Data items

Each of the 122 manuscripts presented the results of at least one dose-finding experiment in humans. Some papers reported the results of more than one experiment.

From each, we extracted:

• characteristic data, including:
  • type of cancer;
  • name and type of experimental therapy;
  • name and type of concommitant therapies;
• outcome data at each dose, including:
  • dose level;
  • number of patients evaluated;
  • number of toxicity events recorded;
  • number of efficacy events recorded.

Data extraction

Data were extracted from papers and recorded on a prior-written standardised form, with one form for each manuscript. The data were then recorded on sheets in an Excel file that was deposited in the University of Birmingham’s data repository. Details on accessing the data are given below.

Data presence

We can load the datasets by running:

source('https://raw.githubusercontent.com/brockk/dosefindingdata/master/Load.R')

The script above hosted on GitHub:

• loads the readxl, httr, dplyr, and stringr packages;
• downloads the raw data file from the data repository;
• joins the raw datasets to create datasets useful for analysis.

We can check the number of manuscripts:

library(dplyr)

manuscripts %>% nrow

## [1] 122

and the number of studies:

studies %>% nrow

## [1] 139

There are more studies than manuscripts because some manuscripts report results from more than one dose-finding experiment.

Characteristic data was available for every manuscript. The clear majority of studies report toxicity outcomes by dose:

studies %>% count(ToxByDose)

## # A tibble: 2 x 2
##   ToxByDose     n
##   <lgl>     <int>
## 1 FALSE         6
## 2 TRUE        133

Substantially fewer report efficacy outcomes by dose:

studies %>% count(EffByDose)

## # A tibble: 2 x 2
##   EffByDose     n
##   <lgl>     <int>
## 1 FALSE        54
## 2 TRUE         85

The five outcomes reported most commonly are:

binary_events %>% 
  left_join(studies, by = 'Study') %>% 
  left_join(outcomes, by = 'OutcomeId') %>% 
  group_by(OutcomeClass, Outcome = OutcomeText) %>% 
  summarise(
    NumObs = n(), 
    NumStudies = length(unique(Study))
    ) %>% 
  mutate(ObsPerStudy = NumObs / NumStudies) %>% 
  arrange(-NumObs) %>% 
  head(5) %>% 
  knitr::kable(digits = 1)

We see that DLT is reported in the great majority of studies. The most commonly-reported efficacy outcome is objective response by RECIST. As described above, this outcome is only relevant in solid tumour settings.

It was frustratingly common that toxicity would be reported by dose-level but efficacy would only be reported for all doses combined. Naturally this impinges our ability to answer the research question.

We found no papers reporting OS or PFS by dose-level.

Fast loading into R

As demonstrated above, the fastest way to get the data loaded into R is to run the command:

source('https://raw.githubusercontent.com/brockk/dosefindingdata/master/Load.R')

However, that runs code I have written on your machine so it requires that you trust me not to turn your computer into a bitcoin-mining drone in my distributed e-wealth empire.

Manuscripts

This tab details the manuscripts studied in this research.

Columns:

• Manuscript, string: Primary key for the manuscript in this project.
• Year, int: Year manuscript was published.
• DOI, string: Manuscript DOI.
• Source, string: How the manuscript came to be in the database. Options are:
  • ChiuzanModelBased, listed in Chiuzan et al. (2017) as a trial using a model-based dose-finding design.
  • ChiuzanRuleBased1, randomly selected from the unpublished list of trials using rule-based dose-finding designs assembled by Chiuzan et al. (2017) during their review.
• SupplementAppendix, bool: TRUE if manuscript has a supplement or appendix.
• DataExtraction1, string: Person who extracted the data.
• DataExtraction2, string: Person who extracted the data a second time or checked the first extraction.
• AddToDB, bool: TRUE if data has been added to the database.
• Note, string: Items noted during data extraction.

Studies

In this database, a Study is an abstract concept encapsulating:

• a set of doses of some treatment or combination of treatments;
• given to patients;
• yielding outcome data.

In a simple scenario, one manuscript would contain one Study. However, there can be multiple Studies in a manuscript. For example, if more than treatment or treatment combination is the subject of dose investigation in a single manuscript, they are seperate studies.

Columns:

