Sample size and power calculations with correlated binary data

Assessment of facial video-based detection of atrial fibrillation across human complexion

Background

Early self-detection of atrial fibrillation (AF) can help delay and/or prevent significant associated complications, including embolic stroke and heart failure. We developed a facial video technology, videoplethysmography (VPG), to detect AF based on the analysis of facial pulsatile signals.

Objective

The purpose of this study was to evaluate the accuracy of a video-based technology to detect AF on a smartphone and to test the performance of the technology in AF patients across the whole spectrum of skin complexion and under various recording conditions.

Methods

The performance of video-based monitoring depends on a set of factors such as the angle and the distance between the camera and the patient's face, the strength of illumination, and the patient's skin tone. We conducted a clinical study involving 60 subjects with a confirmed diagnosis of AF. A continuous electrocardiogram was used as the gold standard for cardiac rhythm annotation. The VPG technology was fine-tuned on a smartphone for the first 15 subjects. Validation recordings were then done using 7053 measurements collected from the remaining 45 subjects.

Results

The VPG technology detected the presence of AF using the video camera from a common smartphone with sensitivity and specificity ≥90%. The ambient level of illumination needs to be ≥100 lux for the technology to deliver consistent performance across all skin tones.

Conclusion

We demonstrated that facial video-based detection of AF provides accurate outpatient cardiac monitoring including high pulse rate accuracy and medical-grade performance for AF detection.

Power considerations for generalized estimating equations analyses of four-level cluster randomized trials

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.

New chemotherapy regimens and biomarkers for Chagas disease: the rationale and design of the TESEO study, an open-label, randomised, prospective, phase-2 clinical trial in the Plurinational State of Bolivia

INTRODUCTION

Chagas disease (CD) affects ~7 million people worldwide. Benznidazole (BZN) and nifurtimox (NFX) are the only approved drugs for CD chemotherapy. Although both drugs are highly effective in acute and paediatric infections, their efficacy in adults with chronic CD (CCD) is lower and variable. Moreover, the high incidence of adverse events (AEs) with both drugs has hampered their widespread use. Trials in CCD adults showed that quantitative PCR (qPCR) assays remain negative for 12 months after standard-of-care (SoC) BZN treatment in ~80% patients. BZN pharmacokinetic data and the nonsynchronous nature of the proliferative mammal-dwelling parasite stage suggested that a lower BZN/NFX dosing frequency, combined with standard or extended treatment duration, might have the same or better efficacy than either drug SoC, with fewer AEs.

METHODS AND ANALYSIS

New ThErapies and Biomarkers for ChagaS infEctiOn (TESEO) is an open-label, randomised, prospective, phase-2 clinical trial, with six treatment arms (75 patients/arm, 450 patients). Primary objectives are to compare the safety and efficacy of two new proposed chemotherapy regimens of BZN and NFX in adults with CCD with the current SoC for BZN and NFX, evaluated by qPCR and biomarkers for 36 months posttreatment and correlated with CD conventional serology. Recruitment of patients was initiated on 18 December 2019 and on 20 May 2021, 450 patients (study goal) were randomised among the six treatment arms. The treatment phase was finalised on 18 August 2021. Secondary objectives include evaluation of population pharmacokinetics of both drugs in all treatment arms, the incidence of AEs, and parasite genotyping.

ETHICS AND DISSEMINATION

The TESEO study was approved by the National Institutes of Health (NIH), U.S. Food and Drug Administration (FDA), federal regulatory agency of the Plurinational State of Bolivia and the Ethics Committees of the participating institutions. The results will be disseminated via publications in peer-reviewed journals, conferences and reports to the NIH, FDA and participating institutions.

TRIAL REGISTRATION NUMBER

NCT03981523.

Power calculation for analyses of cross-sectional stepped-wedge cluster randomized trials with binary outcomes via generalized estimating equations

Power calculation for stepped-wedge cluster randomized trials (SW-CRTs) presents unique challenges, beyond those of standard parallel cluster randomized trials, due to the need to consider temporal within cluster correlations and background period effects. To date, power calculation methods specific to SW-CRTs have primarily been developed under a linear model. When the outcome is binary, the use of a linear model corresponds to assessing a prevalence difference; yet trial analysis often employs a nonlinear link function. We propose power calculation methods for cross-sectional SW-CRTs under a logistic model fitted by generalized estimating equations. Firstly, under an exchangeable correlation structure, we show the power based on a logistic model is lower than that from assuming a linear model in the absence of period effects. We then evaluate the impact of background prevalence changes over time on power. To allow the correlation among outcomes in the same cluster to change over time and with treatment status, we generalize the methods to more complex correlation structures. Our simulation studies demonstrate that the proposed power calculation methods perform well with the model-based variance under the true correlation structure and reveal that a working independence structure can result in substantial efficiency loss, while a working exchangeable structure performs well even when the underlying correlation structure deviates from exchangeable. An extension to our methods accounts for variable cluster sizes and reveals that unequal cluster sizes have a modest impact on power. We illustrate the approaches by application to a quality of care improvement trial for acute coronary syndrome.

Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm

BACKGROUND/AIMS

Generalized estimating equations are commonly used to fit logistic regression models to clustered binary data from cluster randomized trials. A commonly used correlation structure assumes that the intracluster correlation coefficient does not vary by treatment arm or other covariates, but the consequences of this assumption are understudied. We aim to evaluate the effect of allowing variation of the intracluster correlation coefficient by treatment or other covariates on the efficiency of analysis and show how to account for such variation in sample size calculations.

METHODS

We develop formulae for the asymptotic variance of the estimated difference in outcome between treatment arms obtained when the true exchangeable correlation structure depends on the treatment arm and the working correlation structure used in the generalized estimating equations analysis is: (i) correctly specified, (ii) independent, or (iii) exchangeable with no dependence on treatment arm. These formulae require a known distribution of cluster sizes; we also develop simplifications for the case when cluster sizes do not vary and approximations that can be used when the first two moments of the cluster size distribution are known. We then extend the results to settings with adjustment for a second binary cluster-level covariate. We provide formulae to calculate the required sample size for cluster randomized trials using these variances.

RESULTS

We show that the asymptotic variance of the estimated difference in outcome between treatment arms using these three working correlation structures is the same if all clusters have the same size, and this asymptotic variance is approximately the same when intracluster correlation coefficient values are small. We illustrate these results using data from a recent cluster randomized trial for infectious disease prevention in which the clusters are groups of households and modest in size (mean 9.6 individuals), with intracluster correlation coefficient values of 0.078 in the control arm and 0.057 in an intervention arm. In this application, we found a negligible difference between the variances calculated using structures (i) and (iii) and only a small increase (typically <5%) for the independent correlation structure (ii), and hence minimal effect on power or sample size requirements. The impact may be larger in other applications if there is greater variation in the ICC between treatment arms or with an additional covariate.

