 Methodology
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A simulation study on estimating biomarker–treatment interaction effects in randomized trials with prognostic variables
 Bernhard Haller^{1}Email authorView ORCID ID profile and
 Kurt Ulm^{1}
 Received: 2 November 2017
 Accepted: 22 January 2018
 Published: 20 February 2018
Abstract
Background
To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker–treatment interactions in randomized clinical trials with timetoevent outcomes.
Methods
A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker–treatment interaction for a timetoevent outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered.
Results
The simulation study revealed that in a regression model for estimating a biomarker–treatment interaction, the probability of detecting a biomarker–treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a falsepositive finding increases if too many potential predictors are included or if variable selection is performed inadequately.
Conclusions
We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and prespecifying analyses to avoid selective reporting and increased falsepositive rates.
Keywords
 Biomarker–treatment interaction
 Randomized trial
 Stratified medicine
 Predictive covariates
 Variable selection
Background
Treatment individualization, i.e. finding the right treatment with the right dose at the right time for a specific patient based on certain patient characteristics, is one of the great goals in modern medicine [1]. One requirement for treatment individualization based on, e.g. a certain biomarker like a genetic characteristic or a blood parameter, is the existence of a relevant association between the biomarker and the treatment effect [2], often referred to as the biomarker–treatment interaction. Only a small number of trials have been planned to analyse biomarker–treatment interactions [3], but often the association between one or more biomarkers and a treatment effect is evaluated post hoc in data collected in randomized clinical trials intended for overall comparison of treatment groups, like e.g. the detection of the association between the response to cetuximab and the presence or absence of the Kras mutation in the tumours of patients with advanced colorectal cancer [4].
While often the treatment effect is analysed in different subgroups (prespecified or post hoc specified) to identify patients that benefit from one or another treatment [5], it is widely recognized that the comparison of treatment groups in many different subgroups can lead to spurious results [6]. Therefore, it is often recommended to assess the biomarker–treatment interaction in a regression model, which directly allows us to estimate and test for an interaction effect under common model assumptions [7]. Various authors who provide methods for estimating biomarker–treatment interactions stress the importance of the adequate inclusion of prognostic factors in the model [8, 9]. For treatment effect estimation in a randomized clinical trial, the European Medicines Agency’s guideline on ‘Points to consider on adjustment for baseline covariates’ recommends including other prognostic factors, i.e. covariates that are assumed to be associated with the outcome, as covariates in the regression model to increase the precision of the estimate of the treatment effect [10]. Furthermore, it has been shown that the estimate for the treatment effect is biased in a Cox regression model, if relevant prognostic covariates are not included [11]. While defining the model used for effect estimation and hypothesis testing a priori and including all relevant covariates can be considered as best practices [12], adequate information about prognostic factors might not be available for all research questions, especially when molecular information that has not been well studied and for which limited information from prior investigations is available is included in a regression model. Various approaches to determining the covariates that are to be included in a regression model are presented in the literature [13].
The focus of this article is estimating the interaction between one certain prespecified biomarker of major interest and the treatment. A simulation study was performed to evaluate how the presence and inclusion of further prognostic covariates affect the estimate of the biomarker–treatment interaction. Different strategies for model building, such as including only the main effects of treatment, the biomarker and their interaction, additionally including covariates that are significantly associated with the outcome, or using variable selection methods based on Akaike’s information criterion (AIC) [14] are considered. Scenarios with varying proportions of censored observations, different strengths of association of the prognostic covariates and the outcome, different correlations between prognostic covariates and the biomarker of interest, and different numbers of potential prognostic covariates are considered. The different strategies of covariate inclusion are compared in the control of type I error probabilities and the power to reject the null hypothesis of no biomarker–treatment interaction. A special focus was placed on the socalled rule of ten [12, 15]. This is often considered for predictive models, but (to the best of our knowledge) has not been investigated for the number of additional covariates considered in a regression model, when the primary goal was estimation of an interaction effect.
