nextdose: Estimates the updated model and next dose to be tested for...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

This function incorporates pseudodata created in pseudodata with the collected cohorts of patient information and updates the fitted model selected (choice of POM, CR, or binary 2-parameter logistic model). Using this newly updated model, it then re-estimates the next dose to be tested for a specified target dose limiting toxicity (DLT) rate. It has the flexibility to add in various safety constraints and can accommodate continuous or discrete doses. This function also has to option to combine toxicity grades 0 and 1 as specified by CTCAEv4.0 into one category should clinical investigators wish to do so.

Usage

1
2
3
4
5
6
nextdose(design, pseudotox, pseudodose, collectedtox, collecteddose,
  cohortsize = 3, targetDLT = 0.3, pseudoweights = NA, discrete = FALSE,
  discretedoses = NA, rounddown = FALSE, numberdltrule = NA,
  lowerlimitrule = NA, upperlimitrule = NA, dltrule = NA,
  increaserule = NA, minimum = NA, maximum = NA, combine01 = FALSE,
  plotit = TRUE)

Arguments

design

Specifies which dose finding design you are running simulations on. Choices are: POM, CR, or CRM.

pseudotox

A vector of toxicity grades specified by the pseudodata.

pseudodose

A vector of dose levels specified by the pseudodata.

collectedtox

A vector of treated patient toxicity outcomes. For j patients treated up to this point in the trial, collecteddose is in the form: c(toxicity grade patient 1, ..., toxicity grade patient j).
If combine01 is false, toxicity grades range from 0, 1, 2, 3, and 4. If combine01 is true, toxicity grades are coded 0 (grades 0 and 1), 1 (grade 2), 2 (grade 3), and 3 (grade 4). Toxicity grades are specified according to CTCAEv4.0.

collecteddose

A vector of treated patient dose levels. For j patients treated up to this point in the trial, collecteddose is in the form: c(dose patient 1, ..., dose patient j).

cohortsize

Number of patients treated in a cohort. Defaults to 3.

targetDLT

Target dose limiting toxicity (DLT) rate pre-specified by clinical investigators prior to the start of the trial. Must be specified between 0 and 1. Defaults to 0.30.

pseudoweights

Pseudoweights determine the amount of influence the pseudodata has on estimating the updated model. Please refer to Van Meter et al (2010) for more information. We suggest setting the pseudoweights equal to the cohortsize. When pseudoweights = X, the total pseudodata represent X individuals in the updated model. If not specified, pseudoweights defaults to cohortsize. Must be specified between 0 and 1. Defaults to 0.30.

discrete

True/False. If discrete = TRUE this allows for discrete dose levels to be specified prior to the start of the trial. Defaults to FALSE.

discretedoses

Specified discrete dose levels if desired. They must be specified if discrete is equal to TRUE. It is written for j dose levels as: c(d1, d2,..., dj).

rounddown

True/False. Only applicable when using discrete dose levels.
If rounddown = TRUE, the estimate dose from specified model will round down to the more conservative discrete dose level.
If rounddown = FALSE, it will select the discrete dose closest to the estimated model selection. Defaults to FALSE.

numberdltrule

Number of dose limiting toxicities (DLTs) that would be considered excessively unsafe if observed in a cohort of patients. This would prompt the DLT constraint if there is one specified. Defaults to NA.

lowerlimitrule

If lowerlimitrule is specified, this constraint will stop the trial if the dose is estimated to be under a certain level as a safety precaution. Investigators may feel any dose under this lowerlimitrule would suggest excessive toxicity with this investigational drug and no true MTD may exist.
This can be specified as a dose level or percentage.
If lowerlimitrule <1 (percentage), it takes the range of possible data (maximum - minimum) and makes the lower bound when the trial would stop early as: minimum + (maximum - minimum)*lowerlimitrule.
If lowerlimitrule >1 (dose level) then the lower bound when the trial would stop early is the lowerlimitrule.

upperlimitrule

If upperlimitrule is specified, this constraint will stop the trial if the dose is estimated to be higher than a certain level.
If upperlimitrule <1 (percentage), it takes the range of possible data (maximum - minimum) and makes the upper bound when the trial would stop early as: maximum - (maximum - minimum)*upperlimitrule.
If upperlimitrule >1 (dose level) then the upper bound when the trial would stop early is the upperlimitrule.

