Nmax and the quest to restore caution, integrity, and practicality to the sample size planning process.

In a time when the alarms of research replicability are sounding louder than ever, mappingout studies with statistical and inferential integrity is of paramount importance. Indeed, fundingagencies almost always require grant applicants to present compelling a priori power analyses tojustify proposed sample sizes, as a critical part of the information considered collectively toensure a sound investment. Unfortunately, even researchers’ most sincere attempts at sample sizeplanning are fraught with the fundamental challenge of setting numerical values not just for thefocal parameters for which statistical tests are planned, but for each of the model’s other moreperipheral or contextual parameters as well. As we plainly demonstrate, regarding the latterparameters, even in very simple models any slight deviation in well-intentioned numericalguesses can undermine power for the assessment of the more focal parameters that are of keytheoretical interest. Toward remedying this all-too-common but seemingly underestimatedproblem in power analysis, we adopt a hope-for-the-best-but-plan-for-the-worst mindset andpresent new methods that attempt to (1) restore appropriate conservatism and robustness, and inturn credibility, to the sample size planning process, and (2) greatly simplify that process.Derivations and suggestions for practice are presented using the framework of measured variablepath analysis models as they subsume many of the types of models (e.g., multiple linearregression, ANOVA) for which sample size planning is of interest.

Considerations of sample size in medical research

At the most general level, sample size refers to the number of participants (typically human or animal subjects) who provide data for a scientific experiment or study. Selecting an appropriate sample size is a fundamental feature of a well-designed study, and, as the title suggests, sample size can be carefully planned ahead of time. Sample size planning is important because the sample size of a study has implications for statistical power as well as accuracy. In conventional terms, statistical power reflects the probability of rejecting the null hypothesis, assuming that the purported effect is non-null (non-zero) in reality. Although power can be treated as a function of unknown parameter values, power is often defined conditionally on a specific value of population effect size. (On a technical level, power is conditional upon the population noncentrality parameter, which is a combination of sample size and effect size, but relying on effect size as a proxy is generally acceptable.) Power is calculated from the statistical significance threshold (otherwise known as the nominal Type I error rate or alpha-level), the population effect size, and the sample size. Because of the connection between sample size and power, sample size planning is sometimes called a priori power analysis. For example, if a new therapy developed to treat depression is truly beneficial, the sample size of a study assessing this therapy should be large enough to demonstrate the beneficial outcome as statistically significant. Accuracy reflects how close an estimate is to the parameter it aims to estimate. Accurate estimates are expressed with narrow confidence intervals (small margins of error). Sample size also has a connection to accuracy. For example, if the investigators in the previous example want to estimate how much the therapy reduces depressive symptoms, the sample size should be large enough so that the effect size estimate derived from the study is an accurate estimate of the true magnitude of the treatment effect. An underlying challenge to sample size planning for both goals is that the population effect size is unknown. An important corollary is that whether a particular sample size provides appropriate power (and in some cases, accuracy) depends on the population effect size, and as such, sample size cannot completely be judged without context. Although there are sometimes limitations to sample size, such as when working with a limited budget or specialized populations, and although sample size is often selected based on convenience or historical precedent, when possible it is desirable to directly plan the study sample size at the study design phase. Explicitly planning sample size helps to ensure that the sample size will be effective in meeting the investigators’ goals and answering the scientific questions of interest.

This chapter will discuss the key statistical considerations in sample size planning in multi-regional clinical trials (MRCT). It will build on the training module IV, discussing the concept of pooled region and pooled subgroups to aid overall sample size planning and allocation of that sample size across regions. This chapter will describe five approaches to sample size allocation as well as the pros and cons of each approach. In addition to the E17 sample size concept, we will also provide some summaries of popular statistical methodologies for sample size calculation and some practical implementation considerations.