Methods
Assessing the biomarker–treatment interaction
where a linear association between a covariate and the loghazard ratio is assumed. In Eq. (1), λ_{0}(t) is the (unspecified) baseline hazard rate, β_{ T } the regression coefficient for treatment T, β_{ B } the coefficient for the biomarker of interest B, β_{T×B} the regression coefficient for their interaction term and β_{ k } the vector of regression coefficients for the K additional covariates, X_{1},…,X_{ K }. When an interaction term is present, the main effects of the treatment T and the biomarker B can be interpreted as the expected treatment difference at a (fictitious) biomarker value of B=0 and the effect of the biomarker B under treatment T=0 conditional on all other covariates. Regression coefficients are estimated by numerical maximization of the partial loglikelihood PL(β). The variancecovariance matrix of the estimated regression coefficients can be derived as the inverse of the observed information matrix \( {I}^{1}(\boldsymbol {\hat {\beta }})\) (see e.g. [16] or [17] for more details).
Strategies for covariate inclusion

Main: A model including only the main effects of treatment T and the biomarker B and their interaction T×B, ignoring all other possible prognostic covariates.

True: A model including the main effects of treatment T, the biomarker of interest B and their interaction T×B, as well as all covariates that are truly associated with the outcome, indicating perfect prior knowledge of relevant covariates.

AIC _{ A }: A model that includes the main effects of treatment T and the biomarker B and their interaction T×B and additionally all covariates that were selected in a forward variable selection procedure based on Akaike’s information criterion (AIC) [14] given T, B and T×B are included (a model including T, B and T×B was used as a starting and minimal model). Additional covariates were selected as long as the AIC criterion$$ \operatorname{AIC} = 2\operatorname{ll}(\hat{\boldsymbol{\beta}})  2p $$(2)
was increased, where \(\operatorname {ll}(\hat {\boldsymbol {\beta }})\) is the partial loglikelihood evaluated at the maximum likelihood estimator \(\hat {\boldsymbol {\beta }}\) and p is the number of estimated regression coefficients.

AIC _{ B }: A modelling strategy similar to AIC _{ A } described above, but prognostic factors were selected based on the AIC criterion considering just the main effect of treatment T as a starting model and not including B or T×B in the variable selection process. After prognostic factors were chosen according to the AIC criterion, B and T×B were added to the model to estimate the biomarker–treatment interaction.

Significance: A model that includes the main effects of treatment T, the covariate of interest and their interaction, as well as all covariates that were significantly associated with the outcome in a Cox regression model including only one covariate (often referred to as univariate Cox models in the medical literature). While this strategy is generally not recommended from a statistical point of view [18], it appears to be a quite popular approach in practice.

Full: A model that includes the treatment T, the biomarker B and their interaction T×B as covariates as well as the main effects of all K potential predictors X_{1},…,X_{ K }.
Data generation and simulation settings
Numerous different settings were considered to evaluate the modelling strategies under varying conditions. For each simulation scenario, 500 subjects were generated. The matrix of continuous covariates (covariate of interest B and potential predictors X_{1},…,X_{ K }) was drawn from a multivariate normal distribution using the R package mvtnorm [19]. For each variable, a mean of 0 and a standard deviation of 1 were used. The correlation structure was specified as described below. Since a randomized controlled trial was intended to be simulated, the treatment variable was drawn independently from all other patient characteristics with Pr(T=1)= Pr(T=0)=0.50 for each individual. For all scenarios, β_{ T } and β_{ B } were chosen as β_{ T }= ln(0.75)=−0.288 (i.e. exp(β_{ T })=0.75) and β_{ B }= ln(1.25)=0.223 (i.e. exp(β_{ B })=1.25).
For each scenario, a timeconstant baseline hazard rate of λ_{0}(t)=1 was used. The hazard rate for each individual was calculated according to Eq. 1 considering the patient’s characteristics and the regression coefficients for the specific scenario. Event times were generated from an exponential distribution using each individual’s hazard rate. All aspects of the simulation study including data generation, estimating regression coefficients and summarizing the results were performed with the statistical software R [20].
The following aspects were varied in the simulation study.
Censoring distribution
 1.
a low proportion of censored observations (between 30% and 40% censored observations corresponding to 300 to 350 observed events)
 2.
a high proportion of censoring (between 60% and 70% censored observations corresponding to 150 to 200 observed events).
Strength of interaction
 1.
Simulation of data under the null hypothesis of no biomarker–treatment interaction: β_{T×B}=0.
 2.
