dtrackr - Consort statement example

CONSORT statement

CONSORT diagrams are part of the requirements in reporting parallel group clinical trials or case control designs in observational studies. They are described in the updated 2010 CONSORT statement (Schulz, Altman, and Moher 2010). They clarify how patients were recruited, selected, randomised and followed up. For observational studies an equivalent requirement is the STROBE statement (von Elm et al. 2008). There are other similar requirements for other types of study such as the TRIPOD statement that are applicable for multivariate models (Collins et al. 2015).

For this demonstration we use the cgd data set from the survival package (Terry M. Therneau and Patricia M. Grambsch 2000; Therneau 2022), which is from a placebo controlled trial of gamma interferon in Chronic Granulomatous Disease.

In this example the treatment column contains the intervention.

For the analysis we are considering only the first observations for each patient, and our study criteria are as follows:

This can be coded into the dplyr pipeline, with additional dtrackr functions:

# Some useful formatting options
old = options(
  dtrackr.strata_glue="{tolower(.value)}",
  dtrackr.strata_sep=", ",
  dtrackr.default_message = "{.count} records",
  dtrackr.default_headline = NULL
)

demo_data = survival::cgd %>% 
  track() %>%
  filter(enum == 1, .type="inclusion", .messages="{.count.out} first observation") %>%
  include_any(
    hos.cat == "US:NIH" ~ "{.included} NIH patients",
    hos.cat == "US:other" ~ "{.included} other US patients"
  ) %>%
  group_by(treat, .messages="cases versus controls") %>%
  comment() %>%
  capture_exclusions() %>%
  exclude_all(
    age<5 ~ "{.excluded} subjects under 5",
    age>35 ~ "{.excluded} subjects over 35",
    steroids == 1 ~ "{.excluded} on steroids at admission"
  ) %>%
  comment(.messages = "{.count} after exclusions") %>%
  status(
    mean_height = sprintf("%1.2f \u00B1 %1.2f",mean(height),sd(height)),
    mean_weight = sprintf("%1.2f \u00B1 %1.2f",mean(weight),sd(weight)),
    .messages = c(
      "average height: {mean_height}",
      "average weight: {mean_weight}"
    )                    
  ) %>%
  ungroup(.messages = "{.count} in final data set")

# restore to originals
options(old)

With a bit of experimentation the flowchart needed for a STROBE/CONSORT checklist can be generated. One option to output the flowchart is svg which can then be manually formatted as required, but for publication ready output pdf is usually preferred.

demo_data %>% flowchart()
%0 12:s->14 13:s->14 10:s->12 11:s->13 6:s->10 6:e->8 7:s->11 7:e->9 4:s->6 5:s->7 3:s->4 3:s->5 2:s->3 1:s->2 14 74 in final data set 12 placebo average height: 145.58 ± 29.10 average weight: 45.46 ± 23.20 13 rifn-g average height: 143.14 ± 25.12 average weight: 40.70 ± 20.19 10 placebo 36 after exclusions 11 rifn-g 38 after exclusions 6 placebo 43 records 7 rifn-g 46 records 8 placebo 7 subjects under 5 0 subjects over 35 0 on steroids at admission 9 rifn-g 4 subjects under 5 3 subjects over 35 1 on steroids at admission 4 cases versus controls 5 cases versus controls 3 inclusions: 26 NIH patients 63 other US patients 2 128 first observation 1 203 records

Excluded data

During this pipeline, we may be keen to understand why certain data items are being rejected. This would enable us to examine the source data, and potentially correct it during the data collection process. We’ve used it to allow continuous quality checks on the data to feed back to the data curators, as we regularly conduct analyses. By tracking the exclusions, not only do we track the data flow through the pipeline we also retain all excluded items, with the reason for exclusion. Thus we can reassure ourselves that the exclusions are as expected. We enabled this by calling capture_exclusions() in the pipeline above. Having tracked the exclusions we can retrieve them by calling excluded() which gives a data frame with the excluded records and the reasons. If the exclusions happened over multiple stages as the dataframe format change in between then this will be held as a nested dataframe (i.e. see ?tidyr::nest):