The objectives of this research is to study the influence factors affecting decision making towards cosmetic surgery of Thai customers in generation X and generation Y in Bangkok. The sampling size is 400 and all the respondent were taking to consideration regarding to the Statistics, Frequency, Percentage and Average score card. For Standard Deviation and Hypothesis test, we used t-test statistics, ANOVA, Multiple Linear Regression and also Pearson Correlation at 0.05 level which detail are as following. 1.The majority are female with 23-52 year old, bachelor degree is the highest, monthly income is 30,001-40,000 Baht, married, most of them are company employee. The results from statistical hypothesis testing revealed that rational and emotional factors, and social influence factors. The multiple regression analysis demonstrated that had positive effect on the decision making to undergo cosmetic surgery of Generation X and Generation Y customers who lived in Bangkok at the significance level of.05, Occupational progress factors Pearson Correlation analysis demonstrated that had positive effect on the decision making to undergo cosmetic surgery of Generation X and Generation Y customers who lived in Bangkok Multiple Regression analysis demonstrated that at the significance level of.01. However, ANOVA analysis demonstrated that media type, frequency of media exposure, and time spending on researching the surgery generally did not affect Generation X and Generation Y customers’ decision making to undergo plastic surgery. Only media exposure in terms of content which featured the experiences of actors and singers as well as the innovation and technology of cosmetic surgery had influence on the samples’ decision making at the significance level of.05

A fundamental part of experimental design is to determine the sample size of a study. However, sparse information about population parameters and effect sizes before data collection renders effective sample size planning challenging. Specifically, sparse information may lead research designs to be based on inaccurate a priori assumptions, causing studies to use resources inefficiently or to produce inconclusive results. Despite its deleterious impact on sample size planning, many prominent methods for experimental design fail to adequately address the challenge of sparse a-priori information. Here we propose a Bayesian Monte Carlo methodology for interim design analyses that allows researchers to analyze and adapt their sampling plans throughout the course of a study. At any point in time, the methodology uses the best available knowledge about parameters to make projections about expected evidence trajectories. Two simulated application examples demonstrate how interim design analyses can be integrated into common designs to inform sampling plans on the fly. The proposed methodology addresses the problem of sample size planning with sparse a-priori information and yields research designs that are efficient, informative, and flexible. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Clinical practice involves measuring quantities for a variety of purposes, such as aiding diagnosis, predicting future patient outcomes, and serving as endpoints in studies or randomized trials. Measurements are almost always prone to various sorts of errors, which cause the measured value to differ from the true value; accordingly, studies investigating measurement error frequently appear in this and other journals. The importance of measurement error depends upon the context in which the measurements in question are to be used. For example, a certain degree of measurement error may be acceptable if measurements are to be used as an outcome in a comparative study such as a clinical trial, but the same measurement errors may be unacceptably large to make measurements usable in individual patient management, such as screening or risk prediction. In the past 20 years many papers have been published advocating how studies of measurement error should be analyzed, with a paper by Bland and Altman1 being one of the most cited and well known examples. There has been much controversy concerning the choice of parameter to be estimated and reported, and consequently confusion surrounding the meaning and interpretation of results from studies investigating measurement error. In this paper we first distinguish between the general concepts of agreement and reliability to aid researchers in considering which are relevant for their particular application. We then review the statistical methods that can be used to investigate and quantify agreement and reliability, dealing separately with the different types of measurement error study, while emphasizing the largely common techniques that should be used for data analysis. We reiterate that the judgment of whether agreement or reliability are acceptable must be related to the clinical application, and cannot be proven by a statistical test. We highlight the fact that reliability depends on the population in which measurements are made, and not just on the measurement errors of the measurement method. We discuss the advantages of method comparison studies making at least two measurements with each measurement method on each subject. A key advantage is that the cause of a correlation between paired differences and means in the so-called Bland–Altman plot can be determined, in contrast to when only a single measurement is made with each method. Throughout the paper, we try to emphasize that calculated values of agreement and reliability from measurement error studies are estimates of parameters, and as such we should report such estimates with CIs to indicate the uncertainty with which they have been estimated. We restrict our attention to measurements of a continuous quantity; alternative methods are required for categorical data2. One difficulty in the measurement error field is the number of different terms used to describe studies of measurement error. The