Assessing sample size

The appropriate number of patients to be recruited for a trial is dependent 

Often there is a desire to compare responses to multiple treatments rather than simply evaluate active against a placebo control. For instance, it may be useful to evaluate several doses or to assess a product against another marketed product. Increasing the number of treatments will increase the sample size required overall, but will also increase the number of subjects required per treatment arm because the number of statistical comparisons is larger. If all between-group comparisons are to be made, the number of statistical tests increases dramatically as the number of treatment arms increases. With two groups, only one comparison is possible. With three groups, the number 

For some conditions, a large placebo effect can be anticipated. For example, studies of hormone replacement therapies for hot flashes in postmenopausal women consistently show a 50% decline from baseline in the number of daily hot flashes in the placebo group. Therefore, in order to show significance, an active treatment must produce an effect that is substantially larger than 50%. A marked placebo response is commonly observed with any condition that has a subjective component, such as chronic pain (e.g. arthritis), episodic pain (e.g. headaches), psychological states (e.g. anxiety), and certain physiologic measurements (e.g. blood pressure). 

The larger the benefit, the smaller the number of subjects required to show statistical significance. For example, if a herbal supplement is expected to produce a 5% lowering of the blood cholesterol level vs placebo, this will require many more subjects than a study evaluating the effects of a drug that is expected to produce a 50% reduction vs placebo. 

The significance level relates to the risk of designating a chance occurrence as statistically significant. Usually a 5% level is utilized for testing treatment effects. If a p-value of 0.04 is reported for a treatment effect, this means that there is only a 4% chance that the difference in response between the active and control treatments occurred due to chance. Keep in mind, however, that if many tests are run in a trial, it is entirely possible that one or two might be significant due to chance. As an extreme example, consider a study in which 100 statistical tests are run. We would expect five of those tests to show significance with a p-value of 0.05 or less due to chance. Therefore, it is essential to specify the main tests to be run in the protocol. Any tests that are conducted after the trial has been completed should be clearly labeled as post hoc exploratory analyses. 

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Tibshirani R. A simple method for assessing sample sizes in microarray experiments. BMC Bioinformat. 2006 7 106. 

Data gathering in the water column should not be overlooked at the appraisal stage of the field life. Assessing the size and flow properties of the aquifer are essential in predicting the pressure support which may be provided. Sampling of the formation water is necessary to assess the salinity of the water for use in the determination of hydrocarbon saturations. 

Many clinical studies have been performed on human subjects to assess the effect of soy isoflavones on chronic disease risk factors with no ill-effects (see Section 10.4). The safety profile of isoflavones is, however, difficult to establish because of the limited sample sizes and short periods of investigation of such studies. At present the upper tested limits are ... 

Similarly, the number and types of tests completed will influence the cost in ways that can be very complex. Recently, the author was involved in designing a proof-of-principle study intended to assess the ability of a dietary supplement to enhance weight loss among subjects instructed to follow a reduced energy diet. Sample size calculations were run for two scenarios, the first using change in body weight as the primary outcome variable, the... 

Extensive testing was also carried out to assess future hazards for human habitation and residential use of the area. This testing was carried out in an extensive program funded by EPA In 1980-81 after remedial drainage construction work at the Canal was complete (O. All of the survey design and technical laboratory problems described above were encountered in this program. Despite extensive efforts to meet requirements for sample size and distribution, to provide for adequate control sampling away from the Canal area, and... 

To discuss the development of CRMs for the emerging use of microanalytical techniques, one has to be concerned chiefly with the degree of homogeneity of the components in the material at the designated sample size. Basic indications for the homogeneity properties of a CRM for microanalytical methods and the assessment of these properties can be derived from the general requirements ... 

