Acceptance criteria meaning

Acceptance criteria means the product specifications and acceptance/rejection criteria, such as acceptable quality level and unacceptable quality level, with an associated sampling plan, that are necessary for making a decision to accept or reject a lot or batch (or any other convenient subgroups of manufactured units). 

In contrast to variable testing (comparison of measured values or analytical values), attribute testing means testing of product or process quality (nonconformity test, good-bad test) by samples. Important parameters are the sample size n (the number of units within the random sample) as well as the acceptance criterion naccept, both of which are determined according to the lot size, N, and the proportion of defective items, p, within the lot, namely by the related distribution function or by operational characteristics. 

An approach for analyzing data of a quantitative attribute that is expected to change with time is to determine the time at which the 95% one-sided confidence limit for the mean curve intersects the acceptance criterion. If analysis shows that the batch-to-batch variability is small, it is advantageous to combine the data into one overall estimate by applying appropriate statistical tests (e.g., p-values for level of significance of rejection of more than 0.25) to the slopes of the regression lines and zero-time intercepts for individual batches. If it is inappropriate to combine data from several batches, the overall shelf life should be based on the minimum time a batch can be expected to remain within the acceptance criteria. 

The shelf life for a single batch is usually computed based on regression techniques. An appropriate approach to shelf life estimation when using regression analysis is by calculating the earliest time at which the 95% confidence limit for the mean intersects the proposed acceptance criterion [8]. A detailed description of shelf life calculations is provided in Sections 7.2.3 and 7.2.4. 

In other words, the range is expressed as a percentage of the mean. (With only duplicate results the range is just the difference between the two results.) If a laboratory uses the RPD, then an acceptance criterion is set in the standard operating procedure stating what to do if the criterion is exceeded. 

The sample mean for this example is 97.76%, so the upper limit for the sample RSD is 3.68%. It is recommended that the means always be rounded to the more restrictive RSD limit so that the assurance level and lower bound specifications are still met, so in this case 97.76% is rounded to 97.7%. Therefore, since the sample RSD of 2.70% is less than the critical RSD of 3.68, the acceptance criterion is met. This means that with 90% assurance, at least 95% of samples taken from the blender would pass the USP 25 content uniformity test for capsules. As mentioned in Sec. III.A., if the USP 25 tablet criterion were evaluated instead of the capsule criterion, the upper limit for the sample RSD would be 2.98% and would also pass. 

The lower and upper acceptance limits for the mean are 92.4 to 107.6. Since 98.2 falls within the interval, the capsules pass the acceptance criterion. 

The lower acceptance limit for the mean is 89.20%. Since 96.44 is greater than 89.20, the capsules pass the acceptance criterion for dissolution. 

In the authors laboratory, the IS response of a sample is compared to the mean IS response of the accepted calibration standards and quality controls in the same run, i.e., those that meet the acceptance criterion of accuracy and do not show other abnormality (e.g., poor chromatography). When the IS response of a sample is outside 50 % of the mean IS signal of calibration standards and quality controls, the sample will be repeated. Moreover, an investigation may be initiated for repeated abnormal IS signal and when there is a pattern or trend. This acceptance criterion was also recommended by others (e.g., [13]). Alternatively, though not reported, some compare the IS response of a sample to those of adjacent samples or to the mean IS response of all the samples in a batch. No matter what approach is used, it is important to be able to single out abnormal samples and to perform corrective actions to ensure that their reported concentrations are accurate. 

Table 9-19 shows the regression analysis performed by Excel using the available Add-In functionality ToolPak. In Table 9-19, the y value of 4.6% was calculated using the 1.0% standard (y = mx + b), where x is the concentration of the 1.0% standard and acceptance criteria given in Table 9-17 of <5.0% was met. In Table 9-19, the %RSD (standard error/y) was calculated as 2.1%, and this had met the acceptance criterion of <4.6% given in Table 9-17. In order to identify if there are significant deviations from the assumed linearity, an investigation of the residuals should also be performed this is demonstrated in Figure 9-6. The residuals were randomly distributed around a true mean of zero.

The obtained results from both analysts are grouped together to determine whether this additive precision is acceptable or not. For example, if each analyst prepared two sample preparations API at target concentration for intermediate precision, then a total of four values are pooled together (additive precision) as stated in Table 9-3 of Assay, Precision rei < 2.0%, n > 4. In addition to an additive precision requirement, some laboratories also include an acceptance criterion (for example, absolute mean difference <2%) for mean value. For example, if analysts 1 and 2 prepare three sample preparations each, then additive precision is calculated from a total of six values (three from each analyst). In addition, the mean value obtained by analyst 1 (n = 3) is compared against the mean value obtained from analyst 2 (n = 3), in which it must pass an absolute difference (between the two means) of <2.0%. 