terms 'agreement', 'reliability', 'reproducibility' and 'repeatability' are used with varying degrees of consistency in the medical literature. We first make clear the distinction between the statistical concepts of agreement and reliability3. Agreement quantifies how close two measurements made on the same subject are, and is measured on the same scale as the measurements themselves. Two measurements of the same subject may differ for a number of reasons, depending on the conditions under which the measurements were made. In a method comparison study there will be differences because of inherent variability in each of the measurement methods, as well as potentially a bias between the measurements from the methods. If the measurements are made by different observers or raters, differences may be due to biases between the observers. Agreement between measurements is a characteristic of the measurement method(s) involved, which does not depend on the population in which measurements are made, unless bias or measurement precision varies with the true value being measured. One popular way of quantifying agreement is to estimate the 95% limits of agreement, as proposed by Bland and Altman1. These limits are defined such that we expect that, in the long run, 95% of future differences between measurements made on the same subject will lie within the limits. If reliability is high, measurement errors are small in comparison to the true differences between subjects, so that subjects can be relatively well distinguished (in terms of the quantity being measured) on the basis of the error-prone measurements. Conversely, if measurement errors tend to be large compared with the true differences between subjects, reliability will be low because differences between measurements of two subjects could be due purely to error rather than to a genuine difference in their true values. The reliability parameter is also known as an intraclass correlation (ICC), as it equals the correlation between any two measurements made on the same subject. Reliability takes values between zero and one, with a value of one corresponding to zero measurement error and a value of zero meaning that all the variability in measurements is due to measurement error. As a dimensionless quantity, it is arguably quite difficult to interpret, and deciding what value constitutes sufficiently high reliability is often made in a subjective fashion. Repeatability of measurements refers to the variation in repeat measurements made on the same subject under identical conditions4. This means that measurements are made by the same instrument or method, the same observer (or rater) if human input is required, and that the measurements are made over a short period of time, over which the underlying value can be considered to be constant. Variability in measurements made on the same subject in a repeatability study can then be ascribed only to errors due to the measurement process itself. Reproducibility refers to the variation in measurements made on a subject under changing conditions4. The changing conditions may be due to different measurement methods or instruments being used, measurements being made by different observers or raters, or measurements being made over a period of time, within which the 'error-free' level of the variable could undergo non-negligible change. The first type of study we consider is a repeatability study, in which we investigate and quantify the repeatability of measurements made by a single instrument or method, and in which the conditions of measurement remain constant. The second type of study we consider is a method comparison study, in which measurements are made using two measurement methods on a sample of subjects. The use of two different methods means that this is a reproducibility study, but the term method comparison is used as it clearly communicates the changing conditions under which measurements have been made. In contrast to a repeatability study, systematic bias may exist between measurements made by two different methods, and their measurement errors may have different SDs. The last type of study we consider is one in which measurements are made by different observers or raters. Again this is a type of reproducibility study. As with method comparison studies, biases may exist between observers, and their measurement SDs may differ. As we discuss below (see Measurements with observers or raters), if interest lies in quantifying the measurement error characteristics of the particular observers in one's study, exactly the same analysis methods should be used as for a method comparison study. In order to investigate the repeatability of measurements, a repeatability study must, for an appropriately selected sample, make at least two measurements per subject under identical conditions. This means that the measurements must be made by the same measurement method, or the same observer or rater. The objective is to then quantify the agreement and reliability of measurements made by that particular method or observer. If the differences between two measurements made on a subject are approximately Normally distributed, in the long run we expect the absolute difference between two measurements on a subject to differ by no more than the repeatability coefficient on 95% of occasions. To estimate the within-subject SD, we can fit a one-way analysis of variance (ANOVA) model to the data containing the repeat measurements made on subjects. ANOVAs can be fitted in all modern statistical packages. ANOVA models partition variability in data into that which can be ascribed to differences between groups, and that remaining to within groups. For a repeatability study, the groups are defined by the subjects under measurement, and this must be specified in the statistical package used. The ANOVA model estimates how much variation in measurements can be attributed to differences in the true, or 'error-free' values of subjects, with the remainder constituting