Sample size re-estimation in cluster randomization trials

Cluster randomization trials in which families are the unit of allocation are commonly adopted for the evaluation of disease prevention interventions. Sample size estimation for cluster randomization trials depends on parameters that quantify the variability within and between clusters and the variability in cluster size. Accurate advance estimates of these nuisance parameters may be difficult to obtain and misspecification may lead to an underpowered study. Since families are typically recruited over time, we propose using a portion of the data to estimate the nuisance parameters and to re-estimate sample size based on the estimates. This extends the standard internal pilot study methods to the setting of cluster randomization trials. The effect of this design on the power, significance level and sample size is analysed via simulation and is shown to provide a flexible and practical approach to cluster randomization trials.     

Sample size re-estimation in group-sequential response-adaptive clinical trials

In clinical trials where the variances of the response variables are unknown, in accurate estimates of these can affect the type II error rate considerably. More accurate estimates of the variances may be obtained by taking a look at the data available part way through the trial and re-calculating the required sample size based on these new estimates. The main impetus for sample size re-estimation came from a two-stage procedure developed by Stein in 1945 and the literature is now replete with variations on this approach. In this paper, existing sample size re-estimation methods for both fixed sample and sequential clinical trial models will be reviewed. These will then be extended for use in group-sequential response-adaptive designs. In particular, a test for a recently developed group-sequential response-adaptive design, which compares two treatments with immediate normally distributed responses and unknown variances, is presented based on a modified version of Stein's test. The principal modifications involve updating the required sample size at each interim analysis and calculating the test statistic based on the current estimates of the variances. Hence, all the available information is used at each stage. Simulation is used to assess to what extent the updating of the required sample size at each interim analysis in the new test helps to attain the nominal error rates. The test is compared to modified versions of a simple test and a Stein-type group sequential t-test studied in the recent literature. These tests calculate the required sample sizes based on less accurate estimates of the variances. The type I error rate is close to the nominal value and the power is more accurately maintained in the new test.     

Sample size re-estimation for clinical trials with longitudinal negative binomial counts including time trends

In some diseases, such as multiple sclerosis, lesion counts obtained from magnetic resonance imaging (MRI) are used as markers of disease progression. This leads to longitudinal, and typically overdispersed, count data outcomes in clinical trials. Models for such data invariably include a number of nuisance parameters, which can be difficult to specify at the planning stage, leading to considerable uncertainty in sample size specification. Consequently, blinded sample size re-estimation procedures are used, allowing for an adjustment of the sample size within an ongoing trial by estimating relevant nuisance parameters at an interim point, without compromising trial integrity. To date, the methods available for re-estimation have required an assumption that the mean count is time-constant within patients. We propose a new modeling approach that maintains the advantages of established procedures but allows for general underlying and treatment-specific time trends in the mean response. A simulation study is conducted to assess the effectiveness of blinded sample size re-estimation methods over fixed designs. Sample sizes attained through blinded sample size re-estimation procedures are shown to maintain the desired study power without inflating the Type I error rate and the procedure is demonstrated on MRI data from a recent study in multiple sclerosis.     

Sample size considerations in observational health care quality studies

A common objective in health care quality studies involves measuring and comparing the quality of care delivered to cohorts of patients by different health care providers. The data used for inference involve observations on units grouped within clusters, such as patients treated within hospitals. Unlike cluster randomization trials where often clusters are randomized to interventions to learn about individuals, the target of inference in health quality studies is the cluster. Furthermore, randomization is often not performed and the resulting biases may invalidate standard tests. In this paper, we discuss approaches to sample size determination in the design of observational health quality studies when the outcome is binary. Methods for calculating sample size using marginal models are briefly reviewed, but the focus is on hierarchical binomial models. Sample size in unbalanced clusters and stratified designs are characterized. We draw upon the experiences that have arisen from a study funded by the Agency for Healthcare Research and Quality involving assessment of quality of care for patients with cardiovascular disease. If researchers are interested in comparing clusters, hierarchical models are preferred.     

Snoring: recent developments

Despite limited evidence validating its efficacy, surgery to overcome snoring is commonly undertaken. This article looks at the development of snoring surgery to present day, highlighting its limitations and outlining current methods being used to target the surgery more effectively.     

Sample size considerations and predictive performance of multinomial logistic prediction models

Multinomial Logistic Regression (MLR) has been advocated for developing clinical prediction models that distinguish between three or more unordered outcomes. We present a full-factorial simulation study to examine the predictive performance of MLR models in relation to the relative size of outcome categories, number of predictors and the number of events per variable. It is shown that MLR estimated by Maximum Likelihood yields overfitted prediction models in small to medium sized data. In most cases, the calibration and overall predictive performance of the multinomial prediction model is improved by using penalized MLR. Our simulation study also highlights the importance of events per variable in the multinomial context as well as the total sample size. As expected, our study demonstrates the need for optimism correction of the predictive performance measures when developing the multinomial logistic prediction model. We recommend the use of penalized MLR when prediction models are developed in small data sets or in medium sized data sets with a small total sample size (ie, when the sizes of the outcome categories are balanced). Finally, we present a case study in which we illustrate the development and validation of penalized and unpenalized multinomial prediction models for predicting malignancy of ovarian cancer.     

