Title:Analyses and Displays Associated with Outliers or Shifts / WorkingGroup:Standard Scripts for Analysis and Programming
PhUSE
PhUSE Computational Science Development of Standard Scripts for Analysis and Programming Working Group
Analysis and Display White Papers Project Team
Analyses and Displays Associated with Outliers or Shifts from Normal to Abnormal:Focus on Vital Signs, Electrocardiogram, and Laboratory Analyte Measurements in Phase 2-4 Clinical Trials and Integrated Summary Documents
Table of Contents
1. Disclaimer...... 3
2. Notice of Current Edition...... 3
3. Additions and/or Revisions...... 4
4. Overview: Purpose...... 5
5. Scope...... 5
6. Definitions...... 6
7. Problem Statement...... 6
8. Background...... 6
9. Considerations...... 7
10. Recommendations...... 7
10.1. General Recommendation...... 7
10.2. All Measurement Types...... 7
10.3. Laboratory Analyte Measurements...... 11
10.4. ECG Quantitative Measurements...... 13
10.5. Vital Sign Measurements...... 14
11. Tables and Figures for Individual Studies...... 14
11.1. Recommended Displays...... 14
11.2. Discussion...... 24
12. Tables and Figures for Integrated Summaries...... 25
12.1. Recommended Displays...... 25
12.2. Discussion...... 31
13. Example SAP Language...... 32
13.1. Individual Study...... 32
13.2. Integrated Summary...... 34
14. Acknowledgements...... 37
15. Project Leader Contact Information...... 37
16. References...... 38
17. Appendix: Figures and Tables...... 39
List of Tables and Figures
Figure 11.1. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
Low Value: Individual Study...... 16
Figure 11.2. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
High Value: Individual Study...... 18
Figure 11.3. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
Low Value with Change Criteria: Individual Study...... 20
Figure 11.4. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
High Value with Change Criteria: Individual Study...... 22
Table 11.1. Treatment-Emergent Abnormal Summary for Qualitative Safety Measures:
Individual Study...... 24
Figure 12.1. Scatterplot and Shift Summary for Quantitative Safety Measures for Low Value:
Integrated Database...... 27
Figure 12.2. Scatterplot and Shift Summary for Quantitative Safety Measures for High Value:
Integrated Database...... 29
Table 12.1. Treatment-Emergent Abnormal Summary for Qualitative Safety Measures:
Integrated Database...... 31
Table 13.1. Selected Categorical Limits for ECG Data...... 33
Table 13.2. Categorical Criteria for Abnormal Treatment-Emergent Blood Pressure and Pulse
Measurement and Categorical Criteria for Weight and Temperature Changes for Adults...... 34
Figure 17.1. Summary for Quantitative Safety Measures: Individual Study...... 39
Figure 17.2. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
Low Value: Individual Study...... 40
Figure 17.3. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
High Value: Individual Study...... 41
Figure 17.4. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
Low Value with Change Criteria: Individual Study...... 42
Figure 17.5. Scatterplot and Shift Summary for Quantitative Safety Measures Assessing
High Value with Change Criteria: Individual Study...... 43
Figure 17.6. Summary of Common Treatment-Emergent Abnormal for Quantitative Safety
Measures: Individual Study...... 44
Figure 17.7. Scatterplot and Shift Summary for Quantitative Safety Measures: Integrated Database....45
Figure 17.8. Scatterplot and Shift Summary for Quantitative Safety Measures for Low Value:
Integrated Database...... 46
Figure 17.9. Scatterplot and Shift Summary for Quantitative Safety Measures for High Value:
Integrated Database...... 47
Table 17.1. Shift Table Analyses...... 48
Table 17.2. Shift from Normal/High to Low and from Normal/Low to High for Laboratory Measures.....49
Table 17.3. Shift from Normal/High to Low and from Normal/Low to High: Integrated Database...... 50
1. Disclaimer
The opinions expressed in this document are those of the authors and do not necessarily represent the opinions of PhUSE, the members’ respective companies or organizations, or regulatory authorities. The content in this document should not be interpreted as a data standard and/or information required by regulatory authorities.