CONCLUSION

The common approach of fitting generalized estimating equations with an exchangeable working correlation structure with a common intracluster correlation coefficient across arms likely does not substantially reduce the power or efficiency of the analysis in the setting of a large number of small or modest-sized clusters, even if the intracluster correlation coefficient varies by treatment arm. Our formulae, however, allow formal evaluation of this and may identify situations in which variation in intracluster correlation coefficient by treatment arm or another binary covariate may have a more substantial impact on power and hence sample size requirements.

Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes

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.

Performance of the Beta-Binomial Model for Clustered Binary Responses: Comparison with Generalized Estimating Equations

This study examined performance of the beta-binomial model in comparison with GEE using clustered binary responses resulting in non-normal outcomes. Monte Carlo simulations were performed under varying intracluster correlations and sample sizes. The results showed that the beta-binomial model performed better for small sample, while GEE performed well under large sample.

Sample size estimation for modified Poisson analysis of cluster randomized trials with a binary outcome

The modified Poisson regression coupled with a robust sandwich variance has become a viable alternative to log-binomial regression for estimating the marginal relative risk in cluster randomized trials. However, a corresponding sample size formula for relative risk regression via the modified Poisson model is currently not available for cluster randomized trials. Through analytical derivations, we show that there is no loss of asymptotic efficiency for estimating the marginal relative risk via the modified Poisson regression relative to the log-binomial regression. This finding holds both under the independence working correlation and under the exchangeable working correlation provided a simple modification is used to obtain the consistent intraclass correlation coefficient estimate. Therefore, the sample size formulas developed for log-binomial regression naturally apply to the modified Poisson regression in cluster randomized trials. We further extend the sample size formulas to accommodate variable cluster sizes. An extensive Monte Carlo simulation study is carried out to validate the proposed formulas. We find that the proposed formulas have satisfactory performance across a range of cluster size variability, as long as suitable finite-sample corrections are applied to the sandwich variance estimator and the number of clusters is at least 10. Our findings also suggest that the sample size estimate under the exchangeable working correlation is more robust to cluster size variability, and recommend the use of an exchangeable working correlation over an independence working correlation for both design and analysis. The proposed sample size formulas are illustrated using the Stop Colorectal Cancer (STOP CRC) trial.

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Cluster randomized trials (CRTs) are widely used in epidemiological and public health studies assessing population-level effect of group-based interventions. One important application of CRTs is the control of vector-borne disease, such as malaria. However, a particular challenge for designing these trials is that the primary outcome involves counts of episodes that are subject to right truncation. While sample size formulas have been developed for CRTs with clustered counts, they are not directly applicable when the counts are right truncated. To address this limitation, we discuss two marginal modeling approaches for the analysis of CRTs with truncated counts and develop two corresponding closed-form sample size formulas to facilitate the design of such trials. The proposed sample size formulas allow investigators to explore the power under a large number of scenarios without computationally intensive simulations. The proposed formulas are validated in extensive simulations. We further explore the implication of right truncation on power and apply the proposed formulas to illustrate the power calculation for a malaria control CRT where the primary outcome is subject to right truncation.

Power and sample size for GEE analysis of incomplete paired outcomes in 2 × 2 crossover trials

The 2 × 2 crossover trial uses subjects as their own control to reduce the intersubject variability in the treatment comparison, and typically requires fewer subjects than a parallel design. The generalized estimating equations (GEE) methodology has been commonly used to analyze incomplete discrete outcomes from crossover trials. We propose a unified approach to the power and sample size determination for the Wald Z-test and t-test from GEE analysis of paired binary, ordinal and count outcomes in crossover trials. The proposed method allows misspecification of the variance and correlation of the outcomes, missing outcomes, and adjustment for the period effect. We demonstrate that misspecification of the working variance and correlation functions leads to no or minimal efficiency loss in GEE analysis of paired outcomes. In general, GEE requires the assumption of missing completely at random. For bivariate binary outcomes, we show by simulation that the GEE estimate is asymptotically unbiased or only minimally biased, and the proposed sample size method is suitable under missing at random (MAR) if the working correlation is correctly specified. The performance of the proposed method is illustrated with several numerical examples. Adaption of the method to other paired outcomes is discussed.

A Randomized Controlled Trial of the Effect of Music During Cesarean Sections and the Early Postpartum Period on Breastfeeding Rates

Objective: The objective of this research was to study the role of music listening by mothers during a cesarean section and the postpartum period to achieve exclusive breastfeeding in the first 6 months. Methods and Study Design: This was a prospective, observational, randomized controlled trial study. A total of 185 singleton pregnant women, in at least 37 weeks of gestation, who were appointed for elective cesarean sections, were recruited. They were randomized into three groups, including pregnant women who did not listen to music (Group 1), listened to music during cesarean section (Group 2), and listened to music during cesarean section and the postpartum room for the first 2 days (Group 3). The breastfeeding results of all three groups were followed up at 7 days, 14 days, and then at months 1, 2, 3, and 6. Results: Success in exclusive breastfeeding among Groups 1, 2, and 3 and Groups 1 and 2 + 3 was not different in every lactating period (7 days-6 months). From subgroup analysis, mothers who listened to music in a private ward had more success in exclusive breastfeeding than those in a common ward. Mothers who listened to music and had an income of <20,000 baht, an educational level lower than university, planned the pregnancy, had their first pregnancy, and stayed in a private ward had more successful exclusive breastfeeding in a 6-month period than those mothers who did not listen to music, and the difference was statistically significant. Conclusions: Music listening by mothers during a cesarean section and in the postpartum ward did not enhance exclusive breastfeeding during the first 6 months of the postpartum period. However, from subgroup analysis, mothers who listened to music in a private ward had more success in exclusive breastfeeding than those in a common ward. Thai Clinical Trials Registry number was TCTR20180712001.