dltrule

If the numberdltrule (i.e. 2 DLTs) occurs in the last cohort of patients tested, the next estimated dose must decrease by an amount specified by the dltrule. Defaults to NA.
If 0 < dltrule < 1 (percentage), with excessive DLTs in the last cohort, the next estimated dose must decrease by the dltrule (percentage) of the last tested dose level, i.e. (1-dltrule)*lastdose.
If dltrule >= 1 (dose level), with excessive DLTs in the last cohort the next estimated dose must decrease by dltrule dose level, i.e. lastdose-dltrule.

increaserule

If increaserule is specified, then the next estimated dose can only increase by an amount specified by the increaserule between tested cohorts. Defaults to NA.
If 0 < increaserule < 1 (percentage), the next dose can only increase to a maximum of lastdose*(1+increaserule).
If increaserule >= 1 (dose level), the next dose can only increase to a maximum of lastdose + increaserule.

minimum

Minimum dose that will be considered to test patients. Must be specified if lowerlimitrule<1.

maximum

Maximum dose that will be considered to test patients. Must be specified if upperlimitrule<1.

combine01

True/False. If combine01 = TRUE, toxicity grades 0 and 1 are combined into 1 category. Therefore all toxicities must be coded: 0 (grades 0 and 1), 1 (grade2), 2 (grade 3), and 3 (grade 4) according to CTCAEv4.0. Defaults to FALSE.

plotit

True/False. If true, returns a plot of the updated model with the next dose to test in a cohort identified as well as the current accrued patient toxicity outcomes. Defaults to TRUE.

Details

If using a POM CRM, this function assumes a proportional odds model as described by Harrell (Harrell, 2001). For combine01=FALSE, y equal to toxicity grade outcomes j in c(0, 1, 2, 3, 4) as specified by CTCAEv4.0, and x equal to the dose, this is written in the form:

P(y>=j|x)=1/(1+exp(-(α_j + β*x))),for j = 1, 2, 3, 4


For combine01=Ture, toxicity grades are now 0/1, 2, 3, and 4, and y is recoded as 0 = grades 0 and 1, 1 = grade 2, 2 = grade 3, and 3 = grade 4.

If using a CR model design, this function assumes a continuation ratio model as described by Harrell (Harrell, 2001). For combine01=FALSE, y equal to toxicity grade outcomes j in c(0, 1, 2, 3, 4) as specified by CTCAEv4.0, and x equal to the dose, this is written in the form:

P(y=j|y>=j,x)=1/(1+exp(-(α + θ_j + γ*x))), j = 0, 1, 2, 3


For combine01=TURE, toxicity grades are now 0/1, 2, 3, and 4, and y is recoded as 0 = grades 0 and 1, 1 = grade 2, 2 = grade 3, and 3 = grade 4.

If using a binary CRM, this assumes a standard 2-parameter logistic model. For y equal to toxicity outcome (1 is a DLT, 0 is not a DLT) and x equal to the dose, it is written in the form:

P(y=1|x)=1/(1+exp(-(α + β*x)))


Estimated next dose is calculated by Pr(3 or 4 toxicity) <= targetDLT for the model utilized.

Value

Next Dose with No Used Constraints

Returns estimated dose with no safety constraints implemented for a specified target DLT rate.

Next Dose with Constraints if Applicable

Returns estimated dose with safety constraints used if applicable for a specified target DLT rate.

Next Dose if Using Discrete Dose Levels

Returns the next dose estimated if discrete dose levels are requested.

Estimated Probability of a Grade 0 or 1 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 0 or 1 toxicity as specified by CTCAEv4.0. Only applicable if combine01 = TRUE.

Estimated Probability of a Grade 0 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 0 toxicity as specified by CTCAEv4.0. Only applicable if combine01 = FALSE.

Estimated Probability of a Grade 1 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 1 toxicity as specified by CTCAEv4.0. Only applicable if combine01 = FALSE.

Estimated Probability of a Grade 2 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 2 toxicity as specified by CTCAEv4.0.

Estimated Probability of a Grade 3 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 3 toxicity as specified by CTCAEv4.0.

Estimated Probability of a Grade 4 Toxicity

Using the newly re-estimated model based on the combination of pseudodata and updated patient information, this returns the probability of experiencing a grade 4 toxicity as specified by CTCAEv4.0.

Estimated Regression Model

Returns parameter estimates for the newly re-estimated model based on the combination of pseudodata and updated patient information.

