共查询到20条相似文献，搜索用时 15 毫秒
1.
This paper develops methods of analysis for active extension clinical trials. Under this design, patients are randomized to treatment or placebo for a period of time (period 1), and then all patients receive treatment for an additional period of time (period 2). We assume a continuous outcome is measured at baseline and at the end of the two consecutive periods. If only period 1 data is available, classic estimators of the treatment effect include the change score, analysis of covariance, and maximum likelihood (ML). We show how to extend these estimators by incorporating period 2 data which we refer to as the period 2 estimators. Under the assumption that the mean responses for treatment and placebo arms are the same at the end of period 2, the new estimators are unbiased and more efficient than estimators that ignore period 2 data. If this assumption is not met, the period 2 tests may be more powerful than period 1 tests, but the estimators are biased downward (upward) if the treatment effect during period 2 is larger (smaller) in treatment arm than the placebo arm. In general, the proposed period 2 procedure can provide an efficient way to supplement but not supplant the usual period 1 analysis. 相似文献
2.
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. 相似文献
3.
Many clinical trials involve the collection of data on the time to occurrence of the same type of multiple events within sample units, in which ordering of events is arbitrary and times are usually correlated. To design a clinical trial with this type of clustered survival times as the primary endpoint, estimating the number of subjects (sampling units) required for a given power to detect a specified treatment difference is an important issue. In this paper we derive a sample size formula for clustered survival data via Lee, Wei and Amato's marginal model. It can be easily used to plan a clinical trial in which clustered survival times are of primary interest. Simulation studies demonstrate that the formula works very well. We also discuss and compare cluster survival time design and single survival time design (for example, time to the first event) in different scenarios. 相似文献
4.
Hoover DR 《Statistics in medicine》2002,21(10):1351-1364
Behaviour modification is often delivered to teaching subgroups. For example, experimental and control smoking cessation programmes may be given to 15 classes (subgroups) with 10 (otherwise independent) individuals. We present general statistical tests and power estimates to compare continuous outcomes from two interventions in settings where the magnitude of teaching subgroup heterogeneity, number of subgroups and subgroup size can differ between intervention arms. An application is made to data from a trial to reduce disease-transmitting sexual behaviour. The statistical impact of teaching subgroup heterogeneity effect increases as the (a) number of participants in a subgroup increases, and (b) ratio of 'averaged experimental and control subgroup effect variance' to study subject variance increases. If plausible levels of subgroup teaching effect heterogeneity are ignored, the true sizes of tests with nominal 0.05 two-sided type I errors range from 0.055 to 0.47, while when planning studies, estimated sample sizes are only 11.1-95.2 per cent of the true requirements. 相似文献
5.
Carter B 《Statistics in medicine》2010,29(29):2984-2993
Cluster randomized controlled trials are increasingly used to evaluate medical interventions. Research has found that cluster size variability leads to a reduction in the overall effective sample size. Although reporting standards of cluster trials have started to evolve, a far greater degree of transparency is needed to ensure that robust evidence is presented. The use of the numbers of patients recruited to summarize recruitment rate should be avoided in favour of an improved metric that illustrates cumulative power and accounts for cluster variability. Data from four trials is included to show the link between cluster size variability and imbalance. Furthermore, using simulations it is demonstrated that by randomising using a two block randomization strategy and weighting the second by cluster size recruitment, chance imbalance can be minimized. 相似文献
6.
7.
8.
It is often anticipated in a longitudinal cluster randomized clinical trial (cluster‐RCT) that the course of outcome over time will diverge between intervention arms. In these situations, testing the significance of a local intervention effect at the end of the trial may be more clinically relevant than evaluating overall mean differences between treatment groups. In this paper, we present a closed‐form power function for detecting this local intervention effect based on maximum likelihood estimates from a mixed‐effects linear regression model for three‐level continuous data. Sample size requirements for the number of units at each data level are derived from the power function. The power function and the corresponding sample size requirements are verified by a simulation study. Importantly, it is shown that sample size requirements computed with the proposed power function are smaller than that required when testing group mean difference using data only at the end of trial and ignoring the course of outcome over the entire study period. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
9.
Cluster randomized trials (CRTs) are increasingly used to evaluate the effectiveness of health‐care interventions. A key feature of CRTs is that the observations on individuals within clusters are correlated as a result of between‐cluster variability. Sample size formulae exist which account for such correlations, but they make different assumptions regarding the between‐cluster variability in the intervention arm of a trial, resulting in different sample size estimates. We explore the relationship for binary outcome data between two common measures of between‐cluster variability: k, the coefficient of variation and ρ, the intracluster correlation coefficient. We then assess how the assumptions of constant k or ρ across treatment arms correspond to different assumptions about intervention effects. We assess implications for sample size estimation and present a simple solution to the problems outlined. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
10.