Sample size considerations for stratified cluster randomization design with binary outcomes and varying cluster size

Stratified cluster randomization trials (CRTs) have been frequently employed in clinical and healthcare research. Comparing with simple randomized CRTs, stratified CRTs reduce the imbalance of baseline prognostic factors among different intervention groups. Due to the popularity, there has been a growing interest in methodological development on sample size estimation and power analysis for stratified CRTs; however, existing work mostly assumes equal cluster size within each stratum and uses multilevel models. Clusters are often naturally formed with random sizes in CRTs. With varying cluster size, commonly used ad hoc approaches ignore the variability in cluster size, which may underestimate (overestimate) the required number of clusters for each group per stratum and lead to underpowered (overpowered) clinical trials. We propose closed-form sample size formulas for estimating the required total number of subjects and for estimating the number of clusters for each group per stratum, based on Cochran-Mantel-Haenszel statistic for stratified cluster randomization design with binary outcomes, accounting for both clustering and varying cluster size. We investigate the impact of various design parameters on the relative change in the required number of clusters for each group per stratum due to varying cluster size. Simulation studies are conducted to evaluate the finite-sample performance of the proposed sample size method. A real application example of a pragmatic stratified CRT of a triad of chronic kidney disease, diabetes, and hypertension is presented for illustration.     

Sample size considerations for studies comparing survival curves using historical controls

Formulas are derived for determination of the number of patients needed in a prospective comparison of survival curves, when the control group patients have already been followed for some period. Although an explicit formula for the required sample size is not available, the computing is straightforward, and tables of examples are presented. Situations are described when one might need to allocate some new patients to the control group, rather than exclusively to the experimental group.     

Stress-induced insulin resistance: recent developments

PURPOSE OF REVIEW: Interest in stress-induced insulin resistance has increased during the past 5 years. Relevant clinical and mechanistic investigations during the past year will be reviewed. RECENT FINDINGS: Recent trials of intensive insulin therapy in intensive care units have brought attention to a high incidence of hypoglycemic episodes with such treatment. The clinical relevance of such hypoglycemia has been shown to be minor, however. Furthermore, animal and in-vitro work further supports the finding that glucose control, rather than glycemia-independent effects of insulin, is the primary mechanism of action of intensive insulin therapy. In elective surgery, cohort studies show an association between intraoperative hyperglycemia and postoperative morbidity. Beneficial effects of preoperative oral carbohydrate treatment on immunocompetence and cardiac contractility have been demonstrated. Laparoscopic segmental colectomy was associated with considerably attenuated derangements in glucose metabolism compared with conventional, open surgery. SUMMARY: Better methods of insulin dosing and administration and glucose monitoring are warranted to further minimize the risks of intensive insulin therapy. In elective surgery, perioperative measures such as preoperative oral carbohydrate treatment and laparoscopic techniques attenuate metabolic and other physiological derangements and such methods should be integrated into perioperative care protocols to minimize morbidity and enhance recovery.     

Sample size considerations for the evaluation of prognostic factors in survival analysis

When the role of a new prognostic factor is investigated, careful planning of an appropriate study is required. This includes an assessment of the power of the study in terms of sample sizes. An adequate analysis of the independent prognostic effect of a new factor has to be adjusted for the existing standard factors. With survival time as endpoint this will usually be done with the Cox proportional hazards model. Sample size and power formulae in survival analysis have been developed by Schoenfeld for randomized treatment comparisons. In the analysis of prognostic factors the covariates included are expected to be correlated with the factor of primary interest. In this situation, the existing sample size and power formulae are not valid and may not be applied. In this paper, Schoenfeld's formula is first extended to the situation where a correlated factor is included in the analysis. The validity of the resulting approximate asymptotic formula is investigated for its asymptotic behaviour by numerical integration and for its finite behaviour by simulation. Second, an approximate formula for sample size and power is provided to detect an interaction between the interesting and a second correlated factor. This extends the formula for independent effects. Finally, the approach is illustrated by an example on the prognostic impact of DNA ploidy and other factors in advanced ovarian cancer.     

