Evaluation of Out of Trend (OOT) Results

What is Out-of-Trend (OOT)?

The analytical results are within the specification but not inside the expected trend related to the initial result or anticipated result. OOT specifies that there may be something incorrect with the analysis or manufacturing process. If the assay of 2nd month stability study found 95.5% (Specification: 105. 0 % to 95.0 %) from the earlier trend results e.g. Initial Result: 99.5 %, 1st month Result: 99.0 %, 2nd month Result: 95.5 % and 3rd month result: 98.8 % then 95.5 % result is called as out-of-trend result.

Probable Sources for OOT?

OOT can be may be due to an assignable reason belonging to analytical laboratory errors and no assignable reason not due to analytical laboratory error. During the stability testing of the sample, any stability study result not found inside the anticipated trend as compared to the initial time point result or over the period of a particular time point stability study analysis is an out-of-trend result. This may be due to the following reasons:

  1. Sampling error i.e. not a uniform or representative batch sample,
  2. Analyst errors, such as STD or sample weighing error, dilution error, wrong sample selection, improper sample handling, unskilled analyst,
  3. STP is not followed properly,
  4. Instrument or equipment errors.
  5. Instrument or equipment calibration error,
  6. Inappropriate packing,
  7. Storage condition is not proper,
  8. Change in the vendor or lot number of crucial raw materials,
  9. Errors during the manufacturing process,
  10. SOP was not followed accurately.

In addition to the above evidence, it’s an alarming sign for the product which may cause Out-of-specification (OOS) and further investigation of the analytical result.      

Detection of OOT Results?

The OOT results are to be detected by the systematic and experimental statistical method. To detect the OOT results it’s important to ensure that the obtained result is proper. The guideline regarding the pre-approval of stability studies is recommended by ICH guideline Q1A which specifies how to perform the stability studies and evaluation of stability data will be done as per ICH Q1E arithmetic assistance shelf life can be established. The post-approval stability study is conducted by using the same guidelines only once a year. 

Following are different approaches illustrated to detect the OOT instability results.

  1. Recommended regression control chart method: As per the Newhart regression control chart, the criterion of the true regression line and the residual difference is supposed to be known. This initial approach is to estimate a regression control chart from data within or between the batches.
  2. Modified regression control chart method for dissimilar ANCOVA models: In this method by using the earlier batches data regression control chart criterion get less unpredictable. The advantage of this method is that slopes are equated so that they can be calculated although entire batches behave similarly.
  3. By time point method: This method is supposed to usual distribution and that all observations at an assumed time point are independent. By time point approach is employed to decide whether a result is inside the assumption on the basis of knowledge from other batches analyzed at a similar stability time point.
  4. Multivariate jackknife distances: This method is susceptible to the estimation of OOT time points in a regression study. As per this method, each time point of analysis will be included and removed to assess its impact on the model.

Identification of OOT results during stability study is a very much important activity in the Pharmaceutical industry. There is no proper guidance is available for the assessment of OOT results. In this blog, various approaches were illustrated for OOT detection. Preferably, the approach selected for the estimation of OOT should be simple and easy to implement.


  1. Guidance for Industry Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production, U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Rockville, MD,
  2. International Conference on Harmonization, ICH Q1A (R2): Stability testing of new drug substances and products,
  3. International Conference on Harmonization, ICH Q1E: Evaluation for stability data,
  4. US v Barr Laboratories, 812 F. Supp. 458 (District Court of New Jersey 1993)
  5. J.F. Lawless, Statistical Models, and Methods for Lifetime Data (John Wiley & Sons, New York).
  6. FDA, 21 CFR Part 11: Electronic Records, Electronic Signatures. PT

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