Calculate abstinence using the timeline followback data in substance research
A Python package to calculate abstinence results using the timeline followback interview data
Install the package using the pip tool. If you need instruction on how to install Python, you can find information at the Python website. The pip tool is the most common Python package management tool, and you can find information about its use instruction at the pypa website.
pip install abstcal
Overview of the Package
This package is developed to score abstinence using the Timeline Followback (TLFB) and visit data in clinical substance use research. It provides functionalities to preprocess the datasets to remove duplicates and outliers. In addition, it can impute missing data using various criteria.
It supports the calculation of abstinence of varied definitions, including continuous, point-prevalence, and prolonged using either intent-to-treat (ITT) or responders-only assumption. It can optionally integrate biochemical verification data.
The Timeline Followback Data (Required)
The dataset should have three columns: id, date, and amount. The id column stores the subject ids, each of which should uniquely identify a study subject. The date column stores the dates when daily substance uses are collected. The amount column stores substance uses for each day.
The Biochemical Measures Dataset (Optional)
The dataset should have three columns: id, date, and amount. The id column stores the subject ids, each of which should uniquely identify a study subject. The date column stores the dates when daily substance uses are collected. The amount column stores the biochemical measures that verify substance use status.
The Visit Data (Required)
It needs to be in one of the following two formats. The long format. The dataset should have three columns: id, visit, and date. The id column stores the subject ids, each of which should uniquely identify a study subject. The visit column stores the visits. The date column stores the dates for the visits.
The wide format. The dataset should have the id column and additional columns with each representing a visit.
Supported Abstinence Definitions
The following abstinence definitions have both calculated as the intent-to-treat (ITT) or responders-only options. By default, the ITT option is used.
- Continuous Abstinence: No substance use in the defined time window. Usually, it starts with the target quit date.
- Point-Prevalence Abstinence: No substance use in the defined time window preceding the assessment time point.
- Prolonged Abstinence Without Lapses: No substance use after the grace period (usually 2 weeks) until the assessment time point.
- Prolonged Abstinence With Lapses: Lapses are allowed after the grace period until the assessment time point.
1. Import the Package
from abstcal import TLFBData, VisitData, AbstinenceCalculator
2. Process the TLFB Data
2a. Read the TLFB data
You can either specify the full path of the TLFB data or just the filename if the dataset is in your current work directory. Supported file formats include comma-separated (.csv), tab-delimited (.txt), and Excel spreadsheets (.xls, .xlsx).
tlfb_data = TLFBData('path_to_tlfb.csv')
2b. Profile the TLFB data
In this step, you will see a report of the data summary, such as the number of records, the number of subjects, and any applicable abnormal data records, including duplicates and outliers. In terms of outliers, you can specify the minimal and maximal values for the substance use amounts. Those values outside of the range are considered outliers and are shown in the summary report.
# No outlier identification tlfb_data.profile_data() # Identify outliers that are outside of the range tlfb_data.profile_data(0, 100)
2c. Drop data records with any missing values
Those records with missing id, date, or amount will be removed. The number of removed records will be reported.
2d. Check and remove any duplicate records
Duplicate records are identified based on id and date. There are different ways to remove duplicates: min, max, or mean, which keep the minimal, maximal, or mean of the duplicate records. You can also have the options to remove all duplicates. You can also simply view the duplicates and handle these duplicates manually.
# Check only, no actions for removing duplicates tlfb_data.check_duplicates(None) # Check and remove duplicates by keeping the minimal tlfb_data.check_duplicates("min") # Check and remove duplicates by keeping the maximal tlfb_data.check_duplicates("max") # Check and remove duplicates by keeping the computed mean (all originals will be removed) tlfb_data.check_duplicates("mean") # Check and remove all duplicates tlfb_data.check_duplicates(False)
2e. Recode outliers (optional)
Those values outside the specified range are considered outliers. All these outliers will be removed by default. However, if the users set the drop_outliers argument to be False, the values lower than the minimal will be recoded as the minimal, while the values higher than the maximal will be recoded as the maximal.
In either case, the number of recoded outliers will be reported.
# Set the minimal and maximal values for outlier detection, by default, the outliers will be dropped tlfb_data.recode_outliers(0, 100) # Alternatively, we can recode outliers by replacing them with bounding values tlfb_data.recode_outliers(0, 100, False)
2f. Impute the missing TLFB data
To calculate the ITT abstinence, the TLFB data will be imputed for the missing records. All contiguous missing intervals will be identified. Each of the intervals will be imputed based on the two values, the one before and the one after the interval.
You can choose to impute the missing values for the interval using the mean of these two values or interpolate the missing values for the interval using the linear values generated from the two values. Alternatively, you can specify a fixed value, which will be used to impute all missing values.
