FRENCH MAJOR TRAUMA REGISTRYtraumabase.eu
Documentation – Golden Base
FR/EN
TRAUMABASE · GOLDEN BASE

Data usage guide

Interpret missing values and prepare the data for analysis.

This guide explains missing-data markers, parent–child relationships, and import settings for Python and R.

Missing values and analysisImporting in Python and R
01

Guide to missing values

Marker definitions

TraumaBase uses four markers to distinguish different meanings and sources of missingness. Their meaning should inform the analysis and the choice of a missing-data handling strategy, taking the missingness mechanism into account.

ND

Not available (Non disponible)

The information is expected, but was not found in the patient record.

NA

Not applicable

The question or measurement does not apply in this context, often because of the answer to a parent question.

NR

Not collected (Non renseigné)

The variable was not collected in this source or during this period.

IMP

Unobtainable measurement (Imprenable)

A physiological measurement could not be obtained, for example because the signal was too weak or absent.

Handling missingness in analysis

NA: use the clinical context

An imputation based on the clinical meaning of the variable may be justified for NA. When the parent answer establishes that an event or treatment was absent, the expected value may be “No” or 0. For example, if a treatment was not administered, its dose is recorded as NA by default in TraumaBase. In this context, it can be imputed as 0 with reasonable confidence.

Parent–child relationships

In the electronic case report form (eCRF), an answer can open or close another question. The parent variable determines whether the child variable applies.

Parent–child example: Medical transport – Catecholamines

Physician-accompanied transport?

Yes↓Catecholamines: Yes / Noor ND if unavailable
No↓NACatecholamine question not applicable
ND↓NDUnknown information

Imputation example: catecholamine dose

If no catecholamine was administered, the dose is recorded as NA in TraumaBase. The absence of treatment then supports imputing the dose as 0 for analysis.

NA imputation example: cardiac arrest
Clinical contextCardiac arrestNo spontaneous circulation
In the dataHeart rate (FC) : NASystolic blood pressure (PAS) : NADiastolic blood pressure (PAD) : NAMeasurements coded as not applicable in this context
For analysis0 may be usedIf this convention is justified for the variable and time point
View the parent–child dependency map

ND, NR and IMP: statistical methods

For ND, NR and IMP (and the rare cases where NA cannot be imputed using a clinical hypothesis), choose a statistical method suited to the research question and the missingness mechanism. Depending on the context, this may include multiple imputation, estimation methods that account for missing data, and sensitivity analyses.

NR may affect an entire collection period; IMP may be related to the clinical condition.

Suggested resources for handling missing values

R-miss-tastic provides methods, references, tutorials, and examples in R and Python to help choose an appropriate approach to missing data.

02

Technical notes

The Golden Base contains the literal string "NA" for “Not applicable”. Some software automatically converts it to a generic missing value on import, losing this distinction. Preserve all four markers before preparing variables for analysis.

Python / pandas

To import a Golden Base CSV or Excel file, use keep_default_na=False to preserve the literal "NA" marker, and na_values to specify which values to convert to generic missing values. The dtype=str option imports columns as text.

import pandas as pd

na_values = ["", "nan", "NaN"]

df_csv = pd.read_csv(
    "golden_base.csv",
    sep=";",
    keep_default_na=False,
    na_values=na_values,
    dtype=str,
)

df_excel = pd.read_excel(
    "golden_base.xlsx",
    keep_default_na=False,
    na_values=na_values,
    dtype=str,
)

Only empty cells and the strings "nan" and "NaN" become generic missing values. The strings "NA", "ND", "NR", and "IMP" are preserved. dtype=str alone does not prevent "NA" from being converted.

Reference documentation

R

read.csv() also interprets "NA" as a missing value by default: set na.strings without including "NA". For Excel, readxl::read_excel() treats only blank cells as missing by default; the options below make the behaviour explicit and consistent with the pandas example.

df_csv <- read.csv(
  "golden_base.csv",
  sep = ";",
  na.strings = c("", "nan", "NaN"),
  colClasses = "character",
  check.names = FALSE
)

df_excel <- readxl::read_excel(
  "golden_base.xlsx",
  na = c("", "nan", "NaN"),
  col_types = "text"
)

Adjust the delimiter and encoding to the file you received. After handling the markers according to the analysis plan, convert the relevant columns to numbers or dates; these examples deliberately import them as text.

Reference documentation