Quantitative biomarker–treatment interaction with a difference in the magnitude of the treatment effect between individuals with a low value of B and individuals with a large value of B: β_{T×B}= ln(1.1)=0.095, leading to a hazard ratio between the treatment groups (T=1 vs. T=0) of about 0.6 for a given value of B=−2 and a hazard ratio of about 0.9 for B=2.
 3.
Qualitative biomarker–treatment interaction indicating an expected lower risk for an event from treatment T=1 for patients with a small value of B and a lower risk under treatment T=0 for patients with a large value of B: β_{T×B}= ln(1.33)=0.285, providing a hazard ratio between the treatment groups smaller than 1 for B<1 and a hazard ratio larger than 1 for B>1 (dotted line in Fig. 1).
 1.
K=12: Here 12 additional prognostic covariates are considered, so the rule of ten is fulfilled under both censoring distributions for most simulation runs, as 150 to 200 events are expected in the settings with a high amount of censoring and up to 15 regression coefficients are to be estimated (12 prognostic variables plus the main effects of treatment T and the covariate of interest B and their interaction T×B).
 2.
K=24: Here 24 additional prognostic covariates are considered, so the rule of ten will be violated for most scenarios with high censoring.
 3.
K=36: Here 36 additional prognostic covariates are considered. Again, the rule of ten will be violated under high censoring.
 1.Firstly, a scenario with a biomarker of interest B that is independent of the potential prognostic variables, and independence between all the prognostic variables was investigated, with$$\Sigma_{1} = \left(\begin{array}{ccccc} 1 & 0 & \cdots & \cdots & 0 \\ 0 & 1 & 0 & \cdots & 0 \\ \vdots & & \ddots & &\vdots \\ 0 & \cdots & 0 & 1 & 0 \\ 0 & \cdots & \cdots & 0 & 1 \\ \end{array} \right). $$
 2.As a second setting, the correlation coefficients between B and all other covariates X_{1},…,X_{ K }, as well as between each pair of covariates X_{ i },X_{ j } with i≠j was set to r=0.5, indicating a moderate correlation between all variables:$$\Sigma_{2} = \left(\begin{array}{ccccc} 1 & 0.5 & \cdots & \cdots & 0.5 \\ 0.5 & 1 & 0.5 & \cdots & 0.5 \\ \vdots & & \ddots & &\vdots \\ 0.5 & \cdots & 0.5 & 1 & 0.5 \\ 0.5 & \cdots & \cdots & 0.5 & 1 \\ \end{array} \right). $$
 3.A block correlation structure between the covariates was considered, with a high correlation of r=0.7 between the biomarker B and a set of variables as well as between those variables, a moderate correlation of r=0.4 for another set and a correlation of r=0.1 or r=0 for the other variables:$$\Sigma_{3} = \left(\begin{array}{ccccccccccccc} 1 & 0.7 & 0.7 & 0.7 & 0.4 & 0.4 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.7 & 1 & 0.7 & 0.7 & 0.4 & 0.4 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.7 & 0.7 & 1 & 0.7 & 0.4 & 0.4 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.7 & 0.7 & 0.7 & 1 & 0.4 & 0.4 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.4 & 0.4 & 0.4 & 0.4 & 1 & 0.4 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 1 & 0.4 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 1 & 0.4 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 0.4 & 1 & 0.1 & 0.1 & 0.1 & 0 & 0 \\ 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 1 & 0.1 & 0.1 & 0 & 0 \\ 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 1 & 0.1 & 0 & 0 \\ 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 1 & 0 & 0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 1 & 0 \\ 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0 & 1 \end{array}\right). $$
For the scenarios with K=24 or K=36 potential predictors, the correlation matrices were adapted accordingly.
Strength of association between prognostic variables and outcome
 1.For all covariates X_{1},…,X_{ K }, the same regression coefficient was chosen:$$\boldsymbol{\beta_{k}} = \boldsymbol{\beta_{eq}} = (\ln(1.1), \ldots, \ln(1.1))^{T} = (0.095, \ldots, 0.095)^{T}. $$
 2.Varying strengths of association between the potential predictors and the risk for an event were considered. The vector of regression coefficients was chosen to be$$\boldsymbol{\beta_{k}} = \boldsymbol{\beta_{v}} = \left(\begin{array}{cc} \ln(1.2) \\ \ln(1.1)\\ \ln(1)\\ \ln(1.2)\\ \ln(1.1)\\ \ln(1)\\ \vdots\\ \ln(1.2)\\ \ln(1.1)\\ \ln(1) \end{array} \right) = \left(\begin{array}{cc} 0.182 \\ 0.095\\ 0\\ 0.182\\ 0.095\\ 0\\ \vdots\\ 0.182\\ 0.095\\ 0 \end{array} \right). $$
Analysis and presentation of results
In each simulation run, all of the methods or strategies described in “Strategies for covariate inclusion” section were fitted or applied, respectively. Estimation of the regression coefficients from the Cox regression models was performed with the function coxph in the survival library [22] of the statistical software R [20]. For the variable selection based on the AIC criterion, the function stepAIC in the library MASS [23] was applied.