Other important parameters include last_record_action, which defines how you interpolate TLFB records using each subject's last record and maximum_allowed_gap_days, which defines the maximum allowed days for TLFB data imputation. When the missing interval is too large (e.g., 1 year), it's not realistic to interpolate the entire time window
It's also possible to integrate biochemical verification data with the TLFB imputation. The details are discussed later.
# Use the mean tlfb_data.impute_data("uniform") # Use the linear interpolation tlfb_data.impute_data("linear") # Use a fixed value, whichever is appropriate to your research question tlfb_data.impute_data(1) tlfb_data.impute_data(5)
3. Process the Visit Data
3a. Read the visit data
Similar to reading the TLFB data, you can read files in .csv, .txt, .xls, or .xlsx format. It's also supported if your visit dataset is in the univariate format, which means that each subject has only one row of data and the columns are the visits and their dates.
# Read the visit data in the long format (the default option) visit_data = VisitData("file_path.csv") # Read the visit data in the wide format visit_data = VisitData("file_path.csv", "wide")
3b. Profile the visit data
You will see a report of the data summary, such as the number of records, the number of subjects, and any applicable abnormal data records, including duplicates and outliers. In terms of outliers, you can specify the minimal and maximal values for the dates. The dates will be inferred from strings. Please use the format mm/dd/yyyy.
Importantly, it will also detect if any subjects have their visits with the dates that are out of the order. By default, the order is inferred using the numeric or alphabetic order of the visits. These records with possibly incorrect data may result in wrong abstinence calculations.
# No outlier identification visit_data.profile_data() # Outlier identification visit_data.profile_data("07/01/2000", "12/08/2020") # Specify the expected order of the visits visit_data.profile_data(expected_visit_order=[1, 2, 3, 5, 4])
3c. Drop data records with any missing values
Those records with missing id, visit, or date will be removed. The number of removed records will be reported.
3d. Check and remove any duplicate records
Duplicate records are identified based on id and visit. There are different ways to remove duplicates: min, max, or mean, which keep the minimal, maximal, or mean of the duplicate records. The options are the same as how you deal with duplicates in the TLFB data.
# Check only, no actions for removing duplicates visit_data.check_duplicates(None) # Check and remove duplicates by keeping the minimal visit_data.check_duplicates("min") # Check and remove duplicates by keeping the maximal visit_data.check_duplicates("max") # Check and remove duplicates by keeping the computed mean (all originals will be removed) visit_data.check_duplicates("mean") # Check and remove all duplicates visit_data.check_duplicates(False)
3e. Recode outliers (optional)
Those values outside the specified range are considered outliers. The syntax and usage is the same as what you deal with the TLFB dataset
# Set the minimal and maximal, and outliers will be removed by default visit_data.recode_outliers("07/01/2000", "12/08/2020") # Set the minimal and maximal, but keep the outliers by replacing them with bounding values visit_data.recode_outliers("07/01/2000", "12/08/2020", False)
3f. Impute the missing visit data
To calculate the ITT abstinence, the visit data will be imputed for the missing records. The program will first find the earliest visit date as the anchor visit, which should be non-missing for all subjects. Then it will calculate the difference in days between the later visits and the anchor visit. Based on these difference values, the following two imputation options are available. The "freq" option will use the most frequent difference value, which is the default option. The "mean" option will use the mean difference value.
# Use the most frequent difference value between the missing visit and the anchor visit visit_data.impute_data(impute="freq") # Use the mean difference value between the missing visit and the anchor visit visit_data.impute_data(impute="mean") # Specify which visit should serve as the anchor or reference visit visit_data.impute_data(anchor_visit=1)
4. Calculate Abstinence
4a. Create the abstinence calculator using the TLFB and visit data
To calculate abstinence, you instantiate the calculator by setting the TLFB and visit data. By default, only those who have both TLFB and visit data will be scored.
abst_cal = AbstinenceCalculator(tlfb_data, visit_data)
4b. Check data availability (optional)
You can find out how many subjects have the TLFB data and how many have the visit data.
4c. Calculate abstinence
For all the function calls to calculate abstinence, you can request the calculation to be ITT (intent-to-treat) or RO (responders-only). You can optionally specify the calculated abstinence variable names. By default, the abstinence names will be inferred. Another shared argument is whether you want to include the ending date. Notably, each method will generate the abstinence dataset and a dataset logging first lapses that make a subject nonabstinent for a particular abstinence calculation.
To calculate the continuous abstinence, you need to specify the visit when the window starts and the visit when the window ends. To provide greater flexibility, you can specify a series of visits to generate multiple time windows.
