In atorus-research/Tplyr: A Traceability Focused Grammar of Clinical Data Summary
' @section Last Updated By:
' Nathan Kosiba
' @section Last Update Date:
' 2/17/2021
This section contains details of each test executed. Checks verifying each test are included as sub-bullets of their associated test.
-
Setup: No prerequisites required
-
T1: Population data can be specified by the user and treatment variable can be specified
- T1.1: Verify target dataset in table is the same as specified
- T1.2: Verify treatment variable in table is the same as specified
- T2: Population data subset can be specified on user specified conditions
- T2.1: Population data created matches data subset as specified
- T3: Manual groups can be specified by combining different subgroups and header N counts will be specified from these groups within the population data
- T3.1: Population groups can be added by combining existing groups
- T3.2: Header N counts of combined groups match the combined total of the groups
- T4: Analysis data can be specified by the user and treatment variable can be specified for the analysis population
- T4.1: Verify analysis dataset in layer is the same as specified when inherited from table
- T4.2: Verify treatment variable in layer is the same as specified when inherited from table
- T5: Population data and treatment variable can be specified independent of analysis data and treatment variable
- T5.1: Verify population data can be manually specified if not the same as analysis data
- T5.2: Verify analysis data can be manually specified if not the same as population data
- T5.3: Verify population treatment variable can be manually specified if not the same as analysis treatment variable
- T5.4: Verify analysis treatment variable can be manually specified if not the same as population treatment variable
- T6: Analysis data subset can be specified on user specified conditions
- T6.1: Analysis data created matches data subset as specified
- T7: n counts of values within a variable can be produced
- T7.1: Complete data value n counts can be produced within a variable
- T7.2: Distinct data value n counts can be produced within a variable
- T7.3: A text string can be passed in and will be used as the value for counts
- T8: n counts of values within a group of variables can be produced
- T8.1: Complete data value n counts can be produced within a group of variables
- T8.2: Distinct data value n counts can be produced within a group of variables
- T9: counts can be produced using a pair of nested variables and inner levels can be subset with keep levels
- T9.1: two variables with a nested relationship can be used to create counts and inner levels can be subset with keep levels
- T9.2: A text string and a variable can be used to create counts and inner levels can be subset with keep levels
- T10: Total n counts can be added and a sort value can be sepecified by the user
- T10.1: Total n count can be added within a layer and sorted using a specified value
- T11: Missing n count handling can be specified including presentation and denominator handling
- T11.1: Missing n count rows can be added within a layer and sorted using a specified value
- T11.2: Missing values can be excluded from the layer denominator
- T12: Dummy values can be specified for categories that need to be presented but may not exist within the data and missing values can be set to a user specified string
- T12.1: Values specified by user are presented in the output table and total or missing rows can be added
- T13: Counts can be produced as n (%)
- T13.1: When specified, both n and % can be displayed in a n (%) fashion and total counts can be added
- T13.2: Distinct n and % can be displayed in a n (%) fashion and total counts can be added
- T13.3: Distinct and non-distinct n and % can be presented together and total counts can be added
- T14: When producing n (%), the denominator can be specified using the analysis data
- T14.1: Check denominators created match counts from analysis data
- T14.2: Check % produced use denominators matching counts from analysis data
- T15: When producing n (%), the denominator can be specified using a particular manually specified subset
- T15.1: Check denominators created match counts using specified conditions
- T15.2: Check % produced use denominators matching counts using specified conditions
- T16: When producing n (%), the denominator can be specified using the population data
- T16.1: Check % produced use denominators matching counts from population data
- T16.2: Check denominators created match counts from population data
- T17: For shift counts when producing n (%), the denominator can be specified using a grouping of variables
- T17.1: Check % produced use denominators matching counts from grouping variables
- T17.2: Check added total row matches counts using denom by variables
- T18: Risk difference including confidence interval can be produced based on specified treatment groupings and arguments can be passed through to prop.test
- T18.1: Check that risk difference calculated between groupings is correct
- T18.2: Check that confidence interval calculated between groupings is correct
- T18.3: Arguments passed through to prop.test create the correct output
- T19: Risk difference can be calculated over user specified cols arguments
- T19.1: Risk difference estimate and confidence interval can be computed across values of the treatment variable and cols argument
