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Guide Sample Size Tables for Clinical Studies

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The sample size calculation for a finite population uses the formula presented in Chart 1 :. The sample size calculation for subgroup comparison hypothesis testing within a sample depends on the selected statistical test, differences between the groups and the investigator's tolerance to detecting differences when they do not exist type I error or failure to detect differences between the subgroups when they really exist type II error.

One strategy that enables to reduce the variability of measurements, increasing the comparability between the individuals in a sample, and, consequently, reducing the sample size required to detect a phenomenon, is pairing or matching of observations Chart 2. It can be used when the same subject is observed at different moments longitudinal study or submitted to measurements in different areas of the body, e. Another type of pairing - more elaborated - is the selection of subjects presenting the same characteristics: age, gender, ethnical group, social class, among other variables that can control the individual variability.

In these cases, the measurement is made between pairs, rather than using a direct comparison of subgroups 1. The sample size, considering the formula presented in Table 2, would be:. In studies where several variables are important for the analysis of the studied outcome, i. Tests for equivalence, non-inferiority and agreement require specific sample sizing methods, different from the tests of differences between mean values and proportions commonly used. In addition, multivariate analyses, comparison of subgroups to different numerical proportions, or multiple longitudinal comparisons, also involve higher complexity in sample sizing calculation.

All these items exceed the scope of this paper 1,5, Sample size calculation for trials that involve the estimate of linear correlation between two quantitative variables is dependent solely on the linear correlation coefficient Chart 3. Example 5: To establish the correlation between the measurement of muscle force of quadriceps and the maximum distance covered by patients with history of intermittent claudication, the sample size calculation could be based on the study conducted by Pereira et al.

According to the formula presented in Chart 3 :. Longitudinal studies prospective cohorts and clinical trials , as they require the patients' follow-up over long periods, can be affected by subjects who leave, quit, drop out, die or are excluded from the study. Dropout subjects should be studied judiciously regarding their reasons for leaving and whether they present difference in the study variables in relation to the other study subjects, to identify factors specifically linked with the dropouts. Provided that the conclusions of a study can be generalized only to the population under study, it is possible that repeating the study in other centers may yield different results, reflecting the reality of the other populations.

Sample Size Calculators for Designing Clinical Research

Such results may indeed exceed the confidence interval limits for the primarily estimated parameter, not necessarily meaning lack of internal validation of either study. This is one of the risks of using results from other investigators when sizing the sample of a different population.

A preliminary analysis of the first fraction of cases pre-test is strongly recommended, making it easier to estimate the sample required to each reality, and prevent analytical constraints at the end of the study One should be, however, careful to prevent sample supersizing, which usually occurs when the access to large computer databases are available.

Increasing the sample reduces the confidence intervals of estimates and allows the detection of differences between subgroups which, even if statistically significant, do not present clinical relevance 3, At last, there are different formulas for the sample size calculation to specific statistical trials besides those presented here, depending on the mathematical model considered, which can be easily found in the literature or on the Internet 1,15, Nabil Al-Rabeei. No Downloads. Views Total views. Actions Shares. Embeds 0 No embeds.

No notes for slide. Calculate samplesize for clinical trials 1. But it is easier to refer to available tables. The difference of cure rate is 0. Total of Tools are available and most of them are free. Decide what is your study design and choose the appropriate method to calculate the sample size. Statistical methods for rates and proportions.

New York: John Wiley and Sons, Gehan EA. Clinical Trials in Cancer Research. Environmental Health Perspectives Vol. An introduction to power and sample size estimation. Emergency Medical Journal ;20; Survey sampling. A seamless trial design is a study design that can address study objectives within a single trial which are normally achieved through the conduct of separate independent trials. A seamless adaptive design is a seamless trial design that fully utilizes data collected from patients before and after the adaptation in the final analysis.

A seamless trial design is called a two-stage seamless design if it combines two studies into a single study. Thus, a two-stage seamless adaptive design consists of two phases stages; each stage contains one study , namely, learning or exploratory phase and confirmatory phase. A two-stage seamless adaptive trial design reduces lead time between studies ie, the first study and the second study. Most importantly, data collected at the learning phase are combined with those data obtained at the confirmatory phase for final analysis.

For a two-stage adaptive design, since it combines two independent trials into a single study, the study objectives and study endpoints at different stages studies could be different. Depending upon study objectives and endpoints used, two-stage adaptive trial designs can be classified into four categories of designs as indicated in Table 6.