What level of inaccuracy can be expected for a simulation with a certain sample size N1 This question can be transformed to another one what is the effective limit-perturbation Xf or xg in the inaccuracy model [(6.22) or (6.23)] To assess the error in a free energy calculation using the model, one may histogram / and g using the perturbations collected in the simulations, and plot x in the tail of the distribution. However, if Xf is taken too small the accuracy is overestimated, and the assessed reliability of the free energy is therefore not ideal. In the following, we discuss the most-likely analysis, which provides a more systematic way to estimate the accuracy of free energy calculations. 

TMA has mainly been used in the study of polymers. The mechanical properties study may be used to characterize a polymer as well as to assess its mechanical utility. There is an obvious application to quality control. The ability to study small specimens gives the technique a distinct advantage over more traditional methods of mechanical testing if sample size is limited. A typical TMA study has already been exemplified in Figure 11.19. 

Two-tailed tests require larger sample sizes than one-tailed tests. Assessing two directions at the same time requires a greater investment. 

Detailed Hazard Assessment Low Thermal Inertia (41- factor) Adiabatic Calorimeter A UNDESIRED ATonset ATaDIAB dT/dt dP/dt sadf Tm, tMR estimates Vent sizing data Sample size 100 ml to 1 liter Safe for general laboratory work Good mimic of large-scale runaway Ideal for what-if scenario study... 

One of the sessions of the Symposium was largely devoted to presentation and discussion on the use of various experimental calorimetric methods for use in assessing possible hazards in chemical processing operations. The methods described covered a wide range of sample sizes and degrees of complexity Grewer, T. Adiabatic small-scale reaction test in Dewar, simple to operate. Janin, R. Measurements of heat release by DSC and of pressure development in sealed microcapsules. 

Significant ethnic variability is observed with many pharmacogenetic markers (38). Consequently, the demographics of the study population need to be taken into account when performing power analyses to determine the appropriate sample size. If the frequency of the polymorphism to be studied is unknown in any of the ethnic groups included in the study, the polymorphism should be screened in relevant population controls to determine their allele frequency. This will help to narrow down the number of polymorphisms to be included and prevent using finite patient DNA to assess markers that may have low frequency or be absent from the study population. 

It is well recognised that the faecal bile acid content of random stool samples is highly variable with marked daily variation.Therefore, studies testing the association between luminal bile acid exposure and the presence of colorectal neoplasia have usually measured serum bile acid levels, which demonstrate less variability and are believed to reflect the total bile acid pool more accurately. Serum DCA levels have been shown to be higher in individuals with a colorectal adenoma compared with individuals without a neoplasm. Only one study has assessed future risk of CRC in a prospective study of serum bile-acid levels. The study was hampered by the small sample size (46 CRC cases). There were no significant differences in the absolute concentrations of primary and secondary bile acids or DCA/CA ratio between cases and controls although there was a trend towards increased CRC risk for those with a DCA/ CA ratio in the top third of values (relative risk 3.9 [95% confidence interval 0.9-17.0 = 0.1]). It will be important to test the possible utility of the DCA/ CA ratio as a CRC risk biomarker in larger, adequately powered studies. A recent study has demonstrated increased levels of allo-DCA and allo-LCA metabolites in the stool of CRC patients compared with healthy controls. ... 

Limitations include difficulties in performing a correct exposure assessment, in some cases even a lack of information on exposure, insufficient sample size (i.e., small number of subjects in the study), selected group of subjects (e.g., the workforce), short length of follow-up, exposure to more than one substance, and potential errors (bias, confounding). 

Clinical trials generate vast quantities of data, most of which are processed by the sponsor. Assessments should be kept to the minimum that is compatible with the safety and comfort of the subject. Highest priority needs to be given to assessment and recording of primary endpoints, as these will determine the main outcome of the study. The power calculation for sample size should be based on the primary critical endpoint. Quite frequently, trials have two or more evaluable endpoints. It must be stated clearly in the protocol whether the secondary endpoints are to be statistically evaluated, in which case power statements will need to be given, or are simply... 

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