An evaluation of quadratic inference functions for estimating intervention effects in cluster randomized trials

Cluster randomized trials (CRTs) usually randomize groups of individuals to interventions, and outcomes are typically measured at the individual level. Marginal intervention effects are frequently of interest in CRTs due to their population-averaged interpretations. Such effects are estimated using generalized estimating equations (GEE), or a recent alternative called the quadratic inference function (QIF). However, the performance of QIF relative to GEE have not been extensively evaluated in the CRT context, especially when the marginal mean model includes additional covariates. Motivated by the HALI trial, we conduct simulation studies to compare the finite-sample operating characteristics of QIF and GEE. We demonstrate that QIF and GEE are equivalent under some conditions. When the marginal mean model includes individual-level covariates, QIF shows an efficiency improvement over GEE with overall larger power, but its test size may be more liberal than GEE and GEE achieves better coverage than QIF. The test size inflation may not by fully addressed from using finite-sample bias corrections. The estimates of QIF tend to be closer to GEE in the HALI data, although the former presents a small standard error. Overall, we confirm that the QIF approach generally has potentially better efficiency than GEE in our simulation studies but might be more cautiously used as a viable approach for the analysis of CRTs. More research is needed, however, to address the finite-sample bias in the variance estimation of the QIF to better control its test size.

Sample size calculation in three-level cluster randomized trials using generalized estimating equation models

Three-level cluster randomized trials (CRTs) are increasingly used in implementation science, where 2fold-nested-correlated data arise. For example, interventions are randomly assigned to practices, and providers within the same practice who provide care to participants are trained with the assigned intervention. Teerenstra et al proposed a nested exchangeable correlation structure that accounts for two levels of clustering within the generalized estimating equations (GEE) approach. In this article, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in three-level CRTs. Given the nested exchangeable correlation structure, we derive the asymptotic variances of the estimator of the treatment effect for different types of outcomes. When the number of clusters is small, researchers have proposed bias-corrected sandwich estimators to improve performance in two-level CRTs. We extend the variances of two bias-corrected sandwich estimators to three-level CRTs. The equal provider and practice sizes were assumed to calculate number of practices for simplicity. However, they are not guaranteed in practice. Relative efficiency (RE) is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal provider and practice sizes. The expressions of REs are obtained from both asymptotic variance estimation and bias-corrected sandwich estimators. Their performances are evaluated for different scenarios of provider and practice size distributions through simulation studies. Finally, a percentage increase in the number of practices is proposed due to efficiency loss from unequal provider and/or practice sizes.

Sample size estimation for stratified individual and cluster randomized trials with binary outcomes

Individual randomized trials (IRTs) and cluster randomized trials (CRTs) with binary outcomes arise in a variety of settings and are often analyzed by logistic regression (fitted using generalized estimating equations for CRTs). The effect of stratification on the required sample size is less well understood for trials with binary outcomes than for continuous outcomes. We propose easy-to-use methods for sample size estimation for stratified IRTs and CRTs and demonstrate the use of these methods for a tuberculosis prevention CRT currently being planned. For both IRTs and CRTs, we also identify the ratio of the sample size for a stratified trial vs a comparably powered unstratified trial, allowing investigators to evaluate how stratification will affect the required sample size when planning a trial. For CRTs, these can be used when the investigator has estimates of the within-stratum intracluster correlation coefficients (ICCs) or by assuming a common within-stratum ICC. Using these methods, we describe scenarios where stratification may have a practically important impact on the required sample size. We find that in the two-stratum case, for both IRTs and for CRTs with very small cluster sizes, there are unlikely to be plausible scenarios in which an important sample size reduction is achieved when the overall probability of a subject experiencing the event of interest is low. When the probability of events is not small, or when cluster sizes are large, however, there are scenarios where practically important reductions in sample size result from stratification.

The impact of varying cluster size in cross-sectional stepped-wedge cluster randomised trials

Background

Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve.

Methods

Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation – a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency.

Results

Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk.

Conclusion

The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0760-6) contains supplementary material, which is available to authorized users.

Relaxation for Critically ill Patient Outcomes and Stress-coping Enhancement (REPOSE): a protocol for a pilot randomised trial of an integrative intervention to improve critically ill patients' delirium and related outcomes

INTRODUCTION

Delirium is a common complication of critical illness, associated with negative patient outcomes. Preventive or therapeutic interventions are mostly ineffective. Although relaxation-inducing approaches may benefit critically ill patients, no well-designed studies target delirium prevention as a primary outcome. The objective of this study is to assess feasibility and treatment effect estimates of a multimodal integrative intervention incorporating relaxation, guided imagery and moderate pressure touch massage for prevention of critical illness delirium and for related outcomes.

METHODS AND ANALYSIS

Randomised, controlled, single-blinded trial with two parallel groups (1:1 allocation: intervention and standard care) and stratified randomisation (age (18-64 years and ≥65 years) and presence of trauma) with blocking, involving 104 patients with Intensive Care Delirium Screening Checklist (ICDSC): 0-3 recruited from two academic intensive care units (ICUs). Intervention group participants receive the intervention in addition to standard care for up to five consecutive days (or until transfer/discharge); control group participants receive standard care and a sham intervention. We will assess predefined feasibility outcomes, that is, recruitment rates and protocol adherence. The primary clinical outcome is incidence of delirium (ICDSC ≥4). Secondary outcomes include pain scores, inflammatory biomarkers, heart rate variability, stress and quality of life (6 weeks and 4 months) post-ICU discharge. Feasibility measures will be analysed descriptively, and outcomes will be analysed longitudinally. Estimates of effects will be calculated.

ETHICS AND DISSEMINATION

The study has received approval from the Human Research Ethics Board, University of Alberta. Results will inform the design of a future multicentre trial.

TRIAL REGISTRATION NUMBER

NCT02905812; Pre-results.

Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models

There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the "working correlation structure" is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs-exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster.

Sample size determination for a matched-pairs study with incomplete data using exact approach

This research was motivated by a clinical trial design for a cognitive study. The pilot study was a matched-pairs design where some data are missing, specifically the missing data coming at the end of the study. Existing approaches to determine sample size are all based on asymptotic approaches (e.g., the generalized estimating equation (GEE) approach). When the sample size in a clinical trial is small to medium, these asymptotic approaches may not be appropriate for use due to the unsatisfactory Type I and II error rates. For this reason, we consider the exact unconditional approach to compute the sample size for a matched-pairs study with incomplete data. Recommendations are made for each possible missingness pattern by comparing the exact sample sizes based on three commonly used test statistics, with the existing sample size calculation based on the GEE approach. An example from a real surgeon-reviewers study is used to illustrate the application of the exact sample size calculation in study designs.