measurement error. Fitting the ANOVA model results in estimates of the between-subject and within-subject SDs (or alternatively the corresponding variances, which are the SDs squared). The estimate of the within-subject SD can be used in the above formula to give an estimate of the repeatability coefficient. We illustrate with a study by Järvelä et al., who investigated the repeatability of measurements of flow volume made by one observer using three-dimensional (3D) power Doppler ultrasonography from 22 ovaries5. The estimated repeatability coefficient was 3.97, meaning that the absolute difference between any two future measurements made by that particular observer on a particular subject/unit are estimated to be no greater than 3.97 on 95% of occasions. It is important to note that the repeatability of another observer may be different, because of differences in the training and ability of observers. Because the repeatability coefficient calculated is an estimate, it is important to calculate a CI for it to indicate how precisely it has been estimated. A CI may be given automatically by statistical software, but in the Appendix we review how a 95% CI for the within-subject SD can be calculated. Such a CI can be used to find a CI for the repeatability coefficient by multiplying the CI limits by . If the CI for the within-subject variance is given by software instead, the limits must first be square rooted to give a CI for the within-subject SD. The ANOVA model assumes that the measurement errors are statistically independent of the true 'error-free' value, and that the SD of the errors is constant throughout the range of 'error-free' values. Sometimes the SD of errors increases with the true value being measured. This should be checked by plotting paired differences between measurements against their mean, the so called Bland–Altman plot. We illustrate this later in the context of a method comparison study (see Plotting the data) and describe how such heterogeneous errors can often be dealt with by making a log transformation (see Non-uniform variability in measurement of the repeatability coefficient on the differences between measurements being approximately Normally This can be checked by a or plot of the paired differences in measurements on each subject. made in the of the CI for the within-subject SD is that the measurement errors are If this is in the may be to The reliability of a measurement method is often of interest when measurements are to be used to between subjects or groups of subjects. For example, if we have a choice of two measurement methods that could be used to an outcome in a clinical or study, using the method with reliability will give greater statistical power to differences between groups, for a given sample In this we describe how measurements from a single method can be used to estimate the reliability in a given we discuss the use of reliability to measurement methods (see Reliability in method comparison and different observers (see in The same ANOVA model as above (see can be used to estimate statistical will automatically give the estimated which is the of we can the estimates of the and within-subject SDs into the formula given above (see Agreement and to estimate the For the flow data of Järvelä et an estimated of was This means that of the variability in measurements of the flow was estimated to be due to genuine differences in flow between with the remaining being due to errors in the measurement process and the observer Because the measurements were all made by one the reliability may be to as As with the repeatability the calculated is an estimate, and a 95% CI should be statistical give a but in the Appendix we review the of a 95% CI for the For the flow data of Järvelä et the estimate of a 95% of to Because we use the same ANOVA model to estimate reliability as we describe for agreement, the same of 95% CIs for the estimate on of the true 'error-free' values. The reliability of a measurement method depends upon the of the population in which the measurements are made. the of reliability given above (see Agreement and we that the of true 'error-free' values in the measured by the between-subject SD, the value of agreement between repeat measurements is a characteristic of the method or instrument the of measurement errors is the range of true reliability depends on the of measurement errors and the true in the population in which measurements are made. The is with a a method for measuring has a within-subject SD of which does not with the underlying value being measured. we a study to estimate the reliability, and we sample from a heterogeneous population in which the between-subject SD in true is 20 This give a reliability or of . we sample our subjects from a more in which the SD of true values is the same as the variability of the within-subject error. In this the reliability is to . If studies report only an estimate of the reliability (ICC), can only make use of the estimate if the population in which the to use such measurements has We that report estimates of and within-subject SD, in to the In this can whether the measurement method will be sufficiently for their application, in which the between subjects may be we use a measurement method or in clinical we must that the measurements it are to by the measurement method used, that measurements made with the method are using the method. If the measurements from the two methods are sufficiently close and the of a patient on their basis be the the method could the method in clinical because the method is or to The as to what constitutes depends on how the measurements are to be used. If the two measurements are made on the same or we can quantify their To investigate and quantify the agreement between measurements made by two methods, we must at a a sample of subjects using the measurement method and the proposed method. The data from such a study of of measurements from each with the containing the measurements from