where ϕ_{0.975} denotes the 97.5% quantile of the standard normal distribution and \(\widehat {\operatorname {var}}(\hat {\beta }_{T\times B})\) is the estimated variance of the interaction coefficient obtained in the corresponding simulation run for the respective modelling approach. If the algorithm for numerical maximization of the partial loglikelihood did not converge, this information was saved. All results presented in ‘Results’ rely on only estimations for which the numerical optimization algorithm converged. The number of runs for which no result was returned is presented.
For each model and strategy, the confidence interval coverage, i.e. the fraction of simulation runs in which the estimated confidence interval for the biomarker–treatment interaction covered the true value, was derived. The proportion of simulation runs in which the null hypothesis was rejected and a statistically significant biomarker–treatment interaction was detected for the conventional significance level of 5%, i.e. the power of the statistical test if H_{0} were false or the probability of a type I error if H_{0} were true (β_{T×B}=0), was determined [24].
Results
Proportions of rejected null hypotheses and numbers of included covariates stratified for number of potential prognostic variables (K), strength of interaction and proportion of censored observations
K  Interaction  Censoring  Main  True  AIC_{ A }  AIC_{ B }  Significance  Full 

12  No  Low  0.058  0.060  0.062  0.056  0.059  0.060 
12  No  High  0.054  0.051  0.056  0.048  0.053  0.054 
12  Quantitative  Low  0.118  0.133  0.139  0.131  0.130  0.134 
12  Quantitative  High  0.095  0.100  0.108  0.099  0.096  0.099 
12  Qualitative  Low  0.579  0.663  0.663  0.654  0.653  0.661 
12  Qualitative  High  0.393  0.437  0.441  0.424  0.432  0.436 
24  No  Low  0.065  0.058  0.067  0.056  0.057  0.058 
24  No  High  0.057  0.062  0.073  0.054  0.059  0.063 
24  Quantitative  Low  0.115  0.135  0.150  0.131  0.131  0.136 
24  Quantitative  High  0.094  0.100  0.114  0.092  0.100  0.103 
24  Qualitative  Low  0.465  0.634  0.645  0.616  0.610  0.633 
24  Qualitative  High  0.349  0.411  0.426  0.383  0.399  0.415 
36  No  Low  0.065  0.059  0.078  0.056  0.061  0.063 
36  No  High  0.067  0.068  0.085  0.057  0.066  0.071 
36  Quantitative  Low  0.114  0.132  0.153  0.118  0.130  0.134 
36  Quantitative  High  0.093  0.102  0.127  0.093  0.101  0.108 
36  Qualitative  Low  0.412  0.618  0.629  0.578  0.576  0.610 
36  Qualitative  High  0.302  0.406  0.431  0.367  0.382  0.402 
Proportions of rejected null hypotheses and numbers of included covariates for scenarios with K=12
K  Σ  β _{ k }  Censoring  Main  True  AIC_{ A }  AIC_{ B }  Significance  Full 

No interaction  
12  Σ _{1}  β _{ eq }  Low  0.066  0.069  0.072  0.066  0.067  0.069 