Sample size calculation for before-after experiments with partially overlapping cohorts

We investigate sample size calculation for before-after experiments where the outcome of interest is binary and the enrolled subjects contribute a mixed type of data: some subjects contribute complete pairs of before- and after-intervention outcomes, while some subjects contribute incomplete data (before-intervention only or after-intervention only). We use the GEE approach to derive a closed-form sample size formula by treating the incomplete observations as missing data in a generalized linear model. The impacts of various designing factors are appropriately accounted for in the sample size formula, including intervention effect, baseline response rate, within-subject correlation, and distribution of missing values in the before- and after-intervention periods. We illustrate sample size estimation using a real application example. We conduct simulation studies to demonstrate that the proposed sample size maintains the nominal power and type I error under a wide spectrum of trial configurations.

Partnering around cancer clinical trials (PACCT): study protocol for a randomized trial of a patient and physician communication intervention to increase minority accrual to prostate cancer clinical trials

BACKGROUND

Cancer clinical trials are essential for testing new treatments and represent state-of-the-art cancer treatment, but only a small percentage of patients ever enroll in a trial. Under-enrollment is an even greater problem among minorities, particularly African Americans, representing a racial/ethnic disparity in cancer care. One understudied cause is patient-physician communication, which is often of poor quality during clinical interactions between African-American patients and non-African-American physicians. Partnering Around Cancer Clinical Trials (PACCT) involves a transdisciplinary theoretical model proposing that patient and physician individual attitudes and beliefs and their interpersonal communication during racially discordant clinical interactions influence outcomes related to patients' decisions to participate in a trial. The overall goal of the study is to test a multilevel intervention designed to increase rates at which African-American and White men with prostate cancer make an informed decision to participate in a clinical trial.

METHODS/DESIGN

Data collection will occur at two NCI-designated comprehensive cancer centers. Participants include physicians who treat men with prostate cancer and their African-American and White patients who are potentially eligible for a clinical trial. The study uses two distinct research designs to evaluate the effects of two behavioral interventions, one focused on patients and the other on physicians. The primary goal is to increase the number of patients who decide to enroll in a trial; secondary goals include increasing rates of physician trial offers, improving the quality of patient-physician communication during video recorded clinical interactions in which trials may be discussed, improving patients' understanding of trials offered, and increasing the number of patients who actually enroll. Aims are to 1) determine the independent and combined effects of the two interventions on outcomes; 2) compare the effects of the interventions on African-American versus White men; and 3) examine the extent to which patient-physician communication mediates the effect of the interventions on the outcomes.

DISCUSSION

PACCT has the potential to identify ways to increase clinical trial rates in a diverse patient population. The research can also improve access to high quality clinical care for African American men bearing the disproportionate burden of disparities in prostate and other cancers.

TRIAL REGISTRATION

Clinical Trials.gov registration number: NCT02906241 (September 8, 2016).

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review

OBJECTIVES

To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials.

SETTING

Any, not limited to healthcare settings.

PARTICIPANTS

Any taking part in an SW-CRT published up to March 2016.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes.

RESULTS

Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22-0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed.

CONCLUSIONS

Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed.

An imbalance in cluster sizes does not lead to notable loss of power in cross-sectional, stepped-wedge cluster randomised trials with a continuous outcome

BACKGROUND

The current methodology for sample size calculations for stepped-wedge cluster randomised trials (SW-CRTs) is based on the assumption of equal cluster sizes. However, as is often the case in cluster randomised trials (CRTs), the clusters in SW-CRTs are likely to vary in size, which in other designs of CRT leads to a reduction in power. The effect of an imbalance in cluster size on the power of SW-CRTs has not previously been reported, nor what an appropriate adjustment to the sample size calculation should be to allow for any imbalance. We aimed to assess the impact of an imbalance in cluster size on the power of a cross-sectional SW-CRT and recommend a method for calculating the sample size of a SW-CRT when there is an imbalance in cluster size.

METHODS

The effect of varying degrees of imbalance in cluster size on the power of SW-CRTs was investigated using simulations. The sample size was calculated using both the standard method and two proposed adjusted design effects (DEs), based on those suggested for CRTs with unequal cluster sizes. The data were analysed using generalised estimating equations with an exchangeable correlation matrix and robust standard errors.

RESULTS

An imbalance in cluster size was not found to have a notable effect on the power of SW-CRTs. The two proposed adjusted DEs resulted in trials that were generally considerably over-powered.

CONCLUSIONS

We recommend that the standard method of sample size calculation for SW-CRTs be used, provided that the assumptions of the method hold. However, it would be beneficial to investigate, through simulation, what effect the maximum likely amount of inequality in cluster sizes would be on the power of the trial and whether any inflation of the sample size would be required.

Optimal design of longitudinal data analysis using generalized estimating equation models

Longitudinal studies are often applied in biomedical research and clinical trials to evaluate the treatment effect. The association pattern within the subject must be considered in both sample size calculation and the analysis. One of the most important approaches to analyze such a study is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which "working correlation structure" is introduced and the association pattern within the subject depends on a vector of association parameters denoted by ρ. The explicit sample size formulas for two-group comparison in linear and logistic regression models are obtained based on the GEE method by Liu and Liang. For cluster randomized trials (CRTs), researchers proposed the optimal sample sizes at both the cluster and individual level as a function of sampling costs and the intracluster correlation coefficient (ICC). In these approaches, the optimal sample sizes depend strongly on the ICC. However, the ICC is usually unknown for CRTs and multicenter trials. To overcome this shortcoming, Van Breukelen et al. consider a range of possible ICC values identified from literature reviews and present Maximin designs (MMDs) based on relative efficiency (RE) and efficiency under budget and cost constraints. In this paper, the optimal sample size and number of repeated measurements using GEE models with an exchangeable working correlation matrix is proposed under the considerations of fixed budget, where "optimal" refers to maximum power for a given sampling budget. The equations of sample size and number of repeated measurements for a known parameter value ρ are derived and a straightforward algorithm for unknown ρ is developed. Applications in practice are discussed. We also discuss the existence of the optimal design when an AR(1) working correlation matrix is assumed. Our proposed method can be extended under the scenarios when the true and working correlation matrix are different.

Same-Sex Behavior and its Relationship with Sexual and Health-Related Practices Among a Population-Based Sample of Women in Puerto Rico: Implications for Cancer Prevention and Control

This secondary data analysis aimed to estimate the prevalence of same-sex behavior and sexual and health-related practices of a population-based sample (n=560) of women aged 16-64 years in Puerto Rico (PR). Data collection included interviews and biologic samples. Seven percent of the sample had had sex with other women (WSW). Age-adjusted logistic regression models indicated that WSW had higher odds of history of cancer, having ≥ 7 lifetime sexual partners, using sex toys and sharing them, and use of tobacco and illicit drugs. Future research is needed to address the health needs of WSW, including cancer-related risk factors and sexual practices.