# Calculate only one window abst_df, lapse_df = abst_cal.abstinence_cont(2, 5) # Calculate two windows abst_df, lapse_df = abst_cal.abstinence_cont(2, [5, 6]) # Calculate three windows with abstinence names specified abst_df, lapse_df = abst_cal.abstinence_cont(2, [5, 6, 7], ["abst_var1", "abst_var2", "abst_var3"])
To calculate the point-prevalence abstinence, you need to specify the visits. You'll need to specify the number of days preceding the time points. To provide greater flexibility, you can specify multiple visits and multiple numbers of days.
# Calculate only one time point, 7-d point-prevalence abst_df, lapse_df = abst_cal.abstinence_pp(5, 7) # Calculate multiple time points, multiple day conditions abst_df, lapse_df = abst_cal.abstinence_pp([5, 6], [7, 14, 21, 28])
To calculate the prolonged abstinence, you need to specify the quit visit and the number of days for the grace period (the default length is 14 days). You can calculate abstinence for multiple time points. There are several options regarding how a lapse is defined. See below for some examples.
# Lapse isn't allowed abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], False) # Lapse is defined as exceeding a defined amount of substance use abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], '5 cigs') # Lapse is defined as exceeding a defined number of substance use days abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], '3 days') # Lapse is defined as exceeding a defined amount of substance use over a time window abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], '5 cigs/7 days') # Lapse is defined as exceeding a defined number of substance use days over a time window abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], '3 days/7 days') # Combination of these criteria abst_df, lapse_df = abst_cal.abstinence_prolonged(3, [5, 6], ('5 cigs', '3 days/7 days'))
5. Output Datasets
5a. The abstinence datasets
To output the abstinence datasets that you have created from calling the abstinence calculation methods, you can use the following method to create a combined dataset, something like below.
abst_cal.merge_abst_data_to_file([abst_df0, abst_df1, abst_df2], "merged_abstinence_data.csv")
5b. The lapse datasets
To output the lapse datasets that you have created from calling the abstinence calculation methods, you can use the following method to create a combined dataset, something like below.
abst_cal.merge_lapse_data_to_file([lapse_df0, lapse_df1, lapse_df2], "merged_lapse_data.csv")
I. Integration of Biochemical Verification Data
If your study has collected biochemical verification data, such as carbon monoxide for smoking or breath alcohol concentration for alcohol intervention, these biochemical data can be integrated into the TLFB data. In this way, non-honest reporting can be identified (e.g., self-reported of no use, but biochemically un-verified), the self-reported value will be overridden, and the updated record will be used in later abstinence calculation.
The following code shows you a possible work flow. Please note that the biochemical measures dataset should have the same data structure as you TLFB dataset. In other words, it should have three columns: id, date, and amount.
Ia. Prepare the Biochemical Dataset
A key operation to prepare the biochemical dataset is to interpolate extra meaningful records based on the exiting
records using the
interpolate_biochemical_data function, as shown below.
# First read the biochemical verification data biochemical_data = TLFBData("beam_co.csv", included_subjects=included_subjects, abst_cutoff=4) biochemical_data.profile_data() # Interpolate biochemical records based on the half-life biochemical_data.interpolate_biochemical_data(0.5, 1) # Other data cleaning steps biochemical_data.drop_na_records() biochemical_data.check_duplicates()
Ib. Integrate the Biochemical Dataset with the TLFB data
The following code shows you how the integration can be performed. Everything else stays the same, except that in the
impute_data method, you need to specify the
tlfb_data = TLFBData("beam_tlfb.csv", included_subjects=included_subjects) tlfb_sample_summary, tlfb_subject_summary = tlfb_data.profile_data() tlfb_data.drop_na_records() tlfb_data.check_duplicates() tlfb_data.recode_data() tlfb_data.impute_data(biochemical_data=biochemical_data)
II. Calculate Retention Rates
You can also calculate the retention rate with the visit data with a simple function call, as shown below. If a filepath is specified, it will write to a file.
# Just show the retention rates results visit_data.get_retention_rates() # Write the retention rates to an external file visit_data.get_retention_rates('retention_rates.csv')
III. Calculate Abstinence Rates
You can calculate the computed abstinence by providing the list of pandas DataFrame objects.
# Calculate abstinence by various definitions abst_pp, lapses_pp = abst_cal.abstinence_pp([9, 10], 7, including_end=True) abst_pros, lapses_pros = abst_cal.abstinence_prolonged(4, [9, 10], '5 cigs') abst_prol, lapses_prol = abst_cal.abstinence_prolonged(4, [9, 10], False) # Calculate abstinence rates for each abst_cal.calculate_abstinence_rates([abst_pp, abst_pros, abst_prol]) abst_cal.calculate_abstinence_rates([abst_pp, abst_pros, abst_prol], 'abstinence_results.csv')
It will create the following DataFrame as the output. If a filepath is specified, it will write to a file.
|Abstinence Name||Abstinence Rate|
Questions or Comments
If you have any questions about this package, please feel free to leave comments here or send me an email to firstname.lastname@example.org.
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