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CROS NT srl

Contract Research Organisation

Clinical Data Management

Statistics

Dr. Paolo Morelli, CEO

Dr. Luca Girardello, SAS programmer

Best Practice in SAS programs validation. A Case

Study

AGENDAAGENDA

Introduction

Program Verification: a Business Approach

Program Verification: some case studies

FACTS FACTS aboutabout CROS NTCROS NT• Headquarters in Verona (Italy)

• Founded in 1993

• Offices in Milan and Munich

• 40 employees

• Data Management, Statistical, PhV and

hosting services

• Services to Pharma, Biotech and CROs

• Cooperation with Universities of Padua, Bologna, Milan

IntroductionIntroduction

• Topic of the presentation: how to maximize the quality of programming while minimizing the time to verify program.

• In the first part of the presentation we will discuss about thebusiness part:

What is program verification?Why program verification is necessary?When is program verification done?Who performs program verification?How does the verification process work?

• In the second part of the presentation we will discuss about a case study

What is program verificationWhat is program verification

• Making certain that the program does what it is supposed to do, producing a documented evidence of this

Why program verification is necessaryWhy program verification is necessary

• The aim of SAS validation in pharmaceutical research area is that end-users will produce high quality programs that fit the purpose for which they are designed and provide accurate results with a style that they promote:

•Reliabity

•Efficiency

•Portability

•Flexibility

•Ease of use

When is program verification doneWhen is program verification done

• Program verification should performed as soon after the development of the SAS code, before putting the “product” in production

• Development and production environment should be clearly defined;

•Audit trail of program changes should be present as soon the program is released to production

Who performs program verificationWho performs program verification

The SAS programmer who create the code should perform basic testing and follow coding rules, like:

• Error log search

• Warning evaluation

• Comments on critical steps

• Comments on Macro usage

• Details of the SAS program (datetime of creation, SAS programmer name, dataset used, datetime of verification, Name of second SAS programmer, etc)

•It should be emphasized to perform then a program verification by a second SAS programmer

How does the verification process workHow does the verification process work

Biostatistician creates specs then Submits request

SAS developer produces TLGsThen submits verification request

Quality Control programmer verifies results

Interactive Process

Interactive Process

Different Verification ProceduresDifferent Verification Procedures•SOP should define different verification procedures.üIndependent programmingüReviewing resultsüRandom review of results üVisually verify code

•Some of them should mandatory, other optional.

•The Document Containing the programming specs (for example the SAP) should define which approach to follow, illustrating program verification techniques (for example using alternative SAS programming procedures)

•The determination of the level of validation should follow a risk-based model. The key is to determine the effect on the process if the program does not produce the desired result.

Error TypesError Types

• Business strategy should identify common ‘error types’ found in:ü Statistical tablesü Listingsü Graphsü Data analysis filesü Header section of SAS programsü Bad programming specifications

• Metric report related to error type should be analyzed in order to perform preventive action correction

Specific CDISC SDTM Validation specs Specific CDISC SDTM Validation specs ––Metadata LevelMetadata Level

•Verifies that all required variables are present in the dataset

•Reports as an error any variables in the dataset that are not defined in the domain

•Reports a warning for any expected domain variables which are not in the dataset

Specific CDISC SDTM Validation specs Specific CDISC SDTM Validation specs --Metadata LevelMetadata Level

•Notes any permitted domain variables which are not in the dataset

•Verifies that all domain variables are of the expected data typeand proper length

•Detects any domain variables which are assigned a controlled terminology specification by the domain and do not have a format assigned to them

SAS Programming Rules when SAS Programming Rules when validatingvalidating

Ø Emphasizing well commented programs.

Ø Macro in order to use programs repeatedly to verify different programs (re-usability)

Ø Using alternative SAS programming procedures when validating.

Ø Define a workflow if error are identified

How to optimize the processHow to optimize the process

Good specs & Good standards & Good training

= Good programming results

A Case A Case StudyStudy

ExampleExample ofof DerivedDerived DatasetsDatasetsValidationValidation (1/4)(1/4)

PROC COMPARE

Compare original derived datasets

versus validation derived datasets

“Second Programmer” programsall derived datasets

“First Programmer” programsall derived datasets


The COMPARE Procedure Comparison of WORK.LISTING with WORK.VALIDATION (Method=EXACT) Observation Summary Observation Base Compare ID First Obs 1 1 pt=121 First Unequal 79 79 pt=201 Last Unequal 79 79 pt=201 Last Obs 89 89 pt=212 Number of Observations in Common: 89. Total Number of Observations Read from WORK.LISTING: 89. Total Number of Observations Read from WORK.VALIDATION: 89. Number of Observations with Some Compared Variables Unequal: 1. Number of Observations with All Compared Variables Equal: 88.

proc compare base=listing compare=validation listbase listcomp;

id pt; run;

Values Comparison Summary Number of Variables Compared with All Observations Equal: 3. Number of Variables Compared with Some Observations Unequal: 1. Total Number of Values which Compare Unequal: 1. Maximum Difference: 1.