A mixed model formulation for designing cluster randomized trials with binary outcomes

Cluster randomized trials (CRTs) are unlike traditional individually randomized trials because observations within the same cluster are positively correlated and the sample size (number of clusters) is relatively small. Although formulae for sample size and power estimates of CRT designs do exist, these formulae rely upon first-order asymptotic approximations for the distribution of the average intervention effect and are inaccurate for CRTs that have a small number of clusters. These formulae also assume that the intracluster correlation (ICC) is the same for each cluster in the CRT. However, for CRTs in which the clusters are classrooms or medical practices, the degree of ICC is often a factor of how many students are in each classroom or how many patients are in each practice. Specifically, smaller clusters are expected to have larger ICC than larger clusters. A weighted sum of the cluster means, D, is the statistic often used to estimate the average intervention effect in a CRT. Therefore, we propose that a saddlepoint approximation is a natural choice to approximate the distributions of the cluster means more precisely than a standard large-sample approximation. We parameterize the ICC for each cluster as a random effect with a predefined prior distribution that is dependent upon the size of each cluster. After integrating over the range of the random effect, we use Monte Carlo methods to generate sample cluster means, which are in turn used to approximate the distribution of D with saddlepoint methods. Through numerical examples and an actual application, we show that our method has accuracy that is equal to or better than that of existing methods. Futhermore, our method accommodates CRTs in which the correlation within cluster is expected to diminish with the cluster size.

Twin Birth Study: 2-year neurodevelopmental follow-up of the randomized trial of planned cesarean or planned vaginal delivery for twin pregnancy

BACKGROUND

The Twin Birth Study randomized women with uncomplicated pregnancies, between 32(0/7)-38(6/7) weeks' gestation where the first twin was in cephalic presentation, to a policy of either a planned cesarean or planned vaginal delivery. The primary analysis showed that planned cesarean delivery did not increase or decrease the risk of fetal/neonatal death or serious neonatal morbidity as compared with planned vaginal delivery.

OBJECTIVE

This study presents the secondary outcome of death or neurodevelopmental delay at 2 years of age.

STUDY DESIGN

A total of 4603 children from the initial cohort of 5565 fetuses/infants (83%) contributed to the outcome of death or neurodevelopmental delay. Surviving children were screened using the Ages and Stages Questionnaire with abnormal scores validated by a clinical neurodevelopmental assessment. The effect of planned cesarean vs planned vaginal delivery on death or neurodevelopmental delay was quantified using a logistic model to control for stratification variables and using generalized estimating equations to account for the nonindependence of twin births.

RESULTS

Baseline maternal, pregnancy, and infant characteristics were similar. Mean age at assessment was 26 months. There was no significant difference in the outcome of death or neurodevelopmental delay: 5.99% in the planned cesarean vs 5.83% in the planned vaginal delivery group (odds ratio, 1.04; 95% confidence interval, 0.77-1.41; P = .79).

CONCLUSION

A policy of planned cesarean delivery provides no benefit to children at 2 years of age compared with a policy of planned vaginal delivery in uncomplicated twin pregnancies between 32(0/7)-38(6/7)weeks' gestation where the first twin is in cephalic presentation.

Long-term effects of lifestyle intervention or metformin on diabetes development and microvascular complications over 15-year follow-up: the Diabetes Prevention Program Outcomes Study

BACKGROUND

Effective prevention is needed to combat the worldwide epidemic of type 2 diabetes. We investigated the long-term extent of beneficial effects of lifestyle intervention and metformin on diabetes prevention, originally shown during the 3-year Diabetes Prevention Program (DPP), and assessed whether these interventions reduced diabetes-associated microvascular complications.

METHODS

The DPP (1996-2001) was a randomised trial comparing an intensive lifestyle intervention or masked metformin with placebo in a cohort selected to be at very high risk of developing diabetes. All participants were offered lifestyle training at the end of the DPP. 2776 (88%) of the surviving DPP cohort were followed up in the DPP Outcomes Study (DPPOS, Sept 1, 2002, to Jan 2, 2014) and analysed by intention to treat on the basis of their original DPP assignment. During DPPOS, the original lifestyle intervention group was offered lifestyle reinforcement semi-annually and the metformin group received unmasked metformin. The primary outcomes were the development of diabetes and the prevalence of microvascular disease. For the assessment of microvascular disease, we used an aggregate microvascular outcome, composed of nephropathy, retinopathy, and neuropathy.

FINDINGS

During a mean follow-up of 15 years, diabetes incidence was reduced by 27% in the lifestyle intervention group (hazard ratio 0·73, 95% CI 0·65-0·83; p<0·0001) and by 18% in the metformin group (0·82, 0·72-0·93; p=0·001), compared with the placebo group, with declining between-group differences over time. At year 15, the cumulative incidences of diabetes were 55% in the lifestyle group, 56% in the metformin group, and 62% in the placebo group. The prevalences at the end of the study of the aggregate microvascular outcome were not significantly different between the treatment groups in the total cohort (placebo 12·4%, 95% CI 11·1-13·8; metformin 13·0%, 11·7-14·5; lifestyle intervention 11·3%, 10·1-12·7). However, in women (n=1887) the lifestyle intervention was associated with a lower prevalence (8·7%, 95% CI 7·4-10·2) than in the placebo (11·0%, 9·6-12·6) and metformin (11·2%, 9·7-12·9) groups, with reductions in the lifestyle intervention group of 21% (p=0·03) compared with placebo and 22% (p=0·02) compared with metformin. Compared with participants who developed diabetes, those who did not develop diabetes had a 28% lower prevalence of microvascular complications (relative risk 0·72, 95% CI 0·63-0·83; p<0·0001).

INTERPRETATION

Lifestyle intervention or metformin significantly reduced diabetes development over 15 years. There were no overall differences in the aggregate microvascular outcome between treatment groups; however, those who did not develop diabetes had a lower prevalence of microvascular complications than those who did develop diabetes. This result supports the importance of diabetes prevention.

FUNDING

National Institute of Diabetes and Digestive and Kidney Diseases.

Methods for sample size determination in cluster randomized trials

BACKGROUND

The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required.

METHODS

We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method.

RESULTS

We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs.

CONCLUSIONS

There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials.