12  Σ _{1}  β _{ v }  Low  0.051  0.059  0.059  0.055  0.052  0.058 
12  Σ _{2}  β _{ eq }  Low  0.050  0.051  0.054  0.048  0.051  0.051 
12  Σ _{2}  β _{ v }  Low  0.060  0.047  0.052  0.047  0.052  0.052 
12  Σ _{3}  β _{ eq }  Low  0.060  0.065  0.066  0.061  0.063  0.065 
12  Σ _{3}  β _{ v }  Low  0.063  0.066  0.067  0.060  0.067  0.065 
12  Σ _{1}  β _{ eq }  High  0.057  0.056  0.060  0.054  0.055  0.056 
12  Σ _{1}  β _{ v }  High  0.038  0.040  0.037  0.034  0.040  0.044 
12  Σ _{2}  β _{ eq }  High  0.057  0.046  0.054  0.044  0.046  0.046 
12  Σ _{2}  β _{ v }  High  0.060  0.047  0.058  0.049  0.055  0.055 
12  Σ _{3}  β _{ eq }  High  0.053  0.059  0.060  0.050  0.062  0.059 
12  Σ _{3}  β _{ v }  High  0.057  0.056  0.064  0.055  0.061  0.065 
Quantitative interaction  
12  Σ _{1}  β _{ eq }  Low  0.122  0.137  0.143  0.121  0.123  0.137 
12  Σ _{1}  β _{ v }  Low  0.119  0.134  0.139  0.132  0.134  0.136 
12  Σ _{2}  β _{ eq }  Low  0.105  0.131  0.136  0.129  0.131  0.131 
12  Σ _{2}  β _{ v }  Low  0.113  0.131  0.141  0.135  0.132  0.132 
12  Σ _{3}  β _{ eq }  Low  0.129  0.146  0.148  0.146  0.141  0.146 
12  Σ _{3}  β _{ v }  Low  0.121  0.121  0.127  0.120  0.116  0.120 
12  Σ _{1}  β _{ eq }  High  0.098  0.109  0.120  0.109  0.100  0.109 
12  Σ _{1}  β _{ v }  High  0.108  0.112  0.118  0.111  0.108  0.111 
12  Σ _{2}  β _{ eq }  High  0.077  0.088  0.099  0.086  0.088  0.088 
12  Σ _{2}  β _{ v }  High  0.104  0.095  0.106  0.096  0.093  0.093 
12  Σ _{3}  β _{ eq }  High  0.086  0.093  0.095  0.091  0.088  0.093 
12  Σ _{3}  β _{ v }  High  0.095  0.101  0.107  0.098  0.100  0.101 
Qualitative interaction  
12  Σ _{1}  β _{ eq }  Low  0.625  0.685  0.673  0.662  0.644  0.685 
12  Σ _{1}  β _{ v }  Low  0.605  0.664  0.664  0.661  0.649  0.661 
12  Σ _{2}  β _{ eq }  Low  0.517  0.641  0.641  0.634  0.641  0.641 
12  Σ _{2}  β _{ v }  Low  0.521  0.646  0.648  0.643  0.644  0.644 
12  Σ _{3}  β _{ eq }  Low  0.621  0.678  0.686  0.673  0.680  0.678 
12  Σ _{3}  β _{ v }  Low  0.583  0.661  0.664  0.652  0.661  0.658 
12  Σ _{1}  β _{ eq }  High  0.427  0.462  0.464  0.446  0.433  0.462 
12  Σ _{1}  β _{ v }  High  0.424  0.438  0.447  0.432  0.440  0.443 
12  Σ _{2}  β _{ eq }  High  0.338  0.403  0.410  0.389  0.403  0.403 
12  Σ _{2}  β _{ v }  High  0.359  0.431  0.429  0.413  0.433  0.433 
12  Σ _{3}  β _{ eq }  High  0.394  0.424  0.425  0.407  0.420  0.424 
12  Σ _{3}  β _{ v }  High  0.418  0.466  0.471  0.456  0.465  0.449 
Mean number of prognostic covariates included  
β _{ eq }  Low  0  12  6.8  7.3  8.7  12  
β _{ v }  Low  0  8  6.4  6.9  8.7  12  
β _{ eq }  High  0  12  5.1  5.7  8.0  12  
β _{ v }  High  0  8  5.3  5.8  8.2  12 
Proportions of rejected null hypotheses and numbers of included covariates for scenarios with K=24