# here we filter out the majority of the actual content of the excluded data to focus on the 
# metadata recovered during the exclusion.
demo_data %>% excluded() %>% select(.stage,.message,.filter,age, steroids)
#> # A tibble: 15 × 5
#>    .stage  .message                   .filter       age   steroids
#>    <chr>   <glue>                     <chr>         <chr> <chr>   
#>  1 stage 1 7 subjects under 5         age < 5       2     0       
#>  2 stage 1 7 subjects under 5         age < 5       1     0       
#>  3 stage 1 7 subjects under 5         age < 5       1     0       
#>  4 stage 1 7 subjects under 5         age < 5       1     0       
#>  5 stage 1 7 subjects under 5         age < 5       1     0       
#>  6 stage 1 7 subjects under 5         age < 5       4     0       
#>  7 stage 1 7 subjects under 5         age < 5       3     0       
#>  8 stage 1 4 subjects under 5         age < 5       3     0       
#>  9 stage 1 4 subjects under 5         age < 5       4     0       
#> 10 stage 1 4 subjects under 5         age < 5       1     0       
#> 11 stage 1 4 subjects under 5         age < 5       4     0       
#> 12 stage 1 3 subjects over 35         age > 35      44    0       
#> 13 stage 1 3 subjects over 35         age > 35      38    0       
#> 14 stage 1 3 subjects over 35         age > 35      37    0       
#> 15 stage 1 1 on steroids at admission steroids == 1 6     1

This list may have multiple entries for a single data item, if for example something is excluded in any one step for many reasons.

Tagging the pipeline

For reporting results it is useful to have the numbers from the flowchart to embed into the text of the results section of the write up. Here we show the same pipeline as above, but with 4 uses of the .tag system for labelling part of the pipeline. This captures data in a tag-value list during the pipeline, and retains it as metadata for later reuse.


demo_data = survival::cgd %>% 
  track(.messages = NULL) %>%
  filter(enum == 1, .type="inclusion", .messages="{.count.out} first observation") %>%
  comment(.tag = "initial cohort") %>%
  #         ^^^^^^^^^^^^^^^^^^^^^
  #         TAGS DEFINED
  
  include_any(
    hos.cat == "US:NIH" ~ "{.included} NIH patients",
    hos.cat == "US:other" ~ "{.included} other US patients"
  ) %>%
  group_by(treat, .messages="cases versus controls") %>%

  comment(.tag="study cohort") %>%
  #       ^^^^^^^^^^^^^^^^^^^
  #       SECOND SET OF TAGS DEFINED
  
  capture_exclusions() %>%
  exclude_all(
    age<5 ~ "{.excluded} subjects under 5",
    age>35 ~ "{.excluded} subjects over 35",
    steroids == 1 ~ "{.excluded} on steroids at admission"
  ) %>%
  
  comment(.messages = "{.count} after exclusions") %>%
  
  status(
    mean_height = sprintf("%1.2f \u00B1 %1.2f",mean(height),sd(height)),
    mean_weight = sprintf("%1.2f \u00B1 %1.2f",mean(weight),sd(weight)),
    .messages = c(
      "average height: {mean_height}",
      "average weight: {mean_weight}"
    ),
    .tag = "qualifying patients"
  #  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  #  THIRD SET TAGS DEFINED                    
  ) %>%
  ungroup(.messages = "{.count} in final data set", .tag="final set")
  #                                                 ^^^^^^^^^^^^^^^^
  #                                                 LAST TAGS DEFINED

The tagged data can be retrieved as follows, which will give you all tagged data for all 4 points in the pipeline:

demo_data %>% tagged() %>% tidyr::unnest(.content)
#> # A tibble: 6 × 7
#>   .tag                .count .total .strata         treat   mean_height  mean_…¹
#>   <chr>                <int>  <int> <chr>           <fct>   <chr>        <chr>  
#> 1 initial cohort         128    128 ""              <NA>    <NA>         <NA>   
#> 2 study cohort            43     89 "treat:placebo" placebo <NA>         <NA>   
#> 3 study cohort            46     89 "treat:rIFN-g"  rIFN-g  <NA>         <NA>   
#> 4 qualifying patients     36     74 "treat:placebo" placebo 145.58 ± 29… 45.46 …
#> 5 qualifying patients     38     74 "treat:rIFN-g"  rIFN-g  143.14 ± 25… 40.70 …
#> 6 final set               74     74 ""              <NA>    <NA>         <NA>   
#> # … with abbreviated variable name ¹​mean_weight

More often though you will want to retrieve specific values from specific points for the results text for example:

initialSet = demo_data %>% tagged(.tag = "initial cohort", .glue = "{.count} patients")
finalSet = demo_data %>% tagged(.tag = "final set", .glue = "{.count} patients")

# there were `r initialSet` in the study, of whom `r finalSet` met the eligibility criteria.

For example there were 128 patients in the study, of whom 74 patients met the eligibility criteria.