Stability of Cerebral Aneurysms After Stent-Assisted Coil Embolization

BACKGROUND:

The availability of stents has widened the indications of endovascular intervention for cerebral aneurysms.

OBJECTIVE:

To elucidate the effect of stents on radiologic outcomes and to analyze the risk factors for aneurysmal recanalization via propensity score matching.

METHODS:

From the 735 aneurysms treated with coil embolization with stents (n = 187) and without stents (n = 548) between 2009 and 2011, 157 propensity score-matched case pairs were selected. The recanalization rates and relevant risk factors were analyzed. The mean follow-up interval was 24.1 ± 11.3 months (range, 6-48 months) and 22.9 ± 11.4 months (range, 6-56 months) in the stent and nonstent groups, respectively (P = .388).

RESULTS:

The stent group demonstrated lower recanalization rates than the nonstent group during both the 6-month (1.9% vs 10.2%, P = .004) and the final follow-up periods (8.3% vs 18.5%, P = .005). The multivariate analysis identified the following significant factors for recanalization: the use of stents (hazard ratio, 0.40; 95% confidence interval, 0.21-0.76, P = .005), larger aneurysm size (hazard ratio, 1.21; 95% confidence interval, 1.11-1.31, P &lt; .001), and initially incomplete occlusion (hazard ratio, 2.39; 95% confidence interval, 1.28-4.43, P = .006). The incidence of permanent neurological complication tended to be higher in the stent group than in the nonstent group (3.2% vs 0%, P = .063).

CONCLUSION:

In this propensity score-matched analysis, stent implantation reduced the overall recanalization of the coiled cerebral aneurysms. However, the use of stents should be carefully decided upon.

The special case of the 2 × 2 table: asymptotic unconditional McNemar test can be used to estimate sample size even for analysis based on GEE

OBJECTIVES

Aligning the method used to estimate sample size with the planned analytic method ensures the sample size needed to achieve the planned power. When using generalized estimating equations (GEE) to analyze a paired binary primary outcome with no covariates, many use an exact McNemar test to calculate sample size. We reviewed the approaches to sample size estimation for paired binary data and compared the sample size estimates on the same numerical examples.

STUDY DESIGN AND SETTING

We used the hypothesized sample proportions for the 2 × 2 table to calculate the correlation between the marginal proportions to estimate sample size based on GEE. We solved the inside proportions based on the correlation and the marginal proportions to estimate sample size based on exact McNemar, asymptotic unconditional McNemar, and asymptotic conditional McNemar.

RESULTS

The asymptotic unconditional McNemar test is a good approximation of GEE method by Pan. The exact McNemar is too conservative and yields unnecessarily large sample size estimates than all other methods.

CONCLUSION

In the special case of a 2 × 2 table, even when a GEE approach to binary logistic regression is the planned analytic method, the asymptotic unconditional McNemar test can be used to estimate sample size. We do not recommend using an exact McNemar test.

Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments

Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/clustered data analysis in clinical trials or biomedical studies. We provide a systematic review on GEE including basic concepts as well as several recent developments due to practical challenges in real applications. The topics including the selection of “working” correlation structure, sample size and power calculation, and the issue of informative cluster size are covered because these aspects play important roles in GEE utilization and its statistical inference. A brief summary and discussion of potential research interests regarding GEE are provided in the end.

Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes

The sandwich estimator in generalized estimating equations (GEE) approach underestimates the true variance in small samples and consequently results in inflated type I error rates in hypothesis testing. This fact limits the application of the GEE in cluster-randomized trials (CRTs) with few clusters. Under various CRT scenarios with correlated binary outcomes, we evaluate the small sample properties of the GEE Wald tests using bias-corrected sandwich estimators. Our results suggest that the GEE Wald z-test should be avoided in the analyses of CRTs with few clusters even when bias-corrected sandwich estimators are used. With t-distribution approximation, the Kauermann and Carroll (KC)-correction can keep the test size to nominal levels even when the number of clusters is as low as 10 and is robust to the moderate variation of the cluster sizes. However, in cases with large variations in cluster sizes, the Fay and Graubard (FG)-correction should be used instead. Furthermore, we derive a formula to calculate the power and minimum total number of clusters one needs using the t-test and KC-correction for the CRTs with binary outcomes. The power levels as predicted by the proposed formula agree well with the empirical powers from the simulations. The proposed methods are illustrated using real CRT data. We conclude that with appropriate control of type I error rates under small sample sizes, we recommend the use of GEE approach in CRTs with binary outcomes because of fewer assumptions and robustness to the misspecification of the covariance structure.

A randomized controlled trial of capnography during sedation in a pediatric emergency setting

OBJECTIVE

Data suggest that capnography is a more sensitive measure of ventilation than standard modalities and detects respiratory depression before hypoxemia occurs. We sought to determine if adding capnography to standard monitoring during sedation of children increased the frequency of interventions for hypoventilation, and whether these interventions would decrease the frequency of oxygen desaturations.

METHODS

We enrolled 154 children receiving procedural sedation in a pediatric emergency department. All subjects received standard monitoring and capnography, but were randomized to whether staff could view the capnography monitor (intervention) or were blinded to it (controls). Primary outcome were the rate of interventions provided by staff for hypoventilation and the rate of oxygen desaturation less than 95%.

RESULTS

Seventy-seven children were randomized to each group. Forty-five percent had at least 1 episode of hypoventilation. The rate of hypoventilation per minute was significantly higher among controls (7.1% vs 1.0%, P = .008). There were significantly fewer interventions in the intervention group than in the control group (odds ratio, 0.25; 95% confidence interval [CI], 0.13-0.50). Interventions were more likely to occur contemporaneously with hypoventilation in the intervention group (2.26; 95% CI, 1.34-3.81). Interventions not in time with hypoventilation were associated with higher odds of oxygen desaturation less than 95% (odds ratio, 5.31; 95% CI, 2.76-10.22).

CONCLUSION

Hypoventilation is common during sedation of pediatric emergency department patients. This can be difficult to detect by current monitoring methods other than capnography. Providers with access to capnography provided fewer but more timely interventions for hypoventilation. This led to fewer episodes of hypoventilation and of oxygen desaturation.

A randomized trial of planned cesarean or vaginal delivery for twin pregnancy

BACKGROUND

Twin birth is associated with a higher risk of adverse perinatal outcomes than singleton birth. It is unclear whether planned cesarean section results in a lower risk of adverse outcomes than planned vaginal delivery in twin pregnancy.