A major methodological reason to use cluster randomization is to avoid the contamination that would arise in an individually randomized design. However, when patient recruitment cannot be completed before randomization of clusters, the non-blindedness of recruiters and patients may cause selection bias, while in the control clusters, it may slow recruitment due to patient or recruiter preferences for the intervention. As a compromise, pseudo cluster randomization has been proposed. Because no insight is available into the relative performance of methods to analyse data obtained from this design, we compared the type I and II error rates of mixed models, generalized estimating equations (GEE) and a paired t-test to those of the estimator originally proposed in this design. The bias in the point estimate and its standard error were also incorporated into this comparison. Furthermore, we evaluated the effect of the weighting scheme and the accuracy of the sample size formula that have been described previously. Power levels of the originally proposed estimator and the unweighted mixed models were in agreement with the sample size formula, but the power of paired t-test fell short. GEE produced too large type I errors, unless the number of clusters was large (>30-40 per arm). The use of the weighting scheme generally enhanced the power, but at the cost of increasing the type I error in mixed models and GEE. We recommend unweighted mixed models as the best compromise between feasibility and power to analyse data from a pseudo cluster randomized trial. 相似文献
11.
In designing a longitudinal cluster randomized clinical trial (cluster‐RCT), the interventions are randomly assigned to clusters such as clinics. Subjects within the same clinic will receive the identical intervention. Each will be assessed repeatedly over the course of the study. A mixed‐effects linear regression model can be applied in a cluster‐RCT with three‐level data to test the hypothesis that the intervention groups differ in the course of outcome over time. Using a test statistic based on maximum likelihood estimates, we derived closed‐form formulae for statistical power to detect the intervention by time interaction and the sample size requirements for each level. Importantly, the sample size does not depend on correlations among second‐level data units and the statistical power function depends on the number of second‐ and third‐level data units through their product. A simulation study confirmed that theoretical power estimates based on the derived formulae are nearly identical to empirical estimates. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
12.
The sample size required for a cluster randomized trial depends on the magnitude of the intracluster correlation coefficient (ICC). The usual sample size calculation makes no allowance for the fact that the ICC is not known precisely in advance. We develop methods which allow for the uncertainty in a previously observed ICC, using a variety of distributional assumptions. Distributions for the power are derived, reflecting this uncertainty. Further, the observed ICC in a future study will not equal its true value, and we consider the impact of this on power. We implement calculations within a Bayesian simulation approach, and provide one simplification that can be performed using simple simulation within spreadsheet software. In our examples, recognizing the uncertainty in a previous ICC estimate decreases expected power, especially when the power calculated naively from the ICC estimate is high. To protect against the possibility of low power, sample sizes may need to be very substantially increased. Recognizing the variability in the future observed ICC has little effect if prior uncertainty has already been taken into account. We show how our method can be extended to the case in which multiple prior ICC estimates are available. The methods presented in this paper can be used by applied researchers to protect against loss of power, or to choose a design which reduces the impact of uncertainty in the ICC. 相似文献
13.
In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because, in many typical power analysis settings, assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of the alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect. This approach is explored analytically and by simulations in the present paper. We consider several alternative variance expressions and compare their performance to that of the traditional null variance expression. Theoretical analysis and simulations demonstrate that, whereas the null variance expression performs well in many nonnull settings, it can also be very inaccurate, substantially underestimating, or overestimating the true variance in a wide range of realistic scenarios, particularly those where the numbers of treated and control subjects are very different and the true hazard ratio is not close to one. The alternative variance expressions have much better theoretical properties, confirmed in simulations. The most accurate of these expressions has a relatively simple form. It is the sum of inverse expected event counts under treatment and under control scaled up by a variance inflation factor. 相似文献
14.
Koko Asakura Toshimitsu Hamasaki Tomoyuki Sugimoto Kenichi Hayashi Scott R. Evans Takashi Sozu 《Statistics in medicine》2014,33(17):2897-2913
We discuss sample size determination in group‐sequential designs with two endpoints as co‐primary. We derive the power and sample size within two decision‐making frameworks. One is to claim the test intervention's benefit relative to control when superiority is achieved for the two endpoints at the same interim timepoint of the trial. The other is when superiority is achieved for the two endpoints at any interim timepoint, not necessarily simultaneously. We evaluate the behaviors of sample size and power with varying design elements and provide a real example to illustrate the proposed sample size methods. In addition, we discuss sample size recalculation based on observed data and evaluate the impact on the power and Type I error rate. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
15.