K  Σ  β _{ k }  Censoring  Main  True  AIC_{ A }  AIC_{ B }  Significance  Full 

No interaction  
24  Σ _{1}  β _{ eq }  Low  0.044  0.049  0.065  0.053  0.048  0.049 
24  Σ _{1}  β _{ v }  Low  0.055  0.069  0.074  0.065  0.060  0.069 
24  Σ _{2}  β _{ eq }  Low  0.087  0.052  0.063  0.046  0.052  0.052 
24  Σ _{2}  β _{ v }  Low  0.068  0.071  0.081  0.066  0.071  0.071 
24  Σ _{3}  β _{ eq }  Low  0.068  0.049  0.061  0.052  0.055  0.049 
24  Σ _{3}  β _{ v }  Low  0.066  0.056  0.061  0.053  0.054  0.056 
24  Σ _{1}  β _{ eq }  High  0.035  0.056  0.069  0.054  0.046  0.056 
24  Σ _{1}  β _{ v }  High  0.051  0.071  0.076  0.059  0.068  0.076 
24  Σ _{2}  β _{ eq }  High  0.073  0.066  0.074  0.056  0.066  0.066 
24  Σ _{2}  β _{ v }  High  0.060  0.054  0.066  0.048  0.056  0.056 
24  Σ _{3}  β _{ eq }  High  0.062  0.058  0.073  0.047  0.059  0.058 
24  Σ _{3}  β _{ v }  High  0.059  0.068  0.079  0.060  0.057  0.066 
Quantitative interaction  
24  Σ _{1}  β _{ eq }  Low  0.114  0.142  0.158  0.137  0.122  0.142 
24  Σ _{1}  β _{ v }  Low  0.106  0.138  0.150  0.132  0.125  0.143 
24  Σ _{2}  β _{ eq }  Low  0.119  0.135  0.148  0.130  0.135  0.135 
24  Σ _{2}  β _{ v }  Low  0.111  0.124  0.145  0.117  0.126  0.126 
24  Σ _{3}  β _{ eq }  Low  0.121  0.136  0.145  0.128  0.141  0.136 
24  Σ _{3}  β _{ v }  Low  0.121  0.136  0.157  0.141  0.134  0.136 
24  Σ _{1}  β _{ eq }  High  0.088  0.100  0.117  0.091  0.093  0.100 
24  Σ _{1}  β _{ v }  High  0.085  0.104  0.110  0.096  0.094  0.111 
24  Σ _{2}  β _{ eq }  High  0.094  0.109  0.122  0.094  0.109  0.109 
24  Σ _{2}  β _{ v }  High  0.113  0.096  0.116  0.088  0.100  0.100 
24  Σ _{3}  β _{ eq }  High  0.082  0.098  0.109  0.097  0.103  0.098 
24  Σ _{3}  β _{ v }  High  0.100  0.091  0.109  0.086  0.098  0.098 
Qualitative interaction  
24  Σ _{1}  β _{ eq }  Low  0.630  0.697  0.686  0.658  0.632  0.697 
24  Σ _{1}  β _{ v }  Low  0.547  0.678  0.688  0.656  0.615  0.685 
24  Σ _{2}  β _{ eq }  Low  0.349  0.610  0.619  0.595  0.610  0.610 
24  Σ _{2}  β _{ v }  Low  0.358  0.590  0.608  0.584  0.578  0.578 
24  Σ _{3}  β _{ eq }  Low  0.443  0.596  0.620  0.582  0.587  0.596 
24  Σ _{3}  β _{ v }  Low  0.465  0.632  0.651  0.621  0.636  0.631 
24  Σ _{1}  β _{ eq }  High  0.448  0.457  0.463  0.424  0.412  0.457 
24  Σ _{1}  β _{ v }  High  0.384  0.453  0.466  0.425  0.420  0.464 
24  Σ _{2}  β _{ eq }  High  0.276  0.364  0.387  0.340  0.364  0.364 
24  Σ _{2}  β _{ v }  High  0.292  0.397  0.423  0.378  0.411  0.411 
24  Σ _{3}  β _{ eq }  High  0.355  0.387  0.405  0.356  0.382  0.387 
24  Σ _{3}  β _{ v }  High  0.338  0.408  0.409  0.377  0.404  0.408 
Mean number of prognostic covariates included  
β _{ eq }  Low  0  24  13.2  13.7  17.5  24  
β _{ v }  Low  0  16  12.7  13.1  17.8  24  
β _{ eq }  High  0  24  10.2  10.7  16.5  24  
β _{ v }  High  0  16  10.6  11.0  16.8  24 