the two measurement methods. we making two measurements on each subject using each method because the one measurement per method is the most common we by analysis. The first to such a is to plot the The plot is of measurements from the method against from the method (or If measurements were from we expect the to lie on the of the of can be used to if there is if more data are above the than this that the method on the measurements on the of fit for this plot and report a statistical for whether the of the plot from the of As by Bland and in a paper in this we expect the to have a than one if the method on the any measurement and so a of the that the true is to one is not a it the same as a plot with the of of the between the measurements from two methods is often more by plotting the difference in a measurements from the two methods against the of their measurements, as first by and this is to as the Bland–Altman and is frequently in of measurement error We illustrate the plot using an from the literature. et volume measurement in with and volume was measured using and also by We have approximately their plot of differences against means and it is as using only data from the in volume measured by three-dimensional and by against their from study by et the SD and the SD, each with 95% CI The plot the difference between calculated using and against the of two values. et not their plot or of results to indicate which measurements were from It is important to be and always the same measurement from the The the of the paired from zero an estimate of the bias between the two methods. which measurements have been from which to which method, on measurements. The indicate the estimated limits of agreement their CI which we describe The plot can be used to the differences between measurements made by the two methods. The variability of the differences between the two methods how well the methods If the variability of the paired differences is the range of measurements, and there is no between the difference and mean, we can quantify the agreement between the methods by the limits of the of the limits of agreement, we illustrate how the for limits of agreement may be and how they can be dealt on we expect the difference in volume as measured by the two methods to lie between and for 95% of future measurements. It is important to that the limits of agreement calculated in this are just and as with any type of estimate it is to quantify and report how precisely the limits are estimated of 95% The estimated limits of agreement indicate how large the between the measurements will be on 95% of occasions. The whether the methods sufficiently well must then be made depending on the context in which the measurements will be used. In contrast to the repeatability which assumes no bias between measurements, the limits of agreement method this The of the paired differences whether on one method to or measurements to the measurements of the second method, which we to as a bias between the methods. In the data in from et al., there was of a bias between the two methods, because the difference between the methods was close to at We can a statistical to whether there is of bias by a of the differences paired of the measurements from each we the that the true of the differences is corresponding to no bias between the methods. For the data in this that there was no of a bias between and made using The limits of agreement method assumes that the SD of the differences is throughout the range of measurements. Sometimes this will not be the In frequently the SD of the differences will with the In this for small values the limits of agreement will be than for values the limits will be of the plot of differences against from the study by et that the variability of the differences may be when the value being measured is The SD of the paired differences below the of the means was it was for above the This can often be by of the measurements of the two If the difference in the of the measurements results in constant we can calculate the limits of agreement and CIs for the limits in the Because the difference of two is to the of the we can all the estimates made on the log scale by Because of the limits of agreement to the of one measurements to the For the data in the of the difference in measurements was with SD The limits of agreement on the log scale are to the of with 95% limits of agreement for the of to The 95% CIs can be calculated on the log scale to the which measurements were made by which method, we cannot whether the is for measurements by to by or a plot of the of the measurements by the two methods to their mean, using a with the estimated 95% limits of agreement and their 95% This that the of a SD for the paired differences is more of measured by three-dimensional (3D) and by against of measurements, on data published by et the SD and the SD, each with 95% CI to log transformation can also be used to try to For example, et estimate limits of agreement using paired differences in measurements as a of the of the two measurements. The key is that a transformation should be used which that are approximately Normally and have approximately SD the range of It is for there to be an between the paired differences and of this is in a study by et al., who the agreement between as measured using and the calculated using in We have the data from their Bland–Altman plot of the difference between the volume and volume against the mean, and it in There to be a between the paired differences and differences between volume measured by three-dimensional and by from study by et correlation the SD and the SD, each with 95% CI We can a statistical to the for a whether the correlation coefficient between the paired differences and means from zero or by of the differences against the The is known as in the context of the SD of measurements from two methods, which for the data in is statistically for an between paired differences and how should we than models for