More complex formatting and calculations are made possible by use of the glue specification, including those that happen on a per group basis, and we can also pull in values from elsewhere in our analysis.

demo_data %>% tagged(
    .tag = "qualifying patients", 
    .glue = "{.strata}: {.count}/{.total} ({sprintf('%1.1f', .count/.total*100)}%) patients on {sysDate}, with a mean height of {mean_height}", 
    sysDate = Sys.Date()
    # we could have included any number of other parameters here from the global environment
  ) %>% dplyr::pull(.label)
#> treat:placebo: 36/74 (48.6%) patients on 2023-03-24, with a mean height of 145.58 ± 29.10
#> treat:rIFN-g: 38/74 (51.4%) patients on 2023-03-24, with a mean height of 143.14 ± 25.12

Sometimes it will be necessary to operate on all tagged content at once. This is possible but be aware that the content available depends somewhat on where the tag was set in the pipeline so not all fields will always be present (although .count and .total will be). The .total is the overall number of cases at that point in the pipeline. .count is the number of cases in each strata.

demo_data %>% tagged(.glue = "{.count}/{.total} patients")
#> # A tibble: 6 × 3
#>   .tag                .strata         .label          
#>   <chr>               <chr>           <glue>          
#> 1 initial cohort      ""              128/128 patients
#> 2 study cohort        "treat:placebo" 43/89 patients  
#> 3 study cohort        "treat:rIFN-g"  46/89 patients  
#> 4 qualifying patients "treat:placebo" 36/74 patients  
#> 5 qualifying patients "treat:rIFN-g"  38/74 patients  
#> 6 final set           ""              74/74 patients

For comparing inclusions and exclusions at different stages in the pipeline using tags the following example may be useful:

demo_data %>% 
  tagged() %>%   # selects only top level content
  tidyr::unnest(.content) %>% 
  dplyr::select(.tag, .total) %>% 
  dplyr::distinct() %>%
  tidyr::pivot_wider(values_from=.total, names_from=.tag) %>% 
  glue::glue_data("Out of {`initial cohort`} patients, {`study cohort`} were eligible for inclusion on the basis of their age
  but {`study cohort`-`qualifying patients`} were outside the age limits. 
  This left {`final set`} patients included in the final study (i.e. overall {`initial cohort`-`final set`} were removed).")
#> Out of 128 patients, 89 were eligible for inclusion on the basis of their age
#> but 15 were outside the age limits. 
#> This left 74 patients included in the final study (i.e. overall 54 were removed).

Reusable functions

Composing dtrackr inclusion and exclusion criteria into functions lets you reuse them across different studies, but to be useful these functions need to be parameterised. As dtrackr already uses formulae as part of the input to construct the formulae for dtrackr from a helper function requires injection support, using rlang::inject(). This is an advanced R topic but the following example gives a sense of what is possible.


# This is a reusable function to restrict ages
age_restrict = function(df, age_col, min_age = 18, max_age = 65) {
  age_col = rlang::ensym(age_col)
  message = sprintf("{.included} between\n%d and %d years", min_age, max_age)
  dtrackr::include_any(df,
    # injection support for parameters must be made explicit using
    # rlang::inject in any functions using include_any or exclude_all
    rlang::inject(min_age <= !!age_col & max_age >= !!age_col ~ !!message)
  )
}

survival::cgd %>% 
  # the `age` column is in 
  age_restrict(age, max_age = 30) %>%
  # demonstrating that this works in 2 stages
  age_restrict(age, min_age = 20) %>% 
  flowchart()
%0 1:s->2 2 inclusions: 43 between 20 and 65 years 1 inclusions: 52 between 18 and 30 years

References

Collins, Gary S., Johannes B. Reitsma, Douglas G. Altman, and Karel GM Moons. 2015. “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.” BMC Medicine 13 (1): 1. https://doi.org/10.1186/s12916-014-0241-z.
Elm, Erik von, Douglas G Altman, Matthias Egger, Stuart J Pocock, Peter C Gøtzsche, Jan P Vandenbroucke, and STROBE Initiative. 2008. “The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies.” Journal of Clinical Epidemiology 61 (4): 344–49.
Schulz, Kenneth F., Douglas G. Altman, and David Moher. 2010. “CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials.” BMJ 340 (March): c332. https://doi.org/10.1136/bmj.c332.
Terry M. Therneau, and Patricia M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. New York: Springer.
Therneau, Terry M. 2022. A Package for Survival Analysis in r. https://CRAN.R-project.org/package=survival.