- T20: Risk difference can be calculated over nested count layers and by variables
- T20.1: Risk difference estimate and confidence interval can be computed across values of the treatment variable and nested count layer
- T20.2: Risk difference estimate and confidence interval can be computed across values of the treatment variable and by variable
- T20.3: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer and by variable
- T20.4: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer with static text
- T20.5: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer with static text and by variable
- T21: The descriptive statistics of n, mean, median, IQR, Q1, Q3, standard deviation, variance, min, max, and missing can be produced based on an input variable
- T21.1: Check the computed statistic of n matches the expected value
- T21.2: Check the computed statistic of mean matches the expected value
- T21.3: Check the computed statistic of median matches the expected value
- T21.4: Check the computed statistic of IQR matches the expected value
- T21.5: Check the computed statistic of Q1 matches the expected value
- T21.6: Check the computed statistic of Q3 matches the expected value
- T21.7: Check the computed statistic of standard deviation matches the expected value
- T21.8: Check the computed statistic of variance matches the expected value
- T21.9: Check the computed statistic of min matches the expected value
- T21.10: Check the computed statistic of max matches the expected value
- T21.11: Check the computed statistic of missing matches the expected value
- T22: Custom descriptive statistics can be produced based on an input variable and a specified formula
- T22.1: Check that the computed statistic value matches the value from the specified formula
- T23: Descriptive statistics can be performed across discrete values within a grouping variable or a group of grouping variables
- T23.1: Check the statistic values match the values from the specified grouping variable
- T24: Multiple statistics can be presented in one line
- T24.1: Check that the output can include multiple statistics on the same line
- T25: Decimal precision and integer length can be specified by the user
- T25.1: The output decimal precision and integer length is the same as the user specified values
- T26: Decimal precision and integer length can be dynamically created from analysis data
- T26.1: The output decimal precision and integer length is the same as the decimal precision and integer length from the target data variable
- T27: Presentation format can be specified by the user including desired non-numeric text and align per user specification
- T27.1: The output string is formatted the same as user specification including non-numeric text and alignment
- T28: Descriptive statistic missing values can be set to a user specified string
- T28.1: Missing values can be set to a user specified string
- T29: Shift n counts of values using two variables, a 'from' and a 'to' variable, can be produced
- T29.1: n counts can be created in a shift manner using a from and to variable
- T30: Shift n counts of values within a variable can be produced
- T30.1: n counts can be created in a shift manner using a from and to variable and a by variable
- T31: Shift n counts of values within a group of variables can be produced
- T31.1: n counts can be created in a shift manner using a from and to variable and multiple by variables
- T32: Dummy values for shift counts can be specified for categories that need to be presented but may not exist within the data
- T32.1: Values specified by user for the shift variables are presented in the output table
- T32.2: Values are sorted using the order in the provided factor
- T33: Shift counts can be produced as n (%)
- T33.1: When specified, both n and % can be displayed in a n (%) fashion for shift layer
- T34: For shift counts when producing n (%), the denominator can be specified using the analysis data
- T34.1: Check % produced use denominators matching counts from analysis data
- T35: For shift counts when producing n (%), the denominator can be specified using a particular manually specified subset
- T35.1: Check % produced use denominators matching counts using specified conditions
- T36: For shift counts when producing n (%), the denominator can be specified using the population data
- T36.1: Check % produced use denominators matching counts from population data
- T37: For shift counts when producing n (%), the denominator can be specified using a grouping of variables
- T37.1: Check % produced use denominators matching counts from grouping variables
- T38: Row labels can be manually specified by the user
- T38.1: Check row labels in output table match user specified values
- T39: Row labels can be nested to put a subgroup within a parent group
- T39.1: Check row labels and nesting in output table match user specified values and nesting
- T40: Summaries can be stacked on top of one another
- T40.1: Check multiple summaries mixed between descriptive statistics and count are created they can be stacked
- T41: Summaries can be sorted based on manual sorting by presentation specified order
- T41.1: Check that output table has correct count sorting variables matching specified order
- T42: Summaries can be sorted based on count based sorting (either ascending or descending) by a specified treatment group
- T42.1: Check that output table has correct count sorting variables for count based sorting
