scholarly journals Designing efficient randomized trials: power and sample size calculation when using semiparametric efficient estimators

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alejandro Schuler
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Asymptotic Variance ◽  
Sample Size Calculation ◽  
Design Stage ◽  
Type I ◽  
Efficient Estimator ◽  
Power And Control ◽  
Trial Analysis ◽  
Design Power

Abstract Trials enroll a large number of subjects in order to attain power, making them expensive and time-consuming. Sample size calculations are often performed with the assumption of an unadjusted analysis, even if the trial analysis plan specifies a more efficient estimator (e.g. ANCOVA). This leads to conservative estimates of required sample sizes and an opportunity for savings. Here we show that a relatively simple formula can be used to estimate the power of any two-arm, single-timepoint trial analyzed with a semiparametric efficient estimator, regardless of the domain of the outcome or kind of treatment effect (e.g. odds ratio, mean difference). Since an efficient estimator attains the minimum possible asymptotic variance, this allows for the design of trials that are as small as possible while still attaining design power and control of type I error. The required sample size calculation is parsimonious and requires the analyst to provide only a small number of population parameters. We verify in simulation that the large-sample properties of trials designed this way attain their nominal values. Lastly, we demonstrate how to use this formula in the “design” (and subsequent reanalysis) of a real randomized trial and show that fewer subjects are required to attain the same design power when a semiparametric efficient estimator is accounted for at the design stage.

scholarly journals Required sample size for comparing two independent means

2020 ◽  
Vol 6 (2) ◽  
pp. 106-113
Author(s):  
A. M. Grjibovski ◽  
M. A. Gorbatova ◽  
A. N. Narkevich ◽  
K. A. Vinogradov
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Biomedical Literature ◽  
Type I ◽  
Research Practice ◽  
False Null Hypothesis ◽  
Different Levels ◽  
Russian Research

Sample size calculation in a planning phase is still uncommon in Russian research practice. This situation threatens validity of the conclusions and may introduce Type I error when the false null hypothesis is accepted due to lack of statistical power to detect the existing difference between the means. Comparing two means using unpaired Students’ ttests is the most common statistical procedure in the Russian biomedical literature. However, calculations of the minimal required sample size or retrospective calculation of the statistical power were observed only in very few publications. In this paper we demonstrate how to calculate required sample size for comparing means in unpaired samples using WinPepi and Stata software. In addition, we produced tables for minimal required sample size for studies when two means have to be compared and body mass index and blood pressure are the variables of interest. The tables were constructed for unpaired samples for different levels of statistical power and standard deviations obtained from the literature.

An Evaluation of the Quality of Clinical Trials in Anesthesia

2001 ◽  
Vol 95 (5) ◽  
pp. 1068-1073 ◽  
Author(s):  
Hwee Leng Pua ◽  
Jerrold Lerman ◽  
Mark W. Crawford ◽  
James G. Wright
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Adverse Outcomes ◽  
Random Allocation ◽  
Type I ◽  
Trial Methodology ◽  
Eligibility Criteria

Background The authors evaluated the quality of clinical trials published in four anesthesia journals during the 20-yr period from 1981-2000. Methods Trials published in four major anesthesia journals during the periods 1981-1985, 1991-1995, and the first 6 months of 2000 were grouped according to journal and year. Using random number tables, four trials were selected from all of the eligible clinical trials in each journal in each year for the periods 1981-1985 and 1991-1995, and five trials were selected from all of the trials in each journal in the first 6 months of 2000. Methods and results sections from the 160 trials from 1981-1985 and 1991-1995 were randomly ordered and distributed to three of the authors for blinded review of the quality of the study design according to 10 predetermined criteria (weighted equally, maximum score of 10): informed consent and ethics approval, eligibility criteria, sample size calculation, random allocation, method of randomization, blind assessment of outcome, adverse outcomes, statistical analysis, type I error, and type II error. After these trials were evaluated, 20 trials from the first 6 months of 2000 were randomly ordered, distributed, and evaluated as described. Results The mean (+/- SD) analysis scores pooled for the four journals increased from 5.5 +/- 1.4 in 1981-1985 to 7.0 +/- 1.1 in 1991-1995 (P < 0.00001) and to 7.8 +/- 1.5 in 2000. For 7 of the 10 criteria, the percentage of trials from the four journals that fulfilled the criteria increased significantly between 1981-1985 and 1991-1995. During the 20-yr period, the reporting of sample size calculation and method of randomization increased threefold to fourfold, whereas the frequency of type I statistical errors remained unchanged. Conclusion Although the quality of clinical trials in four major anesthesia journals has increased steadily during the past two decades, specific areas of trial methodology require further attention.