the difference as a of the we should be given to the cause of the A between the paired differences and means when the SD of measurements from one method from the SD of measurements from the other method. There are at least two of an between paired differences and The first is that there is between the difference in measurements from the two methods and true value being the bias between methods over the range of true values. The second is that the within-subject SDs of the two methods differ. This will in the of changing bias if a method has or measurement errors than the method. This is and when a or measurement method is compared with a If the cause of the correlation is within-subject SDs and we were to plot the paired differences against the true value being there be no The between the Bland–Altman plot and a plot against true values because the paired means measurement errors, in contrast to the true values. as by and with only one measurement per method per we cannot which of two is the To we must make an that the within-subject SDs of the two methods are so that the correlation is attributed to changing or that the two within-subject SDs are and that there in no between bias and true value we may have a of For the of we with the from et by that the correlation is due to a difference in the within-subject SDs of the two methods. A correlation between differences and means will when the method with measurement error is from measurements of the method with error. For the data in the correlation is the errors than the measured by which and to the volume measured by as the true that such measurements were to no an that there is no changing we can calculate 95% limits of agreement, as by et al., because the limits of agreement method does not that the two within-subject SDs are In order to from the data whether changing bias is we must make at least two measurements with each method on each subject. This study has often been but is we describe how such studies can be to whether a correlation between paired differences and means is due to changing bias between the methods (see with two measurements from each As reliability may be a parameter with which to two different measurement methods. To estimate each reliability, we must make at least two measurements of each subject with each of the two methods. The repeat measurements from each method can then be as two repeatability studies (see estimates of each reliability, which can be advantage of using reliability to measurement methods is that it can be used to methods when their measurements are given on different or as the reliability is a dimensionless Because reliability depends on the of the true values in the population (see Reliability depends on population it is that reliability are compared only if they have been estimated from the same report a single reliability estimate from method comparison studies in which it that only a single measurement per subject per method is It appear that the estimates are using a one-way ANOVA as above (see The one-way ANOVA model assumes that there are no systematic biases between measurements within subjects, and that the within-subject SDs are for all measurements measurements are from two different methods, of may be bias may exist between the methods and their within-subject SDs may differ. If is the model is and the estimates of within and between subject SD are estimates of different The reliability estimate then has a different interpretation from the We against the estimate from the one-way ANOVA to data from two different measurement methods. As making two measurements with each method an of whether bias between the methods is constant or whether the measurement error SDs differ. the measurements from each method can be separately as two repeatability studies, using the methods estimates of the repeatability coefficient and reliability for each method. The Bland–Altman plot of paired differences against means can be by using the of a two measurements from each method in of the single As with the Bland–Altman plot on a single measurement from each method, an correlation between differences and means could be due to changing bias or a difference in measurement error between the methods. We describe a to whether any bias between measurements made by a method with the as measured by the method. We by the of the two measurements made using the method and we by the measurement made on subject by the measurement method. A plot of against can then be used to investigate whether the bias between the and methods with the as measured by the method. It is that is against rather than against because the a to the common measurement error in and It is for the same that plotting paired differences between measurements from two methods against the measurements from one method is in method comparison between values of and that the bias between the methods with the true value as measured by the method. A statistical of this can be by a for with as the The for the that the in this from zero the of against the of constant If of bias changing with true value is we may be in the at which the bias between the methods We can estimate this by the estimate from the above model by our estimate of the This quantity is an estimate of the in bias method for a in the true value as measured by the method. The choice to against is We could have against and have a different and estimate of the at which the bias This choice is because we will two different and because it means the is statistically The model underlying is a type of and as such can be fitted using software for The is to and for measurements are made by observers or raters. measurements made by two different observers are than are two measurements made by the same observer. as with two methods, measurements from two observers may differ due to bias between the observers observer and their measurement errors may also have different SDs. For example, measurements from an observer who can make