- T43: Summaries can be sorted based on alphabetical sorting based on data values
- T43.1: Check that output table has correct count sorting variables for data values
- T44: Summaries can be sorted based on a numeric version of the target variable if available
- T44.1: Check that output table has correct count sorting variables for the corresponding numeric variable
- T45: Summary by variables will be sorted by a numeric variable if available and then by factor
- T45.1: Check that ouput table has correct sorting variables for supplied by variables
- T46: Nested layers can be sorted independently using different methods
- T46.1: Check that when different methods are supplied for nested layers they are applied correctly
- T47: Independent layers can be sorted using different methods and stacked using common sorting variables
- T47.1: Check that when different methods are supplied for independent layers they are applied correctly
- T48: Count layer default formats can be set at the table level
- T48.1: Check that count layer formats set at the table level are applied to layers created
- T48.2: Check that count layer formats applied at the layer level take prescedence over table level formats
- T49: Descriptive statistics layer default formats can be set at the table level
- T49.1: Check that descriptive statistics layer formats set at the table level are applied to layers created
- T49.2: Check that descriptive statistics layer formats applied at the layer level take prescedence over table level formats
- T50: Shift layer default formats can be set at the table level
- T50.1: Check that shift layer formats set at the table level are applied to layers created
- T50.2: Check that shift layer formats applied at the layer level take prescedence over table level formats
- T51: Option for count layer default formats can be specified by the user
- T51.1: Check that the count layer defail formats specified in the option are displayed in the table
- T52: Option for descriptive statistics layer default formats can be specified by the user
- T52.1: Check that the descriptive statistics layer defail formats specified in the option are displayed in the table
- T53: Option for shift layer default formats can be specified by the user
- T53.1: Check that the shift layer defail formats specified in the option are displayed in the table
- T54: Option for a cap on auto precision can be specified by the user
- T54.1: Check that the cap on auto precision specified by the user is displayed correctly in the table for both integers and decimals
- T55: Option for custom descriptive statistics can be specified by the user for use in the table
- T55.1: Check that custom descriptive statistics set in the options can be used and displayed correctly in the table
- T56: Option for setting scipen internal option can be specified by the user
- T56.1: Check that scientific notation supplied is displayed correctly in the table
- T57: Option for setting quantile algorithm choice can be specified by the user
- T57.1: Check that the quantile algorithm supplied is used in table q1 and q3 calculation
- T58: Option for setting IBMRounding can be specified by the user
- T58.1: Check that IBMRounding is not used and SAS rounding is used when option is set to FALSE
- T59: Column headers can be added to the output object
- T59.1: Check that column headers added match those in the output object
- T60: Row breaks can be added between sections based on grouping variables and row labels can be masked in a hierarchical fashion
- T60.1: Check that a row break is added between each section based on the supplied grouping variables and row labels can be masked in a hierarchical fashion
- T61: A table object is returned in a format that is ready to be cosmetically prepared
- T61.1: Check that the table object can be easily cosmetically prepared
- T62: Count layers can process a cols argument and separate population data passed from the table level along with normal count layer processing
- T62.1: Test that n and % results are accurate when the combination of the cols argument and separate population data are applied
- T62.2: Test that risk difference results are accurate when the combination of the cols argument and separate population data are applied
- T62.3: Test that header N values produced are accurate when the combination of the cols argument and separate population data are applied
- T63: Count layers can process a cols argument, separate population data, and a defined subset passed from the table level along with normal count layer processing
- T63.1: Test that n and % results are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T63.2: Test that risk difference results are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T63.3: Test that header N values produced are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T64: Treatment groups from target data will be included in the final table even if the table level subset is too narrow to include them
- T64.1: Test that all treatment groups and cols variable combinations exist in the final table
- T65: When using separate population data treatment groups from target data will be included in the final table even if the table level subset is too narrow to include them
- T65.1: Test that when using separate population data all treatment groups and cols variable combinations exist in the final table
atorus-research/Tplyr documentation built on Feb. 21, 2024, 7:36 p.m.