Sample size considerations for studies of intervention efficacy in the occupational setting

OBJECTIVE: Due to a shared environment and similarities among workers within a worksite, the strongest analytical design to evaluate the efficacy of an intervention to reduce occupational health or safety hazards is to randomly assign worksites, not workers, to the intervention and comparison conditions. Statistical methods are well described for estimating the sample size when the unit of assignment is a group but these methods have not been applied in the evaluation of occupational health and safety interventions. We review and apply the statistical methods for group-randomized trials in planning a study to evaluate the effectiveness of technical/behavioral interventions to reduce wood dust levels among small woodworking businesses. METHODS: We conducted a pilot study in five small woodworking businesses to estimate variance components between and within worksites and between and within workers. In each worksite, 8 h time-weighted dust concentrations were obtained for each production employee on between two and five occasions. With these data, we estimated the parameters necessary to calculate the percent change in dust concentrations that we could detect (alpha = 0.05, power = 80%) for a range of worksites per condition, workers per worksite and repeat measurements per worker. RESULTS: The mean wood dust concentration across woodworking businesses was 4.53 mg/m3. The measure of similarity among workers within a woodworking business was large (intraclass correlation = 0.5086). Repeated measurements within a worker were weakly correlated (r = 0.1927) while repeated measurements within a worksite were strongly correlated (r = 0.8925). The dominant factor in the sample size calculation was the number of worksites per condition, with the number of workers per worksite playing a lesser role. We also observed that increasing the number of repeat measurements per person had little benefit given the low within-worker correlation in our data. We found that 30 worksites per condition and 10 workers per worksite would give us 80% power to detect a reduction of approximately 30% in wood dust levels (alpha = 0.05). CONCLUSIONS: Our results demonstrate the application of the group-randomized trials methodology to evaluate interventions to reduce occupational hazards. The methodology is widely applicable and not limited to the context of wood dust reduction.     

Quality in managed care: developments and considerations

With the rapid movement of both individuals and groups away from fee-for-service health care into managed care, concerns have been expressed appropriately that the quality of care may be affected adversely. Over the past several years, a number of developments have taken place to respond to these concerns. This quality movement in managed care has not been without some issues and considerations, however. This article first describes the National Committee for Quality Assurance (NCQA) and the prominent role it has played in this movement. Next, quality improvement study design is addressed in the context of assuring quality, controlling costs, and achieving NCQA accreditation. The effect that capitation, as a payment strategy for providers, has on data quality is then described. Fourth, the value of partnering is explored. Finally, the newest version of NCQA's performance measurement template is discussed: the Health Plan Employer Data and Information Set, version 3.0.     

Qualitative data analysis: conceptual and practical considerations

Qualitative inquiry requires that collected data is organised in a meaningful way, and this is referred to as data analysis. Through analytic processes, researchers turn what can be voluminous data into understandable and insightful analysis. This paper sets out the different approaches that qualitative researchers can use to make sense of their data including thematic analysis, narrative analysis, discourse analysis and semiotic analysis and discusses the ways that qualitative researchers can analyse their data. I first discuss salient issues in performing qualitative data analysis, and then proceed to provide some suggestions on different methods of data analysis in qualitative research. Finally, I provide some discussion on the use of computer‐assisted data analysis.     

Computer-based ambulatory information systems: recent developments

This article updates the author's earlier review of some of the major computer-based ambulatory information systems and the literature evaluating their costs, benefits, effect on quality of care, and physician acceptance. The evidence suggests that computer-based information systems can increase access to clinical information, improve physician performance, enhance quality of care, and facilitate outcomes research review. In addition to presenting health information networks and clinical decision support systems such as reminder systems, drug ordering systems, and medical care management systems, the article describes applications of telemedicine and Web-based systems. It also discusses barriers to the widespread use of computer-based ambulatory information systems.     

Portage in the UK: recent developments

BACKGROUND: Portage provides home-based, early intervention and support to families who have a young child with additional needs. Working in the context of the Every Child Matters agenda, the National Portage Association (NPA) aims to develop inclusive, high-quality provision. This paper reviews their recent work, presents a summary of the findings of a national survey of Portage and discusses their implications. METHODS: All Portage services known to the NPA and local authorities in England were asked to complete a questionnaire and forward another to families using early years' services to evaluate the support they receive. RESULTS: The survey identified the extent and gaps in Portage provision and the level of unmet need. Families who contributed said they valued Portage because it helped their child and provided support for the whole family. DISCUSSION OF FINDINGS: The survey identified 31 local authorities in England, where Portage is not available. Geographical gaps were most notable in the North East and West Midlands regions. Despite a small increase in the extent of Portage provision, it is estimated that services only meet the needs of a small proportion of families that are eligible. Current work may help overcome geographical gaps through supporting the development of high-quality Portage services but, in endeavouring to meet the needs of families waiting for Portage, it is important not to diminish the quality of support already provided. Portage aims to provide flexible support for families to respond to their individual needs. Evaluating its effectiveness is problematic because of its complex nature but this survey provided an opportunity for families to share their views at a national level. CONCLUSIONS: Continued development work can help Portage provision become more accessible and one of the options routinely available to families who have a young child with additional needs.