Outcomes in Dose-Finding Trials

Kristian Brock 14 May, 2019

• Introduction
• Data
  • Availability
  • Citing the work
  • File format
• References

Introduction

Dose-finding clinical trials vary the dose of an experimental treatment in search of a dose that is sufficiently tolerable and active. The overwhelming majority of these trials adopt an experimental design that assumes the probabilities of efficacy and toxicity increase monotonically in dose (Rogatko et al. 2007; Chiuzan et al. 2017). To test the empirical appropriateness of this assumption, we collected efficacy and toxicity outcomes by doses from a large number of published dose-finding clinical trials.

Data

The outcomes are collected in the file Database.xlsx. We describe briefly here the volume of data contained.

library(readxl)
library(dplyr)

# The file will eventually move to:
# http://edata.bham.ac.uk/337/1/Database.xlsx
file_path = '../Database.xlsx'

manuscripts <- read_excel(file_path, sheet = 'Manuscripts')
studies <- read_excel(file_path, sheet = 'Studies')
outcomes <- read_excel(file_path, sheet = 'Outcomes')
binary_events <- read_excel(file_path, sheet = 'BinaryOutcomeEvents')
# binary_series <- read_excel(file_path, sheet = 'BinaryOutcomeAnalysisSeries')

manuscripts %>% nrow

## [1] 122

studies %>% nrow

## [1] 139

Data have been extracted from 139 studies in 122 manuscripts.

outcomes %>% nrow

## [1] 96

The database contains data on 96 different outcome measures. However, some of these are very sparsely reported.

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

Outcome	NumObs	NumStudies	ObsPerStudy
Patients with DLT	648	131	4.9
Patients with Objective Response by RECIST	273	54	5.1
Patients with Disease Control by RECIST	223	44	5.1
Patients with Best response SD by RECIST	222	44	5.0
Patients with Best response PD by RECIST	170	36	4.7
Patients with Best response PR by RECIST	170	34	5.0

Overwhelmingly the most commonly reported outcome measure by dose is incidence of DLT. 131 studies report this outcome, with an average of 4.9 datapoints per series. Efficacy outcomes are reported far less frequently but objective response as measured by RECIST (Eisenhauer et al. 2009) is reported in over 50 studies.

Availability

The dataset is available at:

http://edata.bham.ac.uk/337/1/Database.xlsx

Citing the work

To cite the dataset, please use:

Kristian Brock, Victoria Homer, Gurjinder Soul, Claire Potter, Cody Chiuzan and Shing Lee “Dose-level toxicity and efficacy outcomes from dose-finding clinical trials in oncology”. 2019. doi: 10.25500/edata.bham.00000337

or use the BibTex entry

@misc{BrockDoseFindingData,
    author = {Kristian Brock and Victoria Homer and Gurjinder Soul and Claire Potter and Cody Chiuzan and Shing Lee},
    year = {2019},
    title = {Dose-level toxicity and efficacy outcomes from dose-finding clinical trials in oncology},
    doi = {10.25500/edata.bham.00000337},
    howpublished= {\url{https://doi.org/10.25500/edata.bham.00000337}},
    timestamp = {2019.05.05}
}

File format

The database.xlsx file contains many tabs. The following sections describe in depth the format of the file.

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, bool: TRUE if the treatment contains a chemotherapy element. This is provided to address the reasonable expectation that the presence of chemotherapy increases the expectation of toxicity. 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. 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 toxicity.
• 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.

References

Chiuzan, Cody, Jonathan Shtaynberger, Gulam A. Manji, Jimmy K. Duong, Gary K. Schwartz, Anastasia Ivanova, and Shing M. Lee. 2017. “Dose-Finding Designs for Trials of Molecularly Targeted Agents and Immunotherapies.” Journal of Biopharmaceutical Statistics 27 (3): 477–94. https://doi.org/10.1080/10543406.2017.1289952.

Eisenhauer, E. a., P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent, R. Ford, J. Dancey, et al. 2009. “New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1).” European Journal of Cancer 45 (2): 228–47. https://doi.org/10.1016/j.ejca.2008.10.026.

Rogatko, André, David Schoeneck, William Jonas, Mourad Tighiouart, Fadlo R. Khuri, and Alan Porter. 2007. “Translation of Innovative Designs into Phase I Trials.” Journal of Clinical Oncology 25 (31): 4982–6. https://doi.org/10.1200/JCO.2007.12.1012.