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' @section Last Updated By:
' Nathan Kosiba
' @section Last Update Date:
' 2/17/2021
This section contains details of each test executed. Checks verifying each test are included as sub-bullets of their associated test.
-
Setup: No prerequisites required
-
T1: Population data can be specified by the user and treatment variable can be specified
- T1.1: Verify target dataset in table is the same as specified
- T1.2: Verify treatment variable in table is the same as specified
- T2: Population data subset can be specified on user specified conditions
- T2.1: Population data created matches data subset as specified
- T3: Manual groups can be specified by combining different subgroups and header N counts will be specified from these groups within the population data
- T3.1: Population groups can be added by combining existing groups
- T3.2: Header N counts of combined groups match the combined total of the groups
- T4: Analysis data can be specified by the user and treatment variable can be specified for the analysis population
- T4.1: Verify analysis dataset in layer is the same as specified when inherited from table
- T4.2: Verify treatment variable in layer is the same as specified when inherited from table
- T5: Population data and treatment variable can be specified independent of analysis data and treatment variable
- T5.1: Verify population data can be manually specified if not the same as analysis data
- T5.2: Verify analysis data can be manually specified if not the same as population data
- T5.3: Verify population treatment variable can be manually specified if not the same as analysis treatment variable
- T5.4: Verify analysis treatment variable can be manually specified if not the same as population treatment variable
- T6: Analysis data subset can be specified on user specified conditions
- T6.1: Analysis data created matches data subset as specified
- T7: n counts of values within a variable can be produced
- T7.1: Complete data value n counts can be produced within a variable
- T7.2: Distinct data value n counts can be produced within a variable
- T7.3: A text string can be passed in and will be used as the value for counts
- T8: n counts of values within a group of variables can be produced
- T8.1: Complete data value n counts can be produced within a group of variables
- T8.2: Distinct data value n counts can be produced within a group of variables
- T9: counts can be produced using a pair of nested variables and inner levels can be subset with keep levels
- T9.1: two variables with a nested relationship can be used to create counts and inner levels can be subset with keep levels
- T9.2: A text string and a variable can be used to create counts and inner levels can be subset with keep levels
- T10: Total n counts can be added and a sort value can be sepecified by the user
- T10.1: Total n count can be added within a layer and sorted using a specified value
- T11: Missing n count handling can be specified including presentation and denominator handling
- T11.1: Missing n count rows can be added within a layer and sorted using a specified value
- T11.2: Missing values can be excluded from the layer denominator
- T12: Dummy values can be specified for categories that need to be presented but may not exist within the data and missing values can be set to a user specified string
- T12.1: Values specified by user are presented in the output table and total or missing rows can be added
- T13: Counts can be produced as n (%)
- T13.1: When specified, both n and % can be displayed in a n (%) fashion and total counts can be added
- T13.2: Distinct n and % can be displayed in a n (%) fashion and total counts can be added
- T13.3: Distinct and non-distinct n and % can be presented together and total counts can be added
- T14: When producing n (%), the denominator can be specified using the analysis data
- T14.1: Check denominators created match counts from analysis data
- T14.2: Check % produced use denominators matching counts from analysis data
- T15: When producing n (%), the denominator can be specified using a particular manually specified subset
- T15.1: Check denominators created match counts using specified conditions
- T15.2: Check % produced use denominators matching counts using specified conditions
- T16: When producing n (%), the denominator can be specified using the population data
- T16.1: Check % produced use denominators matching counts from population data
- T16.2: Check denominators created match counts from population data
- T17: For shift counts when producing n (%), the denominator can be specified using a grouping of variables
- T17.1: Check % produced use denominators matching counts from grouping variables
- T17.2: Check added total row matches counts using denom by variables
- T18: Risk difference including confidence interval can be produced based on specified treatment groupings and arguments can be passed through to prop.test
- T18.1: Check that risk difference calculated between groupings is correct
- T18.2: Check that confidence interval calculated between groupings is correct
- T18.3: Arguments passed through to prop.test create the correct output
- T19: Risk difference can be calculated over user specified cols arguments
- T19.1: Risk difference estimate and confidence interval can be computed across values of the treatment variable and cols argument
- T20: Risk difference can be calculated over nested count layers and by variables