Sample size calculation for agreement between two raters with binary endpoints using exact tests

2016 ◽  
Vol 27 (7) ◽  
pp. 2132-2141 ◽  
Author(s):  
Guogen Shan
Keyword(s):  
Sample Size ◽  
Error Control ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Sample Size Determination ◽  
Type I ◽  
Test Statistic ◽  
Testing Procedures ◽  
Alternative Hypotheses ◽  
The One

In an agreement test between two raters with binary endpoints, existing methods for sample size calculation are always based on asymptotic approaches that use limiting distributions of a test statistic under null and alternative hypotheses. These calculated sample sizes may be not reliable due to the unsatisfactory type I error control of asymptotic approaches. We propose a new sample size calculation based on exact approaches which control for the type I error rate. The two exact approaches are considered: one approach based on maximization and the other based on estimation and maximization. We found that the latter approach is generally more powerful than the one based on maximization. Therefore, we present the sample size calculation based on estimation and maximization. A real example from a clinical trial to diagnose low back pain of patients is used to illustrate the two exact testing procedures and sample size determination.

scholarly journals On sample size calculation in testing treatment efficacy in clinical trials

2021 ◽  
Vol 58 (2) ◽  
pp. 133-147
Author(s):  
Rownak Jahan Tamanna ◽  
M. Iftakhar Alam ◽  
Ahmed Hossain ◽  
Md Hasinur Rahaman Khan
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Statistical Significance ◽  
Sample Size Calculation ◽  
Clinical Trial Design ◽  
Sample Size Determination ◽  
Cluster Sampling ◽  
Effective Sample Size ◽  
Type I

Summary Sample size calculation is an integral part of any clinical trial design, and determining the optimal sample size for a study ensures adequate power to detect statistical significance. It is a critical step in designing a planned research protocol, since using too many participants in a study is expensive, exposing more subjects to the procedure. If a study is underpowered, it will be statistically inconclusive and may cause the whole protocol to fail. Amidst the attempt to maximize power and the underlying effort to minimize the budget, the optimization of both has become a significant issue in the determination of sample size for clinical trials in recent decades. Although it is hard to generalize a single method for sample size calculation, this study is an attempt to offer something that might be a basis for finding a permanent answer to the contradictions of sample size determination, by the use of simulation studies under simple random and cluster sampling schemes, with different sizes of power and type I error. The effective sample size is much higher when the design effect of the sampling method is smaller, particularly less than 1. Sample size increases for cluster sampling when the number of clusters increases.

scholarly journals Sample Size Guideline for Correlation Analysis

2016 ◽  
Vol 3 (1) ◽  
pp. 37 ◽  
Author(s):  
Mohamad Adam Bujang ◽  
Nurakmal Baharum
Keyword(s):  
Correlation Analysis ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Type I Error ◽  
Sample Size Calculation ◽  
P Value ◽  
Type I ◽  
Size Estimation ◽  
Study Objective ◽  
Type 1 Error

<p class="Abstract"><em>Correlation analysis is a common statistical analysis in various fields. The aim is usually to determine to what extent two numerical variables are correlate</em><em>d</em><em> with each other. One of the issues that are important to be considered before conducting any correlation analysis is to plan for the sufficient sample size. This is to ensure, the results that to be derived from the analysis be able to reach a desired minimum correlation coefficient value with sufficient power and desired type I error or p-value. Sample size estimation for correlation analysis should be in line with the study objective. Researchers who are not statistician need simpler guideline to determine the sufficient sample size for correlation analysis. Therefore, this study aims to tabulate tables that show sample size calculation based on desired correlation coefficient, power and type 1 error (p-value) values. Moving towards that, simpler guidelines are proposed to estimate sufficient sample size requirements in different scenarios.</em></p>

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Error Rate ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Binary Outcomes ◽  
Type I ◽  
Operating Characteristics ◽  
Type I Error Rate

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Anova Tests for Homogeneity of Variance: Nonnormality and Unequal Samples

1977 ◽  
Vol 2 (3) ◽  
pp. 187-206 ◽  
Author(s):  
Charles G. Martin ◽  
Paul A. Games
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Type I ◽  
Empirical Comparison ◽  
Jackknife Test ◽  
Type I Error Rate ◽  
Power And Control ◽  
Homogeneity Of Variance ◽  
Test Use ◽  
And Control

This paper presents an exposition and an empirical comparison of two potentially useful tests for homogeneity of variance. Control of Type I error rate, P(EI), and power are investigated for three forms of the Box test and for two forms of the jackknife test with equal and unequal n's under conditions of normality and nonnormality. The Box test is shown to be robust to violations of the assumption of normality. The jackknife test is shown not to be robust. When n's are unequal, the problem of heterogeneous within-cell variances of the transformed values and unequal n's affects the jackknife and Box tests. Previously reported suggestions for selecting subsample sizes for the Box test are shown to be inappropriate, producing an inflated P(EI). Two procedures which alleviate this problem are presented for the Box test. Use of the jack-knife test with a reduced alpha is shown to provide power and control of P(EI) at approximately the same level as the Box test. Recommendations for the use of these techniques and computational examples of each are provided.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

Optimized adaptive enrichment designs for three-arm trials: learning which subpopulations benefit from different treatments

Biostatistics ◽  
2019 ◽  
Author(s):  
Jon Arni Steingrimsson ◽  
Joshua Betz ◽  
Tianchen Qian ◽  
Michael Rosenblum
Keyword(s):  
Sample Size ◽  
Disease Severity ◽  
Adaptive Design ◽  
Type I Error ◽  
Type I ◽  
Expected Sample Size ◽  
Common Control ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs ◽  
Standard Designs

Summary We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs.

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