Overall Sample Size and Allocation to Regions

In recent years, the availability of multi-temporal global land-cover datasets has meant that they have become a key data source for evaluating land cover in many applications. Due to the high data volume of the multi-temporal land-cover datasets, probability sampling is an efficient method for validating multi-temporal global urban land-cover maps. However, the current accuracy assessment methods often work for a single-epoch dataset, and they are not suitable for multi-temporal data products. Limitations such as repeated sampling and inappropriate sample allocation can lead to inaccurate evaluation results. In this study, we propose the use of spatio-temporal stratified sampling to assess thematic mappings with respect to the temporal changes and spatial clustering. The total number of samples in the two stages, i.e., map and pixel, was obtained by using a probability sampling model. Since the proportion of the area labeled as no change is large while that of the area labeled as change is small, an optimization algorithm for determining the sample sizes of the different strata is proposed by minimizing the sum of variance of the user’s accuracy, producer’s accuracy, and proportion of area for all strata. The experimental results show that the allocation of sample size by the proposed method results in the smallest bias in the estimated accuracy, compared with the conventional sample allocation, i.e., equal allocation and proportional allocation. The proposed method was applied to multi-temporal global urban land-cover maps from 2000 to 2010, with a time interval of 5 years. Due to the spatial aggregation characteristics, the local pivotal method (LPM) is adopted to realize spatially balanced sampling, leading to more representative samples for each stratum in the spatial domain. The main contribution of our research is the proposed spatio-temporal sampling approach and the accuracy assessment conducted for the multi-temporal global urban land-cover product.

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.

Considerations of sample size in medical research

Proposal for the revision of guidelines for clinical trials of vaccines to prevent infectious diseases in Japan

ABSTRACTThe primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang’s SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the family-wise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial, which is transformed from the original program by using discounting factor, and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population, and design setting is explored. Examples with normal end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program.

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.

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

Multiple sclerosis (MS) is the most common non-traumatic disabling disease affecting young adults. A definitive curative treatment is currently unavailable. Many randomized controlled trials (RCTs) have reported the efficacy of Chinese herbal medicine (CHM) on MS. Because of the uncertain quality of these RCTs, the recommendations for routine use of CHM for MS remain inconclusive. The comprehensive evaluation of the quality of RCTs of CHM for MS is urgent. Nine databases, namely, PubMed, Embase, Web of Science, Cochrane Library, EBSCO, Sinomed, Wanfang Database, China National Knowledge Infrastructure, and VIP Database, were searched from inception to September 2023. RCTs comparing CHM with placebo or pharmacological interventions for MS were considered eligible. The Consolidated Standards of Reporting Trials (CONSORT) and its extension for CHM formulas (CONSORT-CHM Formulas) checklists were used to evaluate the reporting quality of RCTs. The risk of bias was assessed using the Cochrane Risk of Bias tool. The selection criteria of high-frequency herbs for MS were those with cumulative frequency over 50% among the top-ranked herbs. A total of 25 RCTs were included. In the included RCTs, 33% of the CONSORT items and 21% of the CONSORT-CHM Formulas items were reported. Eligibility title, sample size calculation, allocation concealment, randomized implementation, and blinded description in CONSORT core items were reported by less than 5% of trials. For the CONSORT-CHM Formulas, the source and authentication method of each CHM ingredient was particularly poorly reported. Most studies classified the risk of bias as "unclear" due to insufficient information. The top five most frequently used herbs were, in order, Radix Rehmanniae Preparata, Radix Rehmanniae Recens, Herba Epimedii, Scorpio, and Poria. No serious adverse effect had been reported. The low reporting of CONSORT items and the unclear risk of bias indicate the inadequate quality of RCTs in terms of reporting completeness and result validity. The CONSORT-CHM Formulas appropriately consider the unique characteristics of CHM, including principles, formulas, and Chinese medicinal substances. To improve the quality of RCTs on CHM for MS, researchers should adhere more closely to CONSORT-CHM Formulas guidelines and ensure comprehensive disclosure of all study design elements.

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.

In recent years, pharmaceutical industry has experienced many challenges in discovering and developing new drugs, including long clinical development timelines with significant investment risks. In response, many sponsors are working to speed up the clinical development process. One strategy is to combine the Proof of Concept (PoC) and the dose-ranging clinical studies into a single trial at the early Phase II development. One important question in designing this trial is how to calculate the sample size for such a study. In most of the early Phase II development programs, the budget concerns and ethical concerns may limit the total sample size for the trial. This manuscript discusses various ways of allocating the sample size to each treatment group, under a given total sample size; as well as the performance of different contrast test for PoC.

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.

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).

In a time when the alarms of research replicability are sounding louder than ever, mapping out studies with statistical and inferential integrity is of paramount importance. Indeed, funding agencies almost always require grant applicants to present compelling a priori power analyses to justify proposed sample sizes, as a critical part of the information considered collectively to ensure a sound investment. Unfortunately, even researchers' most sincere attempts at sample size planning are fraught with the fundamental challenge of setting numerical values not just for the focal parameters for which statistical tests are planned, but for each of the model's other, more peripheral or contextual parameters as well. As we plainly demonstrate, regarding the latter parameters, even in very simple models, any slight deviation in well-intentioned numerical guesses can undermine power for the assessment of the more focal parameters that are of key theoretical interest. Toward remedying this all-too-common but seemingly underestimated problem in power analysis, we adopt a hope-for-the-best-but-plan-for-the-worst mindset and present new methods that attempt to (a) restore appropriate conservatism and robustness, and in turn credibility, to the sample size planning process, and (b) greatly simplify that process. Derivations and suggestions for practice are presented using the framework of measured variable path analysis models as they subsume many of the types of models (e.g., multiple linear regression, analysis of variance) for which sample size planning is of interest. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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