- T20.1: Risk difference estimate and confidence interval can be computed across values of the treatment variable and nested count layer
- T20.2: Risk difference estimate and confidence interval can be computed across values of the treatment variable and by variable
- T20.3: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer and by variable
- T20.4: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer with static text
- T20.5: Risk difference estimate and confidence interval can be computed across values of the treatment variable, nested count layer with static text and by variable
- T21: The descriptive statistics of n, mean, median, IQR, Q1, Q3, standard deviation, variance, min, max, and missing can be produced based on an input variable
- T21.1: Check the computed statistic of n matches the expected value
- T21.2: Check the computed statistic of mean matches the expected value
- T21.3: Check the computed statistic of median matches the expected value
- T21.4: Check the computed statistic of IQR matches the expected value
- T21.5: Check the computed statistic of Q1 matches the expected value
- T21.6: Check the computed statistic of Q3 matches the expected value
- T21.7: Check the computed statistic of standard deviation matches the expected value
- T21.8: Check the computed statistic of variance matches the expected value
- T21.9: Check the computed statistic of min matches the expected value
- T21.10: Check the computed statistic of max matches the expected value
- T21.11: Check the computed statistic of missing matches the expected value
- T22: Custom descriptive statistics can be produced based on an input variable and a specified formula
- T22.1: Check that the computed statistic value matches the value from the specified formula
- T23: Descriptive statistics can be performed across discrete values within a grouping variable or a group of grouping variables
- T23.1: Check the statistic values match the values from the specified grouping variable
- T24: Multiple statistics can be presented in one line
- T24.1: Check that the output can include multiple statistics on the same line
- T25: Decimal precision and integer length can be specified by the user
- T25.1: The output decimal precision and integer length is the same as the user specified values
- T26: Decimal precision and integer length can be dynamically created from analysis data
- T26.1: The output decimal precision and integer length is the same as the decimal precision and integer length from the target data variable
- T27: Presentation format can be specified by the user including desired non-numeric text and align per user specification
- T27.1: The output string is formatted the same as user specification including non-numeric text and alignment
- T28: Descriptive statistic missing values can be set to a user specified string
- T28.1: Missing values can be set to a user specified string
- T29: Shift n counts of values using two variables, a 'from' and a 'to' variable, can be produced
- T29.1: n counts can be created in a shift manner using a from and to variable
- T30: Shift n counts of values within a variable can be produced
- T30.1: n counts can be created in a shift manner using a from and to variable and a by variable
- T31: Shift n counts of values within a group of variables can be produced
- T31.1: n counts can be created in a shift manner using a from and to variable and multiple by variables
- T32: Dummy values for shift counts can be specified for categories that need to be presented but may not exist within the data
- T32.1: Values specified by user for the shift variables are presented in the output table
- T32.2: Values are sorted using the order in the provided factor
- T33: Shift counts can be produced as n (%)
- T33.1: When specified, both n and % can be displayed in a n (%) fashion for shift layer
- T34: For shift counts when producing n (%), the denominator can be specified using the analysis data
- T34.1: Check % produced use denominators matching counts from analysis data
- T35: For shift counts when producing n (%), the denominator can be specified using a particular manually specified subset
- T35.1: Check % produced use denominators matching counts using specified conditions
- T36: For shift counts when producing n (%), the denominator can be specified using the population data
- T36.1: Check % produced use denominators matching counts from population data
- T37: For shift counts when producing n (%), the denominator can be specified using a grouping of variables
- T37.1: Check % produced use denominators matching counts from grouping variables
- T38: Row labels can be manually specified by the user
- T38.1: Check row labels in output table match user specified values
- T39: Row labels can be nested to put a subgroup within a parent group
- T39.1: Check row labels and nesting in output table match user specified values and nesting
- T40: Summaries can be stacked on top of one another
- T40.1: Check multiple summaries mixed between descriptive statistics and count are created they can be stacked
- T41: Summaries can be sorted based on manual sorting by presentation specified order
- T41.1: Check that output table has correct count sorting variables matching specified order
- T42: Summaries can be sorted based on count based sorting (either ascending or descending) by a specified treatment group
- T42.1: Check that output table has correct count sorting variables for count based sorting
