death model to a four-state model is described, which allows the disease to be cured, whereby surgery is performed on at least one diseased eye. Incidence is estimated, and the four-state model is used to predict disease burden assuming different surgical strategies whilst accounting for the competing risk of death. The method is applied to data from approximately 10 000 people from a survey of visual impairment in Nigeria. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of Tropical Medicine & International Health is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Risk ratios and odds ratios are measures of the relative association of a predictor and a binary outcome variable. Note: an odds ratio of 1.5 is a 50% increase in odds, a ratio of 2 is a 100% increase, so an odds ratio of 8 is a 700% increase. [Extracted from the article]

Loss to follow up can lead to biased estimates of treatment effect, even if it is the same across arms. Baseline characteristics of participants who are followed up versus participants not followed up should be summarised by treatment arm. [Extracted from the article]

The NNT is related to the risk difference (RD) by NNT = 1/|RD| (where the RD is expressed as a proportion, e.g. 0.118 in our example). The association of 17OHPC and neonatal morbidity in a randomised controlled trial assuming perfect compliance and all singleton births (n = 576), summarised using an absolute effect (RD: risk difference) and number needed to treat (NNT). [Extracted from the article]

An intention-to-treat analysis retains a balance of participant characteristics between the treatment arms. In the unusual situation when the proportion undergoing cerclage in the control arm exceeds that in the intervention arm, treatment would be estimated to be harmful. When arms are balanced on these risk factors, we can be more confident that the difference seen in PTB risk between arms is due to the treatment and not to the participant characteristics. [Extracted from the article]