In the second part of the validation we used the predicted values from the model at 12 h for the acceptance criterion for three reproducibility runs. The model predictions were used since they take into account all of the data generated in the first part of the validation. The acceptance criterion for the mean level of stopper moisture was the 99% confidence interval determined from the model. The calculated 99% confidence interval for the mean value after 12 h of drying was 963 213 ig/stopper. Results from validation runs in Table 7 show that the modeling approach accurately predicted the mean moisture content of the stoppers. The moisture content of the stoppers was consistent from run to run as can be seen in the overlapping 95% confidence intervals for the mean values. 

The details of the assessment of stability data are under intense discussion within the scientific community. A majority of laboratories evaluate data with acceptance criteria relative to the nominal concentration of the spiked sample. The rationale for this is that it is not feasible to introduce more stringent criteria for stability evaluations than that of the assay acceptance criterion. Another common approach is to compare data against a baseline concentration (or day zero concentration) of a bulk preparation of stability samples established by repeated analysis, either during the accuracy and precision evaluations, or by other means. This evaluation then eliminates any systematic errors that may have occurred in the preparation of the stability samples. A more statistically acceptable method of stability data evaluations would be to use confidence intervals or perform trend analysis on the data [24]. In this case, when the observed concentration or response of the stability sample is beyond the lower confidence interval (as set a priori), the data indicate a lack of analyte stability under the conditions evaluated. 

Single Fai lure. Safety class SSCs shall be able to accommodate a single failure and still meet their intended safety function, as required, to ensure compliance with the facility acceptance criterion, A "single failure" means an occurrence which results in the loss of capability of a safety class structure, system or component to accomplish its required safety functions. Multiple failures resulting from a single occurrence are considered to be a single fai lure. 

Assuming the methods are reasonable, the last step in evaluating a test report is to look at the results and determine how strongly they demonstrate that the device will not fail. This involves not only the results themselves, but also the acceptance criterion that the manufacturer has defined for the test. For example, a company may report that a mechanical test of their orthopedic implant demonstrated it can withstand 800 Newtons (N) of compressive force without breaking. Well, that is great But what does that number mean By itself, it means very little. To be meaningful, we need at least one other number—an objective acceptance... 

In the second part of the validation we used the predicted values from the model at 12 h for the acceptance criterion for three reproducibility runs. The model predictions were used since they take into account all of the data generated in the first part of the validation. The acceptance criterion for the mean level of stopper moisture was the 99% confidence interval determined... 

At the end of the incubation period the colonies on the plates are enumerated. The number of CFU per gram or per mL of product is calculated for each medium from the arithmetic means of the plates. Because of the relatively poor accuracy and precision of microbiological enumerations, according to the Ph. Eur. an acceptance criterion may be interpreted as follows ... 

Defective units are counted as no-go . Acceptance criterion, c, is c = X = 0 in this case, meaning that the batch is accepted if no defectives are found. For binary data (go, no-go) the probabilities of x = 0,1, 2,. .., n defectives are calculated using the binomial distribution ... 

It is also common to use risk acceptance criterion. If the risk is below a predefined limit, then the risk should be accepted. Giving weight to knowledge, uncertainty and black swans means... 

In first place, couples (CDF, ACDF have to be placed in the corresponding decision region linked to the first acceptance criterion established in RG 1.174 (USNRC). The mean values for the base case, as well as the sensitivity studies, confirms that the changes remain in the appropriate region, including percentiles (Fig. 5). 

According to ICH QIE, an appropriate approach to retest period estimation is to analyze a quantitative attribute (e.g., assay, degradation products) by determining the earliest time at which the 95% confidence limit for the mean intersects the proposed acceptance criterion. For an attribute known to decrease with time, the lower one-sided 95% confidence limit should be compared to the acceptance criterion. For an attribute known to increase with time, the upper one-sided 95% confidence limit should be compared to the acceptance criterion. For an attribute that can either increase or decrease, or whose direction of change is not known, two-sided 95% confidence limits should be calculated and compared to the upper and lower acceptance criteria. If the data show that the batch-to-batch variability is small, it may be worthwhile to combine the data into the overall estimate. The appropriate statistical modeling is used to analyze the data. ... 

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