METHODS

We randomly assigned women between 32 weeks 0 days and 38 weeks 6 days of gestation with twin pregnancy and with the first twin in the cephalic presentation to planned cesarean section or planned vaginal delivery with cesarean only if indicated. Elective delivery was planned between 37 weeks 5 days and 38 weeks 6 days of gestation. The primary outcome was a composite of fetal or neonatal death or serious neonatal morbidity, with the fetus or infant as the unit of analysis for the statistical comparison.

RESULTS

A total of 1398 women (2795 fetuses) were randomly assigned to planned cesarean delivery and 1406 women (2812 fetuses) to planned vaginal delivery. The rate of cesarean delivery was 90.7% in the planned-cesarean-delivery group and 43.8% in the planned-vaginal-delivery group. Women in the planned-cesarean-delivery group delivered earlier than did those in the planned-vaginal-delivery group (mean number of days from randomization to delivery, 12.4 vs. 13.3; P=0.04). There was no significant difference in the composite primary outcome between the planned-cesarean-delivery group and the planned-vaginal-delivery group (2.2% and 1.9%, respectively; odds ratio with planned cesarean delivery, 1.16; 95% confidence interval, 0.77 to 1.74; P=0.49).

CONCLUSIONS

In twin pregnancy between 32 weeks 0 days and 38 weeks 6 days of gestation, with the first twin in the cephalic presentation, planned cesarean delivery did not significantly decrease or increase the risk of fetal or neonatal death or serious neonatal morbidity, as compared with planned vaginal delivery. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00187369; Current Controlled Trials number, ISRCTN74420086.).

Power and Sample Size Calculations for Generalized Estimating Equations via Local Asymptotics

We consider the problem of calculating power and sample size for tests based on generalized estimating equations (GEE), that arise in studies involving clustered or correlated data (e.g., longitudinal studies and sibling studies). Previous approaches approximate the power of such tests using the asymptotic behavior of the test statistics under fixed alternatives. We develop a more accurate approach in which the asymptotic behavior is studied under a sequence of local alternatives that converge to the null hypothesis at root-m rate, where m is the number of clusters. Based on this approach, explicit sample size formulae are derived for Wald and quasi-score test statistics in a variety of GEE settings. Simulation results show that in the important special case of logistic regression with exchangeable correlation structure, previous approaches can inflate the projected sample size (to obtain nominal 90% power using the Wald statistic) by over 10%, whereas the proposed approach provides an accuracy of around 2%.

Dichotomizing a continuous outcome in cluster randomized trials: impact on power

In cluster randomized trials (CRTs), clusters of individuals are randomized rather than the individuals themselves. For such trials, power depends in part on the degree of similarity among responses within a cluster, which is quantified by the intaclass correlation coefficient (ICC). Thus, for a fixed sample size, power decreases with increasing ICC. In reliability studies with two observers, dichotomizing a continuous outcome variable has been shown to reduce the ICC. We checked (by a simulation study) that this property still applies to CRTs, in which cluster sizes are variable and usually greater than in reliability studies and observations (within clusters) are exchangeable. Then, in a CRT, dichotomizing a continuous outcome actually induces two antagonistic effects: decreased power because of loss of information and increased power induced by attenuation of the ICC. Therefore, we aimed to assess the impact of dichotomizing a continuous outcome on power in a CRT. We derived an analytical formula for power based on a generalized estimating equation approach after dichotomizing a continuous outcome. This theoretical result was obtained by considering equal cluster sizes, and we then assessed its accuracy (by a simulation study) in the more realistic situation of varying cluster sizes. We showed that dichotomization is associated with decreased power: attenuation of the ICC does not compensate for the loss of power induced by loss of information. Loss of power is reduced with increased initial continuous-outcome ICC and/or prevalence of success for the dichotomized outcome approaching 50%.

Older adults' self-management of daily symptoms: complementary therapies, self-care, and medical care

OBJECTIVES

To describe older adults' use of complementary therapies, self-care practices, and medical care to treat daily symptoms and to delineate gender, ethnic, age, and education differences.

METHOD

A total of 200 African American and White participants (age 65+) selected using a site-based procedure complete a baseline interview and up to six sets of three daily follow-up interviews at monthly intervals. The percent of older adults using a therapy and the frequency with which therapies are used are considered.

RESULTS

The use of complementary therapies to treat daily symptoms, though important, is substantially less than the use of self-care practices and medical care. Participants differed by age, ethnicity, and education in the use of therapies.

DISCUSSION

In considering the percentage of individuals who use a therapy and the frequency with which therapies are used, this analysis adds a new dimension to understanding how older adults manage daily symptoms. Older adults are selective in their use of health self-management.

Sample size determination for quadratic inference functions in longitudinal design with dichotomous outcomes

Quadratic inference functions (QIF) methodology is an important alternative to the generalized estimating equations (GEE) method in the longitudinal marginal model, as it offers higher estimation efficiency than the GEE when correlation structure is misspecified. The focus of this paper is on sample size determination and power calculation for QIF based on the Wald test in a marginal logistic model with covariates of treatment, time, and treatment-time interaction. We have made three contributions in this paper: (i) we derived formulas of sample size and power for QIF and compared their performance with those given by the GEE; (ii) we proposed an optimal scheme of sample size determination to overcome the difficulty of unknown true correlation matrix in the sense of minimal average risk; and (iii) we studied properties of both QIF and GEE sample size formulas in relation to the number of follow-up visits and found that the QIF gave more robust sample sizes than the GEE. Using numerical examples, we illustrated that without sacrificing statistical power, the QIF design leads to sample size saving and hence lower study cost in comparison with the GEE analysis. We conclude that the QIF analysis is appealing for longitudinal studies.

Improving the diagnostic accuracy of ultrasonography in suspected acute appendicitis by the combined transabdominal and transvaginal approach

Transabdominal ultrasound has a lower diagnostic yield in acute appendicitis than computed tomography (CT) scanning. The addition of transvaginal sonography in women with suspected appendicitis has shown improvement in the efficacy of diagnosis, potentially providing the option of selective CT use and reducing overall investigative cost and surgical delay. Two hundred ninety-two women who underwent combined transabdominal and transvaginal ultrasound for suspected acute appendicitis were evaluated. Patients were divided into two groups; Group 1 including patients with a positive sonographic diagnosis of appendicitis who underwent operation and Group 2 including patients with a negative sonographic diagnosis. Of the 157 women in Group 1, the diagnosis of appendicitis was histologically confirmed in 144 patients with five cases having a normal appendix in whom eight other pathologies were found. Of the 135 women with negative ultrasound examinations, 14 underwent surgery in which four cases of appendicitis were found. The sensitivity of the combined approach was 97.3 per cent, the specificity 91 per cent, the positive predictive value 91.7 per cent, and the negative predictive value 97 per cent. Combined ultrasound has a high predictive value for the diagnosis of appendicitis and may assist in reduction of the use of CT scanning for diagnosis and in the negative appendectomy rate.