Influence from Pseudodata

Returns the percentage of influence the pseudodata has on estimating the new model. Please refer to Van Meter et al (2010) for more information.

Influence from Collected Data

Returns the percentage of influence the treated patient data has on estimating the new model. Please refer to Van Meter et al (2010) for more information.

Constraints Used

Identifies which safety constraint was implemented if any. Returns: DLT Rule Used, Increase Rule Used, Lower Bound Stop Rule Used, Upper Bound Stop Rule Used, or None Used.

Author(s)

Emily V. Dressler, PhD
Markey Cancer Center
Division of Cancer Biostatistics
University of Kentucky
[email protected]

References

1. Van Meter EM, Garrett-Mayer E, Bandyopadhyay D. Proportional odds model for dose finding clinical trial designs with ordinal toxicity grading. Statistics in Medicine 2011; 30: 2070-2080.
2. Van Meter EM, Garrett-Mayer E, Bandyopadhyay. Dose finding clinical trial design for ordinal toxicity grades using the continuation ratio model: an extension of the continual reassessment method. Clinical Trials 2012; 9(3): 303-313.
3. Garrett-Mayer E. The continual reassessment method for dose-finding studies: a tutorial. Clinical Trials 2006; 3: 57-71.
4. Piantadosi S, Fisher JD, Grossman S. Practical implementation of a modified continual reassessment method for dose-finding trials. Cancer Chemother Pharmacol 1998; 41: 429-436.
5. Harrell FE, Jr. Regression Modeling Strategies with Application to Linear Models, Logistic Regression, and Survival Analysis. Springer: New York, NY, 2001.
6. McCullagh P. Regression Models for Ordinal Data. Journal of the Royal Statistical Society. Series B (Methodological). 1980; 42: 109-142.
7. CTCAE. Cancer Therapy Evaluation Program, Common Terminology Criteria for Adverse Events, Version 4.0, DCTD, NCI, NIH, DHHS (http://ctep/cancer.gov). In Cancer Therapy Evaluation Program, 2010.

See Also

pseudodata

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
#Pseudodata toxicity grades#
initial.cr.y1 <- c(rep(0, 45), rep(1, 36), rep(2, 9), rep(3, 8), rep(4, 2),
                   rep(0, 24), rep(1, 31), rep(2, 15), rep(3, 26), rep(4, 4),
                   rep(0, 14), rep(1, 23), rep(2, 13), rep(3, 40), rep(4, 10),
                   rep(0, 1), rep(1, 4), rep(2, 5), rep(3, 35), rep(4, 55))

#Pseudodata dose levels#
initial.cr.d1 <- c(rep(200, 100), rep(934, 100), rep(1467, 100), rep(3000, 100))

#Pseudodata toxicity grades if combining grades 0 and 1 into 1 category#
combine.cr.y1 <- c(rep(0, 81), rep(1, 9), rep(2, 8), rep(3, 2),
                   rep(0, 55), rep(1, 15), rep(2, 26), rep(3, 4),
                   rep(0, 37), rep(1, 13), rep(2, 40), rep(3, 10),
                   rep(0, 5), rep(1, 5), rep(2, 35), rep(3, 55))

#6 patients already treated at doses 1060 and 800 respectively
#CR model assumption

#Example 1
nextdose(design='CR', pseudotox = initial.cr.y1, pseudodose = initial.cr.d1, cohortsize =,
         collectedtox = c(1, 4, 2, 0, 0, 1), collecteddose = c(1060, 1060, 1060, 800, 800, 800),
         targetDLT = 0.30, pseudoweights = 3, discrete = FALSE, discretedoses = NA,
         combine01 = FALSE, lowerlimitrule = 500)

#Example 2
#Discrete doses and combining grades 0 and 1
nextdose(design='CR', pseudotox = combine.cr.y1, pseudodose = initial.cr.d1, cohortsize =,
        collectedtox = c(1, 3, 2, 0, 0, 1), collecteddose = c(1060, 1060, 1060, 800, 800, 800),
        targetDLT = 0.30, pseudoweights = 3, discrete = TRUE,
        discretedoses = c(200, 500, 100, 1200, 1500, 1800), combine01 = TRUE,
        lowerlimitrule = 500)