2. Notice of Current Edition
This edition of the “Analyses and Displays Associated with Outliers or Shifts from Normal to Abnormal:Focus on Vital Signs, Electrocardiogram, and Laboratory Analyte Measurements in Phase 2-4 Clinical Trials and Integrated Summary Documents” is the 1stedition.
3. Additions and/or Revisions
Date / Author / Version / Changes2015-09-10 / See Section 14 / v1.0 / First edition
4. Overview: Purpose
The purpose of this white paper is to provide advice for displaying, summarizing, and/or analyzing measures of outliers or shifts in tables, figures, and listings (TFLs), with a focus on vital signs, electrocardiogram (ECG) quantitative measurements, and laboratory analyte measurements in Phase 2-4 clinical trials and integrated submission documents. This white paper also provides advice on data collection if a particular recommended display requires data to be collected in a certain manner that may differ from current practice. The intent is to begin the process of developing industry standards with respect to analysis and reporting for measurements that are common across clinical trials, and even therapeutic areas. In particular, this white paper provides recommendations for key TFLs for measures of outliers or shifts for a common set of safety measurements. Separate white papers address other types of data or analytical approaches (e.g., central tendency).
The development of standard TFLs and associated analyses will lead to improved standardization from collection through data storage. (You need to know how you want to analyze and report results before finalizing how to collect and store data.) The development of standard TFLs will also lead to improved product lifecycle management by ensuring that reviewers receive the desired analyses for the consistent and efficient evaluation of patient safety and drug effectiveness. Although having standard TFLs is an ultimate goal, this white paper reflects recommendations only and should not be interpreted as “required” by any regulatory agency.
5. Scope
The scope of this white paper is to provide advice when developing the analysis plan for Phase 2-4clinical trials and integrated summary documents(or other documents in which measures of outliers or shifts are of interest).
Although the focus of this white paper pertains to specific safety measurements (vital signs, ECG quantitative measurements, and laboratory analyte measurements), some content may apply to other measurements (e.g., different safety measurements and efficacy assessments). Similarly, although the focus of this white paper pertains to Phase 2-4 clinical trials, some of the content may apply to Phase 1 clinical trials or other types of clinical research (e.g., observational studies).
Detailed specifications for TFLs or dataset development are considered out of scope for this version of this white paper. However, the hope is that specifications and code (utilizing Study Data Tabulation Model [SDTM] and Analysis Data Model [ADaM] data structures) will be developed that are consistent with the concepts outlined in this white paper and placed in the publicly available Pharmaceuticals Users Software Exchange (PhUSE) Standard Scripts Repository.
6. Definitions
ADaM = Analysis Data Model; ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; CBER = Center for Biologics Evaluation and Research; CDASH = Clinical Data Acquisition Standards Harmonization; CDER = Center for Drug Evaluation and Research; CS = Computational Science; ECG = electrocardiogram; LLN = lower limit of normal; PhUSE = Pharmaceuticals Users Software Exchange; SDTM = Study Data Tabulation Model; TFLs = tables, figures, and listings; ULN = upper limit of normal
7. Problem Statement
Industry standards have evolved over time for data collection (Clinical Data Acquisition Standards Harmonization [CDASH]), observed data (SDTM), and analysis datasets (ADaM). However, standards have not been developed for analyses and reports. Lack of standardization leads to inefficiency in operation (time, cost), unreliable quality, and the creation of displays that may not be of optimal use to the reviewers.
8. Background
Industry standards have evolved over time for data collection (CDASH), observed data (SDTM), and analysis datasets (ADaM). There is now recognition that the next step would be to develop standard TFLs for common measurements across clinical trials and across therapeutic areas. Some could argue that the industry should have started with creating standard TFLs prior to creating standards for collection and data storage (consistent with end-in-mind philosophy); however, having industry standards for data collection and the analysis of datasets provides a good basis for creating standard TFLs.