• Manuscript, string: Foreign key to Manuscripts, reflecting the manuscript that reported the data.
• Study, string: Primary key for the study in this project.
• PatientGroup, string: brief description of the patient group.
• PatientGroupDetailed, string: more detailed description of the patient group.
• HaemNonhaem, string: Options are:
  • Haematological, if the disease under study was haematological, like leukaemia or lymphoma.
  • NonHaematological, if the disease under study was solid tumour and therefore non-haematological, like lung cancer.
  • Mixed, if both disease types were studied.
  • Unknown, where not specified.
• Treatment, string: brief description of all treatments given, whether dose-varying or not.
• ContainsChemo, bit: 1 if the treatment contains any chemotherapy element, fixed-dose or dose-varying. A full decomposition of treatment types is not provided here (e.g. there is no ContainsInhibitor field) but standardised types of the dose-varying treatment(s) are given in the column DoseVaryingTreatmentType.
• FixedDoseChemo, bit: 1 if the treatment contains a fixed-dose chemotherapy component; 0 if there is no fixed-dose chemo element. Note if chemotherapy is included only as part of the investigative treatment and thus has its dose varied, this field will be 0. This field is provided to address the reasonable expectation that the presence of a standard chemotherapy backbone increases the expectation of toxicity. A special case is made here for chemo to allow an analysis to reflect the reasonably-expected population-level effect that chemotherapy is associated with greater toxicity (and perhaps also response).
• DoseVaryingTreatment, string: the treatment(s) that have their dose varied. In the case of many treatments, items are separated by the + symbol. Any treatment identified in Treatment but not in DoseVaryingTreatment can be assumed to be constant across the doses under investigation.
• DoseVaryingTreatmentType, string: type of the treatment(s) undergoing dose variation. Options are:
  • Cell therapy
  • Chemoprevention
  • Chemotherapy
  • Cytokine
  • GeneTherapy
  • Immunomodulatory drug
  • Inhibitor
  • Monoclonal Antibody
  • Not disclosed
  • Oncolytic virus
  • Radiopharmaceutical
  • Radiotherapy or combinations thereof.
• DoseVaryingTreatmentTypeDetail, string: more detailed and precise description of the type of dose-varying treatment, provided where available.
• MultiVarying, bool: TRUE if the dose of several treatments was varied.
• MonotonicDoses, bool: TRUE if the doses investigated are monotonically increasing.
• DoseUnits, string: the units of the doses.
• MTDorRP2D, string: the dose recommended as the MTD or RP2D.
• ToxByDose, bool: TRUE if toxicity outcomes were reported by dose.
• EffByDose, bool: TRUE if efficacy outcomes were reported by dose.
• AdverseEventLevelCounts, bool: TRUE if adverse events were tabulated by dose for specific events (e.g. Anaemia) in contrast to the broad level (e.g. Grade 3/4 AE).

Outcomes

This tab details the outcome measures collected from the manuscripts.

Columns:

• OutcomeId, int: Primary key for the outcome measure in this project.
• OutcomeText, string: Description of the outcome measure.
• OutcomeClass, string: Class of the outcome measure. Options are:
  • Safety
  • Efficacy
• Include, bool: This may be deprecated.
• HighIsGood, bool: TRUE if a high value is a good thing for patients. FALSE otherwise. E.g. high response rate is good and low toxicity rate is good.
• PerPatientOutcome, bool: TRUE if the outcome measure is binary at the patient level, e.g. “Any AE” is binary at the patient-level because a patient either experiences any AE (in which case it is TRUE) or they do not (in which case it is FALSE). In contrast, “Total AEs” is not binary because a patient may experience many AEs.
• Note, string: Items noted during data extraction.

BinaryOutcomeEvents

This tab contains the data extracted from manuscripts on binary outcome measures.

Columns:

• Study, string: Foreign key to Studies tab.
• Dose, string: Description of dose as reported. In the main, this is just a number, and simple to interpret. In more complicated scenarios, it could contain information reflecting: the frequency that treatments were given; several doses reported together like “10mg - 25mg” (an irritating practice - please do not do this); or doses for several treatments. The bewildering variety in this field is what encouraged us to think about dose-levels (rather than actual doses) in monontonically increasing series.
• OutcomeId, int: Foreign key to Outcomes tab.
• n, int: Number of patients. The denominator in a binary event rate.
• Events, int: number of events.
• Orphaned, bool: TRUE if the dose-level is orphaned and therefore has no unambiguous comparator.

BinaryOutcomeAnalyisSeries

Doses investigated and reported in clinial trials are not always monotonically increasing, despite the fact that they should be under the most commonly-used experimental designs like 3+3 and CRM. Blithely analysing all of the doses as they are reported in publications would sometimes create scenarios where it is impossible to definitively say whether a dose is greater or less than some other (e.g. “5mg per day” vs “10mg every second day”; or “10mg of A + 5mg of B” vs “5mg of A + 10mg of B”).

An analysis-series is a set of doses from a particular study that are strictly monotonically increasing, evaluated with respect to a particular outcome measure. There are many ways to create such subsets. The analysis series presented here are merely those preferred by the author. They favour series with as many doses as possible (whilst still retaining unambiguous monotonic order) and as little repetition as possible. A small amount of repetition has been tolerated where necessary to avoid an “orphaned” dose-level, i.e. a dose-level with no comparator.

You are free to create our own analysis-series if you prefer.

Columns:

• NewSeries, bit: This field exists to automate the generation of AnalysisSeriesId and Order.
• AnalysisSeriesId, int: Primary key for the analysis series. Automatically generated by simple logic in Excel.
• Order, int: The order of the dose in this analysis-series. Automatically generated by simple logic in Excel.
• Study, string: Foreign key to Studies tab.
• Dose, string: Description of dose as reported. In the main, this is just a number, and simple to interpret. In more complicated scenarios, it could contain information reflecting: the frequency that treatments were given; several doses reported together like “10mg - 25mg” (an irritating practice - please do not do this); or doses for several treatments. The bewildering variety in this field is what encouraged us to think about dose-levels (rather than actual doses) in monontonically increasing series.
• OutcomeId, int: Foreign key to Outcomes tab.