In this paper we study sample size calculation methods for the asymptotic Wilcoxon-Mann-Whitney test for data with or without ties. The existing methods are applicable either to data with ties or to data without ties but not to both cases. While the existing methods developed for data without ties perform well, the methods developed for data with ties have limitations in that they are either applicable to proportional odds alternatives or have computational difficulties. We propose a new method which has a closed-form formula and therefore is very easy to calculate. In addition, the new method can be applied to both data with or without ties. Simulations have demonstrated that the new sample size formula performs very well as the corresponding actual powers are close to the nominal powers. 相似文献
16.
W James Gauderman 《Statistics in medicine》2002,21(1):35-50
Consideration of gene-environment (GxE) interaction is becoming increasingly important in the design of new epidemiologic studies. We present a method for computing required sample size or power to detect GxE interaction in the context of three specific designs: the standard matched case-control; the case-sibling, and the case-parent designs. The method is based on computation of the expected value of the likelihood ratio test statistic, assuming that the data will be analysed using conditional logistic regression. Comparisons of required sample sizes indicate that the family-based designs (case-sibling and case-parent) generally require fewer matched sets than the case-control design to achieve the same power for detecting a GxE interaction. The case-sibling design is most efficient when studying a dominant gene, while the case-parent design is preferred for a recessive gene. Methods are also presented for computing sample size when matched sets are obtained from a stratified population, for example, when the population consists of multiple ethnic groups. A software program that implements the method is freely available, and may be downloaded from the website http://hydra.usc.edu/gxe. 相似文献
17.
Pharmacogenetic trials investigate the effect of genotype on treatment response. When there are two or more treatment groups and two or more genetic groups, investigation of gene-treatment interactions is of key interest. However, calculation of the power to detect such interactions is complicated because this depends not only on the treatment effect size within each genetic group, but also on the number of genetic groups, the size of each genetic group, and the type of genetic effect that is both present and tested for. The scale chosen to measure the magnitude of an interaction can also be problematic, especially for the binary case. Elston et al. proposed a test for detecting the presence of gene-treatment interactions for binary responses, and gave appropriate power calculations. This paper shows how the same approach can also be used for normally distributed responses. We also propose a method for analysing and performing sample size calculations based on a generalized linear model (GLM) approach. The power of the Elston et al. and GLM approaches are compared for the binary and normal case using several illustrative examples. While more sensitive to errors in model specification than the Elston et al. approach, the GLM approach is much more flexible and in many cases more powerful. 相似文献
18.
In clinical trials it is often desirable to test for non-inferiority and for superiority simultaneously. For such a situation a two-stage adaptive procedure may be advantageous to a conventional single-stage procedure because a two-stage adaptive procedure allows the design of stage II, including the main study objective and sample size, to depend on the outcome of stage I. We propose a framework for designing two-stage adaptive procedures with a possible switch of the primary study objectives at the end of stage I between non-inferiority and superiority. The framework permits control of the type I error rate and specification of the unconditional powers and maximum sample size for each of non-inferiority and superiority objectives. The actions at the end of stage I are predetermined as functions of the stage I observations, thus making specification of the unconditional powers possible. Based on the results at the end of stage I, the primary objective for stage II is chosen, and sample sizes and critical values for stage II are determined. 相似文献
19.
A trial of a new therapy is to be compared to results from a previous trial of patients treated with a standard therapy. For a given sample size for the trial of the new therapy, we desire the power, against a specific alternative hypothesis, for the hypothesis test of the null hypothesis that the therapies are equivalent. Alternatively, the sample size required for the trial of the new therapy is needed for a target power. We explain why a popular method for doing these calculations is wrong, and discuss alternative methods in the context of normal outcomes, binary outcomes, and time-to-event outcomes. 相似文献
20.
Girardeau, Ravaud and Donner in 2008 presented a formula for sample size calculations for cluster randomised crossover trials, when the intracluster correlation coefficient, interperiod correlation coefficient and mean cluster size are specified in advance. However, in many randomised trials, the number of clusters is constrained in some way, but the mean cluster size is not. We present a version of the Girardeau formula for sample size calculations for cluster randomised crossover trials when the number of clusters is fixed. Formulae are given for the minimum number of clusters, the maximum cluster size and the relationship between the correlation coefficients when there are constraints on both the number of clusters and the cluster size. Our version of the formula may aid the efficient planning and design of cluster randomised crossover trials. 相似文献