Table 6 Classifications of two-stage adaptive designs Abbreviations: D, different; S, same. Thus, we have SS same objectives and same endpoints , SD same objectives and different endpoints , DS different objectives and same endpoints , and DD designs, where SS designs indicate study designs with the same objectives and same endpoints at different stages and so on.

In clinical trials, different study objectives could be dose finding or treatment selection at the first stage and efficacy confirmation at the second stage. Different study endpoints could include biomarker, surrogate endpoint, and clinical endpoint with different shorter treatment duration at the first stage versus clinical endpoint at the second stage, or the same clinical endpoint with different treatment durations.

SS designs are similar to typical group sequential designs with one planned interim analysis. Thus, standard methods for a typical group sequential design can be directly applied to the SS designs. To address this issue, Table 7 provides a simple comparison in terms of significance level, power, lead time, and sample size required for achieving a desired power.

Table 7 Simple comparison Note: n 1 and n 2 are the sample sizes for the two separate trials and m is the sample size for the two-stage adaptive design. Abbreviations: m, months; yr, year. Besides, the use of two-stage adaptive design could reduce lead time between studies and hence shorten the process of clinical development. In terms of the sample size required, the use of two-stage adaptive design may also reduce the sample size required for achieving the desired power depending upon the study objectives and the study endpoints used at different stages in the two-stage adaptive trial design.

In clinical trials, a DMC is usually established to monitor the validity and integrity of the intended clinical trial. Typically, a DMC consists of experienced physicians and biostatisticians. A charter is necessarily developed to outline the activities and functions of the DMC, but also to describe roles and responsibilities of DMC members. A DMC has the authority to perform a review of unblinded data, though most DMCs prefer a blinded review of interim data. In clinical trials, an independent DMC ensures the quality, validity, and integrity of the clinical trial.

Some sponsors, however, will make every attempt to influence the function and activity of the DMC, which challenges the independence of the DMC. The following is a summary of some observations which are commonly seen in DMCs across various therapeutic areas:. Based on the above observations, it is doubtful that an independent DMC is really independent. In addition, the following debatable issues have also been raised.

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First, should the DMC directly communicate with regulatory agencies for any wrongdoing in the conduct of the intended clinical trial? Second, can the DMC perform well if a less-well-understood adaptive design is used in the intended clinical trial? In this article, several commonly encountered statistical controversial issues in clinical research are discussed.

In practice, many more debatable issues are still under tremendous discussion among regulatory agencies, academia, and the pharmaceutical industry. It should be noted that, debatable issues are likely encountered in clinical trials. Consequently, the accuracy and reliability of statistical inference on the treatment effect is a concern to the investigator.

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Sample sizes in dosage investigational clinical trials: a systematic evaluation

To address this issue, Shao and Chow 17 and Chow and Shao 18 proposed the concept of reproducibility and generalizability of evaluation of the accuracy and reliability of the clinical trials. The reproducibility is defined as the probability of observing positive results which have achieved statistical significance of future clinical trials that are conducted under similar experimental conditions given the observed significant positive clinical results.

Shao and Chow 17 suggested considering the probability of reproducibility as a monitoring tool for the performance of a test treatment under investigation for regulatory approval. The evaluation of reproducibility provides valuable information which protects patients from unexpected risk of the test treatment.

For example, in a given clinical trial with a relatively low probability of reproducibility, the observed significant positive clinical results may not be reproducible if the clinical trial is repeatedly conducted under similar experimental conditions. Chow SC.

Controversial Issues in Clinical Trials. Some controversial issues in clinical trials.

Sample Size Tables for Clinical Studies | Wiley Online Books

Ther Innov Regul Sci. Sample Size Calculation in Clinical Research. Taylor and Francis; Modification of sample size in group sequential trials. Ascorbic acid for the common cold: a prophylactic and therapeutic trial.


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Chow SC, Shao J. Analysis of clinical data with breached blindness. Stat Med. Assessing sensitivity and similarity in bridging studies. J Biopharm Stat. Inference for clinical trials with some protocol amendments. Statistical consideration of adaptive methods in clinical development. Chow SC, Chang M. Adaptive Design Methods in Clinical Trials.

Westfall P, Bretz F. Multiplicity in clinical trials. In: Chow SC, editor. Encyclopedia of Biopharmaceutical Statistics. New York: Taylor and Francis; — Geneva: ICH; London: European Medicines Agency; Points to consider on multiplicity issues in clinical trials.

Literature

London: The European Agency for the evolution of medicinal products evaluation of medicines for human use; On closed testing procedures with special reference to ordered analysis of variance. Biometrik a. Soon G. Editorial: missing data — prevention and analysis. Shao J, Chow SC.