- T43: Summaries can be sorted based on alphabetical sorting based on data values
- T43.1: Check that output table has correct count sorting variables for data values
- T44: Summaries can be sorted based on a numeric version of the target variable if available
- T44.1: Check that output table has correct count sorting variables for the corresponding numeric variable
- T45: Summary by variables will be sorted by a numeric variable if available and then by factor
- T45.1: Check that ouput table has correct sorting variables for supplied by variables
- T46: Nested layers can be sorted independently using different methods
- T46.1: Check that when different methods are supplied for nested layers they are applied correctly
- T47: Independent layers can be sorted using different methods and stacked using common sorting variables
- T47.1: Check that when different methods are supplied for independent layers they are applied correctly
- T48: Count layer default formats can be set at the table level
- T48.1: Check that count layer formats set at the table level are applied to layers created
- T48.2: Check that count layer formats applied at the layer level take prescedence over table level formats
- T49: Descriptive statistics layer default formats can be set at the table level
- T49.1: Check that descriptive statistics layer formats set at the table level are applied to layers created
- T49.2: Check that descriptive statistics layer formats applied at the layer level take prescedence over table level formats
- T50: Shift layer default formats can be set at the table level
- T50.1: Check that shift layer formats set at the table level are applied to layers created
- T50.2: Check that shift layer formats applied at the layer level take prescedence over table level formats
- T51: Option for count layer default formats can be specified by the user
- T51.1: Check that the count layer defail formats specified in the option are displayed in the table
- T52: Option for descriptive statistics layer default formats can be specified by the user
- T52.1: Check that the descriptive statistics layer defail formats specified in the option are displayed in the table
- T53: Option for shift layer default formats can be specified by the user
- T53.1: Check that the shift layer defail formats specified in the option are displayed in the table
- T54: Option for a cap on auto precision can be specified by the user
- T54.1: Check that the cap on auto precision specified by the user is displayed correctly in the table for both integers and decimals
- T55: Option for custom descriptive statistics can be specified by the user for use in the table
- T55.1: Check that custom descriptive statistics set in the options can be used and displayed correctly in the table
- T56: Option for setting scipen internal option can be specified by the user
- T56.1: Check that scientific notation supplied is displayed correctly in the table
- T57: Option for setting quantile algorithm choice can be specified by the user
- T57.1: Check that the quantile algorithm supplied is used in table q1 and q3 calculation
- T58: Option for setting IBMRounding can be specified by the user
- T58.1: Check that IBMRounding is not used and SAS rounding is used when option is set to FALSE
- T59: Column headers can be added to the output object
- T59.1: Check that column headers added match those in the output object
- T60: Row breaks can be added between sections based on grouping variables and row labels can be masked in a hierarchical fashion
- T60.1: Check that a row break is added between each section based on the supplied grouping variables and row labels can be masked in a hierarchical fashion
- T61: A table object is returned in a format that is ready to be cosmetically prepared
- T61.1: Check that the table object can be easily cosmetically prepared
- T62: Count layers can process a cols argument and separate population data passed from the table level along with normal count layer processing
- T62.1: Test that n and % results are accurate when the combination of the cols argument and separate population data are applied
- T62.2: Test that risk difference results are accurate when the combination of the cols argument and separate population data are applied
- T62.3: Test that header N values produced are accurate when the combination of the cols argument and separate population data are applied
- T63: Count layers can process a cols argument, separate population data, and a defined subset passed from the table level along with normal count layer processing
- T63.1: Test that n and % results are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T63.2: Test that risk difference results are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T63.3: Test that header N values produced are accurate when the combination of the cols argument, separate population data, and a defined subset are applied
- T64: Treatment groups from target data will be included in the final table even if the table level subset is too narrow to include them
- T64.1: Test that all treatment groups and cols variable combinations exist in the final table
- T65: When using separate population data treatment groups from target data will be included in the final table even if the table level subset is too narrow to include them
- T65.1: Test that when using separate population data all treatment groups and cols variable combinations exist in the final table
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