more measurements will have a SD than made by a observer. If measurements in the future are to be made by different observers, we to describe and quantify the differences between such measurements in order to whether differences are genuine or may be due to measurement error. As with a method comparison study, the way to study this is for each observer to make at least two measurements of a sample of subjects. The of such a study and the type of statistical analysis that are should be by whether interest lies in a particular of observers, or whether we are in a population of observers or raters. If future measurements are to be made by a of observers, the measurement error study should each of observers making measurements of a sample of subjects, with at least two measurements per observer per subject. We can then use exactly the same methods as for method different observers as different measurement methods. If each observer two or more measurements on each we can whether there is bias or changing bias between observers. We can the bias of future measurements (in by measurements using the corresponding estimated We can also estimate repeatability and reliability for each using the one-way ANOVA model (see It may be of interest to which observers are more and if differences in reliability can be related to observer such as of or If we are to that biases between observers are we can fit a so-called model to such a for a subject and observer The estimates indicate the and for bias between observers. such models can be to the measurement error SD to differ between observers. The observers in a measurement error study can often be considered as a sample of observers from a population of observers who may be used in future studies or clinical In this we are not in the particular observers in the measurement error study, but only in the that they the population of observers. In this it is important that a number of observers is used in the measurement error study. For example, if the measurement error study only two observers, can be the population of observers, because we have a sample of just Because the observers in the measurement error study are considered a sample from the population of observers, we such a study using a model that the observer as a We use a with subject and observer Fitting a model estimates of the between-subject SD, SD and the measurement error SD to be the same for different The SD variability in measurements due to observers, or biases between the observers. such estimates one can calculate the SD of the difference in two measurements, made by the same observer or by two different observers. the SD is the will be than the This is because biases between observers to make measurements from different observers it is more difficult to distinguish between subjects on the basis of measurements made by two different observers than if the subjects been measured by the same observer. In this paper we have distinguished between the concepts of agreement and parameter is to the as they describe different characteristics of the measurement The choice of what to report in a particular study should be by how measurements are to be used in the and also by the fact that may to use a measurement method in a different We have the fact that the reliability of a method depends not only on the of the measurement errors but on the of true values in the population in which measurements are made. As measurement techniques potentially may be used in a variety of clinical and different it is to report estimates of and within-subject SDs. We have which methods we are for the analysis of repeatability studies, method comparison studies, and studies with measurements made by different observers or raters. In we not a single reliability coefficient should be used for method comparison If the reliability of two methods are to be each reliability should be estimated by making at least two measurements on each subject with each measurement method. We have how an between paired differences and means may not be by changing bias between two methods. Such an may also be by a difference in the measurement error but with only one measurement per subject per method it is not to which of is the comparison studies should make at least two measurements per subject per method. This an of the of any between paired differences and means for measurements made by the two methods, and also the repeatability and reliability of each method to be estimated. measurements an observer or measurement error studies must use an number of observers if interest lies in making a population of observers. we results for the of CIs for two in the one-way ANOVA in which two measurements are from each

ABSTRACTWe consider clinical trials with a binary composite endpoint where the trial is successful when a significant result is achieved for the composite or one prespecified main component. Appropriate sample size planning is challenging in this situation, as in addition to the Type I error rate, power, and target difference the overall event rates and the correlation between the test statistics have to be defined. Reliable estimates of these quantities, however, are usually hard to obtain and therefore there is a high risk to not achieve the intended power in a fixed sample size design. In this article, we propose an internal pilot study design where the nuisance parameters are estimated in a blinded way at an interim stage and where the sample size is then revised accordingly. We investigate the characteristics of the proposed design with respect to the actual Type I error rate, power, and sample size. The application of this design is illustrated by a clinical trial example.

BackgroundWhen evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner.MethodsFor longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists.ResultsAs an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis.ConclusionsThis paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.

