`standardise()` Error In Rstpm2: Invalid Data Frame

Encountering errors while using statistical packages can be frustrating. This article addresses a specific issue encountered in the rstpm2 package in R, where the standardise() function returns an invalid data frame. We'll break down the problem, provide a reproducible example, and discuss potential causes and solutions. Understanding these issues can greatly improve your experience in survival analysis and multi-state modeling. Let's dive in!

Understanding the Issue with standardise()

The standardise() function in the rstpm2 package is designed to calculate standardized transition probabilities and length of stay estimates from competing risk or multi-state models. However, users may encounter an "invalid data frame" error when applying this function. This error typically arises due to inconsistencies in the number of rows generated during the standardization process, which can be caused by various factors within the model specification or data structure. Specifically, the error message Error in data.frame(): ! arguments imply differing number of rows: 0, 30 indicates that some calculations within the function are resulting in different row counts, leading to a misalignment when creating the final data frame.

To effectively address this issue, it’s crucial to understand how standardise() works and the common pitfalls that can lead to errors. The function relies on underlying models (often built using gsm in this context) and transition matrices to estimate probabilities over time. The complexity of these models, including the number of covariates and the structure of the transition matrix, can introduce potential points of failure. Debugging such issues often involves carefully examining the input data, model specifications, and the intermediate results produced by the function. Furthermore, staying updated with the latest version of the package and consulting package documentation or community forums can provide additional insights and solutions.

Reproducible Example: Demonstrating the Error

To illustrate the error, consider a scenario using the colon dataset from the survival package. This dataset is commonly used in survival analysis and provides a good foundation for demonstrating the issue. Below is a reproducible example that recreates the error:

library(rstpm2)
library(deSolve)

# Use the colon dataset from the survival package as example
colon_df <- survival::colon

# Model for recurrences
recurrence <- gsm(
    Surv(time, status) ~ factor(rx) + sex + age, 
    data = colon_df, 
    subset = (etype == 1), 
    df = 3
)

# Model for death
death <- gsm(
    Surv(time, status) ~ factor(rx) + sex + age, 
    data = colon_df, 
    subset = (etype == 2), 
    df = 3
)

# Competing risk model
cr <- markov_msm(
    list(
        Recurrence = recurrence,
        Death      = death
    ),
    trans = matrix(
        c(
          NA, 1 , 2 ,
          NA, NA, NA,
          NA, NA, NA
        ),
        nrow  = 3,
        ncol  = 3,
        byrow = TRUE
    ),
    # Only include each individual once
    newdata = unique(colon_df[, c("id", "rx", "sex", "age")]),
    t = seq(0, 2500, length = 10)
)

# Obtain standardised estimates.
# This causes the error
standardise(cr)

In this example, we're using the colon dataset to build a competing risk model using rstpm2. The model includes two states: recurrence and death, with transitions defined by the trans matrix. The gsm function is used to model the hazard of each event (recurrence and death), considering factors like treatment (rx), sex, and age. The markov_msm function combines these models into a multi-state model. When standardise(cr) is called, it attempts to calculate standardized estimates, but the differing number of rows error is triggered. This reproducible example encapsulates the issue, making it easier to debug and test potential solutions. It's an essential step in identifying and addressing such problems in statistical modeling.

Dissecting the Error Message

The error message Error in data.frame(): ! arguments imply differing number of rows: 0, 30 provides crucial clues about the nature of the problem. This message specifically indicates that there is a mismatch in the number of rows between different components when the data.frame() function is called internally within standardise(). Essentially, some parts of the calculation are producing zero rows, while others are producing 30 rows, making it impossible to combine them into a single data frame.

The first part of the message, Error in data.frame():, points directly to the function where the error occurs. The data.frame() function is a fundamental R function used to create data frames, which are tabular data structures with rows and columns. The core of the message, ! arguments imply differing number of rows: 0, 30, highlights the root cause: the arguments being passed to data.frame() have inconsistent lengths. In this case, one argument has 0 rows, and another has 30 rows.

This type of error often arises when there are issues in the intermediate calculations performed by standardise(). It suggests that some combination of the model specification, data, or transition matrix is leading to an empty result in one part of the calculation while another part proceeds as expected. For instance, this could happen if a particular transition is impossible under certain covariate combinations or if the time sequence t in markov_msm is not appropriately defined for the data. Understanding this error message is the first step toward debugging the issue, as it narrows down the problem to the creation of the data frame within standardise() and the inconsistent row counts that result.

Potential Causes and Solutions

Several factors could contribute to the "invalid data frame" error when using standardise() in rstpm2. Identifying the specific cause often involves a systematic approach to debugging, examining the model components, data, and function arguments.

1. Model Specification: One common cause is an issue within the model specification, particularly in the transition matrix or the individual models defined using gsm. If the transition matrix (trans) has incorrect entries or defines impossible transitions, it can lead to zero rows in some calculations. Similarly, if the gsm models for specific transitions are misspecified or do not converge, they might produce unexpected results. To address this, carefully review the transition matrix to ensure it accurately reflects the possible transitions in the multi-state model. Also, check the gsm models for any warnings or errors, and verify that the model formulas and data subsets are correctly defined.

2. Data Issues: The data provided to markov_msm can also cause problems. If the newdata argument does not align with the model's expectations, or if there are inconsistencies in the data, it can lead to errors. For example, if certain covariate combinations in newdata make a transition impossible, it might result in zero probabilities for that transition. Ensure that newdata contains all the necessary covariates and that they are correctly formatted. Additionally, check for any missing values or outliers in the data that could affect the model's calculations.

3. Time Sequence (t): The time sequence specified in the t argument of markov_msm is crucial. If the time points are not appropriate for the data's timescale, it can lead to issues. For instance, if the maximum time in t is much larger than the observed event times in the data, some calculations might result in zero events, leading to the row count mismatch. Adjust the t sequence to better match the observed event times and the timescale of the transitions being modeled.

4. Function Arguments: Incorrectly specified function arguments can also be a source of errors. Double-check that all arguments passed to markov_msm and standardise are correctly defined and that they match the function's expectations. This includes ensuring that the list of models passed to markov_msm is in the correct order and that any optional arguments are used appropriately.

5. Package Version: It's also worth considering the package version. Bugs can exist in older versions of software, so updating to the latest version of rstpm2 might resolve the issue. Use `install.packages(