The beginning of the effort leading to this white paper came from the initiation of the FDA/PhUSE Computational Science Collaboration, a yearly conference and ongoing working groups to support addressing computational needs of the industry. The FDA identified key priorities and teamed up with the PhUSE to tackle various challenges using collaboration, crowd sourcing, and innovation (Rosario LA, 2012). The FDA and PhUSE created several Computational Science (CS) working groups to address several of these challenges.The working group, titled “Development of Standard Scripts for Analysis and Programming,” has led the development of this white paper, along with the development of a platform for storing shared code.
There are several existing guidance documents (see bulleted list below) that contain suggested TFLs for common measurements, such as vital signs, ECG quantitative measurements, and laboratory analyte measurements.However, many of these documents are now relatively outdated and generally lack sufficient detail to be used as support for the entire standardization effort.Nevertheless, these documents were used as a starting point in the development of this white paper.The documents include the following:
- ICH E3:Structure and Content of Clinical Study Reports
- Guideline for Industry:Structure and Content of Clinical Study Reports
- Guidance for Industry: Premarketing Risk Assessment
- Reviewer Guidance: Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review
- ICH M4E:The Common Technical Document for the Registration of Pharmaceuticals for Human Use: Efficacy
- ICH E14: The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential For Non-Antiarrhythmic Drugs
- Guidance for Industry: ICH E14 Clinical Evaluation of QT/QTc.Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs
The Reviewer Guidance is considered a key document.As discussed in the guidance, there is generally an expectation that analyses of outliers or shifts are conducted for vital signs, ECG quantitative measurements, and laboratory analyte measurements. The guidance recognizes value to both analyses of central tendency and analyses of outliers or shifts from within reference limits to outside reference limits (below lower reference limit or above upper reference limit).We assume both will be conducted for safety signal detection.This white paper covers the outliers or shifts portion with the expectation that an additional TFL or TFLs will also be created with a focus on central tendency (see the CS white paper pertaining to central tendency).
9. Considerations
Members of the Analysis and Display White Papers Project Team reviewed regulatory guidance and shared ideas and lessons learned from their experience. Draft white papers were developed and posted in the PhUSE wiki environment for public comment.
Most contributors and reviewers of this white paper are industry statisticians, with input from non-industry statisticians (e.g., FDA and academia) and industry and non-industry clinicians.Additional input (e.g., from other regulatory agencies) for future versions of this white paper would be beneficial.
10. Recommendations
10.1. General Recommendation
This section contains some general considerations for the plans of analyses and displays associated with outliers or shifts from normal to abnormal for laboratory analyte measurements, vital signs, and ECG quantitative measurements. Section 10.2 discusses general considerations for all three safety domains. Section 10.3 discusses considerations specific to laboratory analyte measurements. Section 10.4 discusses considerations specific to ECGs quantitative measurements. Section 10.5 discusses considerations specific to the vital signs.
10.2.All Measurement Types
P-values and Confidence Intervals
There has been an ongoing debate about the value for (or lack of value for) the inclusion of p-values and/or confidence intervals in safety assessments (Crowe BJ, 2009).This white paper does not attempt to resolve this debate.As noted in the Reviewer Guidance, p-values or confidence intervals can provide some evidence of the strength of the findings, but unless the trials are designed for the hypothesis testing of safety endpoints, these should be thought of as descriptive.Throughout this white paper, p-values and measures of spread are included in several places.Where these are included, they should not be considered as hypothesis testing.If a company or compound team decides that these are not helpful as a tool for reviewing the data, they can be excluded from the display. Although certain statistical methods are recommended in this white paper for p-values and confidence intervals (for teams that choose to include them), alternative methods can be considered.