ObjectiveTo investigate publication rates in small trials and to explore which factors are associated with publication rates in small trials, including sample size, the type and number of primary and secondary outcomes. Study design and settingWe studied a subgroup of ‘small’ trials from a pre-existing dataset (IntoValue), containing German trials completed between 2009 and 2017. Small trials were defined as phase II-III, III and IV trials with 150 or fewer participants. We performed an updated publication search and collected additional data from online trial records. ResultsOut of 499 trials, 325 (65%) trials published their results in a journal article or dissertation. Median time-to-publication was 3.41 years (95% CI: 3.04–4.10). Planned sample size was not associated with publication rates, but the difference between planned and achieved sample size was (per 10% unsuccessfully recruited participants, HR = 0.95, 95% CI: 0.91–1.00). Phase III vs. II-III trials, studied intervention (device vs. other) and clearly vs. unclearly defined primary outcomes predicted a higher likelihood of earlier publication. ConclusionAbout 35% of small trials in Germany remain unpublished, even after an extensive follow-up period of over 9 years. Publication rates are low and were associated with sample size, trial phase and type of intervention.

Design and analysis features used in small population and rare disease trials: A targeted review

In clinical trials, researchers usually determine a study sample size prior to the start of the study to provide a sufficient power at a targeted treatment difference. When the targeted treatment difference deviates from the true one, the study may either have insufficient power or use more subjects than necessary. To address the difficulty in sample size planning, researchers have developed various flexible sample size designs and compared their performances. Some previous work suggests that re-estimation designs are inefficient and that one can improve uniformly by using standard group sequential likelihood ratio tests, although more interim analyses are involved. However, researchers need to further study the statement and the minimal number of tests needed before a standard group sequential test might outperform a re-estimation design. In this paper, we conducted simulation studies to answer these questions using various optimality criteria.

Some Thoughts About Data Type, Distribution, and Statistical Significance

To evaluate the reporting of critical design issues and methods of statistical analysis in pharmacogenetic studies published in the medical literature. Systematic review of 65 original pharmacogenetic studies published in the literature over the last 15 years. The sample size determination and the planned sample size were lacking in 63 papers. The study design characterization was lacking in 43 papers. The number of patients analyzed ranged from 36 to 1400 (median=161 and interquartile range of 119-250). The Pearson's χ2 test and the Fisher's test were the most common forms of analysis. Multiple statistical testing was relevant to 59 papers, but only 11 addressed the issue of multiplicity (Bonferroni correction). Sources of multiplicity were multiple association assessment (45 papers), analysis of both genotype and allelic frequencies (44), and multiple analysis methods (unadjusted and adjusted). Hardy-Weinberg equilibrium was tested in 12 of 45 papers performing allelic analysis and was fully reported in four of them. The results of association analyses were commonly reported as P values but rarely as estimates of an association measure (odds ratio or relative risk) and its accuracy. These results show that there is considerable room for improvement in the current standards of design, analysis, and reporting of pharmacogenetic research.

Several studies have compared the feeding of genetically modified (GM) grains and conventional grains to poultry. The general conclusion has been that there were no significant differences detected in the biological performance of the birds (i.e., the grains were bioequivalent). However, the question has been posed whether the experimental designs used in the studies had sufficient statistical power to detect treatment differences. The power of tests can be used to determine the ability of an experimental design to detect treatment differences. The definition of statistical power is the probability of rejecting the null hypothesis when it is false and should be rejected. The complement of statistical power is the Type II error (beta). That is, accepting the null hypothesis that there is no difference in treatments when there is one. A priori power analysis can indicate the probability at which the sampling regimen or experiment can actually detect an effect if a difference exists. Post hoc power analysis indicates the sufficiency or the sample size needed for an experiment that has already been conducted. In the current study, the power of tests for experiments published in the literature where significant and nonsignificant differences were reported between control birds and birds fed new feed grains was examined. With some exceptions, the power of tests is rarely formally considered or mentioned in poultry research. The results of the survey of the literature showed, in general, low power of statistical tests for feeding experiments involving non-GM grains or in those cases when GM and non-GM grains were compared in poultry feeding experiments. These results suggest that care needs to be taken when designing experiments for bioequivalence of grains fed to poultry.