Seroepidemiology of viral hepatitis, HIV and herpes simplex type 2 in the household population aged 21-64 years in Puerto Rico

BACKGROUND

Viral hepatitis and sexually transmitted infections (STIs) are key public health problems that pose an enormous risk for disease transmission in the general population. This study estimated, for the first time, prevalence estimates of serologic markers of HCV, HBV, HAV, HIV and HSV-2 in the adult population of Puerto Rico and assessed variations across sociodemographic and behavioral characteristics.

METHODS

A seroepidemiologic survey was employed using a stratified cluster probability sample of households in Puerto Rico. Participants completed a face-to-face interview, a self-administered questionnaire using an ACASI system, and provided blood specimens for antibody testing. Prevalence estimates of viral hepatitis, HIV and HSV-2 were estimated using a logistic regression model weighting for the probability of participation in each household block and the inverse of the probability of selection according to geographic strata, households' blocks, and sex distribution.

RESULTS

A total of 1,654 adults participated in the study. Seroprevalence estimates for HCV (2.3%, 95% CI: 1.3%-4.2%), HBV (3.1%, 95% CI: 2.0%-4.7%), and HSV-2 (22.3%, 95% CI: 18.5%-26.7%) in Puerto Rico are roughly in agreement with estimates obtained in the US population; however, HAV (41.3%, 95% CI: 36.9%-45.8%) and HIV (1.1%, 95% CI: 0.5%-2.3%) seroprevalence estimates in Puerto Rico were higher. The proportion of individuals that were unaware of their serostatus was as follows: 80% for HCV, 98.3% for HBV, 96.4% for HAV, 36.4% for HIV, and 97.8% for HSV-2. Post-stratification estimates of seroprevalence varied significantly by demographic and risk related characteristics.

CONCLUSION

This data underscore the disproportionate impact of some viral infections across selected population subgroups in Puerto Rico. A concerted island-wide effort is needed to strengthen viral hepatitis and STIs prevention and control strategies, support surveillance to monitor chronic infections, and ensure appropriate counseling, testing, and medical management of infected persons. Integration of HCV, HBV, and HSV-2 counseling into HIV existing prevention services and outreach through social networks might represent a valuable approach to reach high-risk individuals.

Marginal and mixed-effects models in the analysis of human papillomavirus natural history data

Human papillomavirus (HPV) natural history has several characteristics that, at least from a statistical perspective, are not often encountered elsewhere in infectious disease and cancer research. There are, for example, multiple HPV types, and infection by each HPV type may be considered separate events. Although concurrent infections are common, the prevalence, incidence, and duration/persistence of each individual HPV can be separately measured. However, repeated measures involving the same subject tend to be correlated. The probability of detecting any given HPV type, for example, is greater among individuals who are currently positive for at least one other HPV type. Serial testing for HPV over time represents a second form of repeated measures. Statistical inferences that fail to take these correlations into account would be invalid. However, methods that do not use all the data would be inefficient. Marginal and mixed-effects models can address these issues but are not frequently used in HPV research. The current study provides an overview of these methods and then uses HPV data from a cohort of HIV-positive women to illustrate how they may be applied, and compare their results. The findings show the greater efficiency of these models compared with standard logistic regression and Cox models. Because mixed-effects models estimate subject-specific associations, they sometimes gave much higher effect estimates than marginal models, which estimate population-averaged associations. Overall, the results show that marginal and mixed-effects models are efficient for studying HPV natural history, but also highlight the importance of understanding how these models differ.

Nonparametric Sample Size Estimation for Sensitivity and Specificity with Multiple Observations per Subject

We propose a sample size calculation approach for the estimation of sensitivity and specificity of diagnostic tests with multiple observations per subjects. Many diagnostic tests such as diagnostic imaging or periodontal tests are characterized by the presence of multiple observations for each subject. The number of observations frequently varies among subjects in diagnostic imaging experiments or periodontal studies. Nonparametric statistical methods for the analysis of clustered binary data have been recently developed by various authors. In this paper, we derive a sample size formula for sensitivity and specificity of diagnostic tests using the sign test while accounting for multiple observations per subjects. Application of the sample size formula for the design of a diagnostic test is discussed. Since the sample size formula is based on large sample theory, simulation studies are conducted to evaluate the finite sample performance of the proposed method. We compare the performance of the proposed sample size formula with that of the parametric sample size formula that assigns equal weight to each observation. Simulation studies show that the proposed sample size formula generally yields empirical powers closer to the nominal level than the parametric method. Simulation studies also show that the number of subjects required increases as the variability in the number of observations per subject increases and the intracluster correlation increases.

Statistical methods in cancer epidemiological studies

In this chapter, we discuss statistical methods for various study designs that are commonly used in epidemiological research and particularly in cancer epidemiological research. After a brief review of basic concepts in epidemiological studies, statistical methods for case-control studies and cohort studies are discussed. Statistical methods for nested case-control and case-cohort studies, which have been increasingly used in cancer epidemiology, also are discussed. This chapter is designed for cancer epidemiologists who understand basic statistical methods for commonly used epidemiological study designs and are able to initiate power and sample size calculations. Therefore, this chapter emphasizes newly developed statistical methods for epidemiological studies as well as study planning.

Sample size and power calculations based on generalized linear mixed models with correlated binary outcomes

The generalized linear mixed model (GLIMMIX) provides a powerful technique to model correlated outcomes with different types of distributions. The model can now be easily implemented with SAS PROC GLIMMIX in version 9.1. For binary outcomes, linearization methods of penalized quasi-likelihood (PQL) or marginal quasi-likelihood (MQL) provide relatively accurate variance estimates for fixed effects. Using GLIMMIX based on these linearization methods, we derived formulas for power and sample size calculations for longitudinal designs with attrition over time. We found that the power and sample size estimates depend on the within-subject correlation and the size of random effects. In this article, we present tables of minimum sample sizes commonly used to test hypotheses for longitudinal studies. A simulation study was used to compare the results. We also provide a Web link to the SAS macro that we developed to compute power and sample sizes for correlated binary outcomes.