Some teams may find p-values and/or confidence intervals useful to facilitate focus but have concerns that a lack of statistical significance provides unwarranted dismissal of a potential signal.Conversely, there are concerns that there could be over-interpretation of p-values due to multiplicity issues, adding potential concern for too many outcomes.Similarly, there are concerns that the lower- or upper-bound of confidence intervals will be over-interpreted.(A percentage can be as high as x causing undue alarm.)It is important for the users of these TFLs to be educated on these issues.
Importance of Visual Displays
Communicating information effectively and efficiently is crucial in detecting safety signals and enabling decisionmaking.Current practice, which focuses on tables and listings, has not always enabled us to communicate information effectively because tables and listings may be long and repetitive, making it difficult to see trends.Graphics, on the other hand, can provide a more effective presentation of complex data, increasing the likelihood of detecting key safety signals and improving the ability to make clinical decisions.They can also facilitate the identification of unexpected values.
Standardized presentation of visual information is encouraged.The FDA/Industry/Academia Safety Graphics Working Group was initiated in 2008 and was formed to develop a wiki and to improve best practice for safety graphics. It has recommendations for the effective use of graphics for three key safety areas: adverse events, ECGs, and laboratory analytes. The working group focused on static graphs, and their recommendations were considered while developing this white paper. In addition, there has also been advancement in interactive visual capabilities.The interactive capabilities are beneficial but are considered out of scope for this version of the white paper.
Conservativeness
The focus of this white paper pertains to clinical trials in which there are comparator data.As such, the concept of “being conservative” is different than when assessing a safety signal within an individual subject or a single arm. A seemingly conservative approach may end up not being conservative in the end.For example, for studies that collect safety data during an off-drug follow-up period, one might consider it conservative to include the adverse events reported in the follow-up period. However, this approach may result in smaller odds ratios than including only the exposed period in the analysis. Another example occurs when choosing cutoffs for shift/outlier analyses. A conservative approach for defining outcomes, from a single-arm perspective, is one that would lead to a higher number of patients reaching a threshold.However, a conservative approach for defining outcomes may actually make it more difficult to identify safety signals with respect to comparing treatment with a comparator (see Section 7.1.7.3.2 in the Reviewer Guidance;U.S. Department of Health and Human Services, 2005b).Thus, some of the approaches recommended in this white paper may appear less conservative than alternatives, but the intent is to propose methodology that can identify meaningful safety signals for a treatment relative to a comparator group.
Measurements After Stopping Study Medication
Measurements collected after stopping medications under study (e.g., treatment under study and comparators) are common for various reasons.In some cases, follow-up phases are included to monitor patients for a period of time after study medication is stopped.In addition, study designs where keeping patients in a study (for the entire planned length of time) after deciding to stop medication early are becoming more popular as newer methods are developed for handling missing data.In these cases, patients can be off study medication for an extended period of time.
Measurements poststudy medication can also arise not by design.For example, a subject can decide to stop study medication at any time, and then later attend the planned visit where the planned measurements are obtained.There is currently no standard approach on how to handle safety assessments post study medication.Some guidance contains advice on how long to collect safety measurements post study medication (e.g., 30 days post or x half-lives).Any advice or decisions related to the collection of safety measurements post study medication should not be confused with how to include such data in displays and/or analyses.It is extremely important to document within the database for analysis the best estimate of the last date study treatment was taken, as well as the dates on which all numerical safety data were collected, so that an accurate determination can be made of time of data collection relative to the last dose of medication.
We recommend that the TFLs in this white paper generally exclude measurements taken during a follow-up phase.Separate TFLs can be created for the follow-up phase and/or the treatment and follow-up phases combined.We also recommend that the TFLs in this white paper exclude measurements taken after the visit, which is considered the “study medication discontinuation” visit.In the study designs that keep patients in a study for the entire planned length of time even after stopping medication, separate TFLs can be created for the “off-medication” time and/or the treatment and off-medication times combined.This enables the researcher to distinguish between drug-related safety signals versus safety signals that could be more related to discontinuing a drug (e.g., return of disease symptoms, introduction of a concomitant medication, and/or discontinuation or withdrawaleffects of the drug) or due to subsequent therapy.