Example output

$`Next Dose with No Used Constraints`
[1] 1279

$`Next Dose with Constraints if Applicable`
[1] 1279

$`Next Dose if Using Discrete Dose Levels`
[1] NA

$`Estimated Probability of a non-DLT`
[1] 70

$`Estimated Probability of a DLT`
Intercept 
       30 

$`Estimated Probability of a Grade 0 or 1 Toxicity`
[1] NA

$`Estimated Probability of a Grade 0 Toxicity`
Intercept 
    22.68 

$`Estimated Probability of a Grade 1 Toxicity`
Intercept 
    29.83 

$`Estimated Probability of a Grade 2 Toxicity`
Intercept 
    17.49 

$`Estimated Probability of a Grade 3 Toxicity`
Intercept 
    12.06 

$`Estimated Probability of a Grade 4 Toxicity`
Intercept 
    17.94 

$`Estimated Regression Model`
Logistic Regression Model
 
 rms::lrm(formula = y ~ cohort + dose, weights = newweights)
 
 
 Sum of Weights by Response Category
 
       0       1 
 13.9175  7.4675 
 
                         Model Likelihood     Discrimination    Rank Discrim.    
                            Ratio Test           Indexes           Indexes       
 Obs           1131      LR chi2      2.34    R2       0.143    C       0.455    
  0             797      d.f.            4    g        1.369    Dxy    -0.089    
  1             334      Pr(> chi2) 0.6734    gr       3.932    gamma  -0.098    
 Sum of weights21.385                         gp       0.224    tau-a  -0.024    
 max |deriv|  0.009                           Brier    0.204                     
 
                Coef    S.E.   Wald Z Pr(>|Z|)
 Intercept       0.2807 1.1954  0.23  0.8143  
 cohort=yall>=1  0.7616 1.1382  0.67  0.5034  
 cohort=yall>=2  0.6866 1.4032  0.49  0.6246  
 cohort=yall>=3  0.8295 1.7080  0.49  0.6272  
 dose           -0.0012 0.0010 -1.16  0.2459  
 

$`Estimated Toxicity at Discrete Doses`
[1] NA

$`Discrete Doses`
[1] NA

$`Influence from Pseudodata`
[1] 33.33

$`Influence from Collected Data`
[1] 66.67

$`Constraints Used`
[1] "None Used"

$`Next Dose with No Used Constraints`
[1] 895

$`Next Dose with Constraints if Applicable`
[1] 895

$`Next Dose if Using Discrete Dose Levels`
discretedoses 
         1200 

$`Estimated Probability of a non-DLT`
[1] 58.6

$`Estimated Probability of a DLT`
Intercept 
    41.39 

$`Estimated Probability of a Grade 0 or 1 Toxicity`
Intercept 
    31.18 

$`Estimated Probability of a Grade 0 Toxicity`
[1] NA

$`Estimated Probability of a Grade 1 Toxicity`
[1] NA

$`Estimated Probability of a Grade 2 Toxicity`
Intercept 
    27.42 

$`Estimated Probability of a Grade 3 Toxicity`
Intercept 
    25.39 

$`Estimated Probability of a Grade 4 Toxicity`
Intercept 
       16 

$`Estimated Regression Model`
Logistic Regression Model
 
 rms::lrm(formula = y ~ cohort + dose, weights = newweights)
 
 
 Sum of Weights by Response Category
 
       0       1 
 10.5475  7.4675 
 
                         Model Likelihood     Discrimination    Rank Discrim.    
                            Ratio Test           Indexes           Indexes       
 Obs            814      LR chi2      2.36    R2       0.165    C       0.500    
  0             480      d.f.            3    g        1.524    Dxy     0.000    
  1             334      Pr(> chi2) 0.5015    gr       4.590    gamma   0.000    
 Sum of weights18.015                         gp       0.258    tau-a   0.000    
 max |deriv|   0.01                           Brier    0.212                     
 
                Coef    S.E.   Wald Z Pr(>|Z|)
 Intercept       0.7836 1.2890  0.61  0.5433  
 cohort=yall>=1  0.3798 1.1599  0.33  0.7433  
 cohort=yall>=2  1.2533 1.4777  0.85  0.3964  
 dose           -0.0013 0.0012 -1.14  0.2541  
 

$`Estimated Toxicity at Discrete Doses`
[1]  9.00 10.76 17.60 41.39 52.94 63.71

$`Discrete Doses`
[1]  100  200  500 1200 1500 1800

$`Influence from Pseudodata`
[1] 33.33

$`Influence from Collected Data`
[1] 66.67

$`Constraints Used`
[1] "None Used"

ordcrm documentation built on May 2, 2019, 1:10 p.m.