Variables with Unequal Values Variable Type Len Label Ndif MaxDif age NUM 8 AGE (years) 1 1.000 Value Comparison Results for Variables _________________________________________________________ || AGE (years) || Base Compare pt || age age Diff. % Diff _______ || _________ _________ _________ _________ || 201 || 41 40 -1.0000 -2.4390 _________________________________________________________


The COMPARE Procedure Comparison of WORK.LISTING with WORK.VALIDATION (Method=EXACT) Observation Summary Observation Base Compare ID First Obs 1 1 pt=121 Last Obs 89 89 pt=212 Number of Observations in Common: 89. Total Number of Observations Read from WORK.LISTING: 89. Total Number of Observations Read from WORK.VALIDATION: 89. Number of Observations with Some Compared Variables Unequal: 0. Number of Observations with All Compared Variables Equal: 89. NOTE: No unequal values were found. All values compared are exactly equal.


ExampleExample ofof TablesTables ValidationValidation (1/3)(1/3)

“First Programmer” programsall tables applying the set of

layout specifications and saves outputs in Word

“Second Programmer” programsall tables avoiding to add

additional SAS code to controloutput

Compare of outputs

ExampleExample ofof TablesTables ValidationValidation (2/3)(2/3)________________________________________________________________ Tmt A Tmt B ________________________________________________________________ Age (years) n 41 48 Mean (SD) 51.44 (10.39) 52.10 (11.00) Median 55.00 55.00 Min - Max 30.00- 66.00 27.00- 71.00 Gender Female 14 (34.15%) 21 (43.75%) Male 27 (65.85%) 27 (56.25%) ________________________________________________________________

First Programmer -Output in Word

Second programmer -Output SAS

proc means data=demog n mean stddev median min max;

var age; by tmt; run;

________________________________________________________________ Tmt A Tmt B ________________________________________________________________ Age (years) n 41 48 Mean (SD) 51.44 (10.39) 52.10 (11.00) Median 55.00 55.00 Min - Max 30.00- 66.00 27.00- 71.00 Gender Female 14 (34.15%) 21 (43.75%) Male 27 (65.85%) 27 (56.25%) ________________________________________________________________

First Programmer -Output in Word

Second programmer -Output SAS

proc freq data=demog; tables gender*tmt; run;

ExampleExample ofof TablesTables ValidationValidation (3/3)(3/3)

ExampleExample ofof ListingsListings ValidationValidation (1/2)(1/2)“Second Programmer” prints

derived datasets in SAS“First Programmer” programsall listings applying the set of

layout specifications and saves outputs in Word

Compare listing output in Word

versus output in SAS of derived dataset

ExampleExample ofof ListingsListings ValidationValidation (2/2)(2/2)

Listing 1 Demographic Characteristics Subject ID Gender Age Race _______________ _______ ____ _____ 121 M 50 3 122 M 34 3 123 F 58 3 124 M 64 3 125 M 57 3 126 F 64 3 127 M 39 3 128 M 55 2 129 M 41 3 130 M 44 3 131 M 32 3 132 M 37 3 133 M 61 3 134 F 56 3 135 M 34 3 136 M 34 3

Listing Output in Word Print of Derived Dataset

ExampleExample ofof RegistrationRegistration ErrorsErrors

Programming41%

Specification14%

Layout45%

MetricsMetrics on on ProgrammingProgramming ErrorsErrors

Selection of Variables

14%

Calculation of variables

20%

SAS Programming

66%

Specification not detailed

40%

Wrong interpretation

of specification

60%

Output Writing

56%

Output Structure

30%

Display Variables

14%

ExamplesExamples ofof ErrorsErrors

• Layout

Writing of a note in tableIncorrect: “Percentages are calculated number of patients”

Correct: “Percentages are calculated on number of patients”


data age; set demog; if age<20 then age_c=1; else if 20<age<40 then age_c=2; else if age>=40 then age_c=3; run;

• Programming

data age; set demog; if age<20 then age_c=1; else if 20<=age<40 then age_c=2; else if age>=40 then age_c=3; run;


• Wrong interpretation of specification

Note of a table (in SAP):“Note 1: Only patients with all value for primary analysis are included in the table.”

In SAS Program:In the table, all patients are included

ThankThank youyou forfor youryour attentionattention

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