Proportions of rejected null hypotheses and numbers of included covariates for scenarios with K=36
K  Σ  β _{ k }  Censoring  Main  True  AIC_{ A }  AIC_{ B }  Significance  Full 

No interaction  
36  Σ _{1}  β _{ eq }  Low  0.047  0.065  0.080  0.054  0.056  0.065 
36  Σ _{1}  β _{ v }  Low  0.059  0.067  0.084  0.066  0.069  0.073 
36  Σ _{2}  β _{ eq }  Low  0.077  0.053  0.073  0.051  0.053  0.053 
36  Σ _{2}  β _{ v }  Low  0.075  0.054  0.074  0.052  0.056  0.056 
36  Σ _{3}  β _{ eq }  Low  0.067  0.060  0.083  0.057  0.064  0.060 
36  Σ _{3}  β _{ v }  Low  0.063  0.055  0.074  0.053  0.069  0.069 
36  Σ _{1}  β _{ eq }  High  0.052  0.071  0.086  0.059  0.055  0.071 
36  Σ _{1}  β _{ v }  High  0.047  0.063  0.080  0.058  0.050  0.066 
36  Σ _{2}  β _{ eq }  High  0.085  0.080  0.082  0.057  0.080  0.080 
36  Σ _{2}  β _{ v }  High  0.085  0.064  0.086  0.054  0.070  0.070 
36  Σ _{3}  β _{ eq }  High  0.057  0.069  0.094  0.056  0.063  0.069 
36  Σ _{3}  β _{ v }  High  0.075  0.063  0.081  0.057  0.076  0.071 
Quantitative interaction  
36  Σ _{1}  β _{ eq }  Low  0.103  0.150  0.165  0.130  0.138  0.150 
36  Σ _{1}  β _{ v }  Low  0.095  0.131  0.150  0.120  0.125  0.136 
36  Σ _{2}  β _{ eq }  Low  0.121  0.120  0.141  0.109  0.120  0.120 
36  Σ _{2}  β _{ v }  Low  0.128  0.128  0.147  0.121  0.134  0.134 
36  Σ _{3}  β _{ eq }  Low  0.115  0.121  0.142  0.103  0.122  0.121 
36  Σ _{3}  β _{ v }  Low  0.119  0.143  0.172  0.125  0.140  0.143 
36  Σ _{1}  β _{ eq }  High  0.081  0.108  0.133  0.098  0.094  0.108 
36  Σ _{1}  β _{ v }  High  0.095  0.112  0.132  0.101  0.102  0.118 
36  Σ _{2}  β _{ eq }  High  0.100  0.085  0.103  0.072  0.085  0.085 
36  Σ _{2}  β _{ v }  High  0.092  0.109  0.127  0.093  0.118  0.118 
36  Σ _{3}  β _{ eq }  High  0.091  0.104  0.134  0.099  0.105  0.104 
36  Σ _{3}  β _{ v }  High  0.097  0.093  0.132  0.093  0.102  0.115 
Qualitative interaction  
36  Σ _{1}  β _{ eq }  Low  0.551  0.652  0.657  0.603  0.558  0.652 
36  Σ _{1}  β _{ v }  Low  0.517  0.700  0.688  0.658  0.599  0.669 
36  Σ _{2}  β _{ eq }  Low  0.280  0.570  0.582  0.518  0.570  0.570 
36  Σ _{2}  β _{ v }  Low  0.266  0.555  0.575  0.517  0.542  0.542 
36  Σ _{3}  β _{ eq }  Low  0.408  0.609  0.637  0.582  0.581  0.609 
36  Σ _{3}  β _{ v }  Low  0.451  0.623  0.637  0.592  0.605  0.620 
36  Σ _{1}  β _{ eq }  High  0.389  0.447  0.456  0.403  0.385  0.447 
36  Σ _{1}  β _{ v }  High  0.390  0.472  0.486  0.437  0.418  0.453 
36  Σ _{2}  β _{ eq }  High  0.219  0.368  0.411  0.334  0.368  0.368 
36  Σ _{2}  β _{ v }  High  0.228  0.385  0.419  0.353  0.389  0.389 
36  Σ _{3}  β _{ eq }  High  0.282  0.364  0.396  0.328  0.352  0.364 
36  Σ _{3}  β _{ v }  High  0.303  0.402  0.416  0.350  0.382  0.391 
Mean number of prognostic covariates included  
β _{ eq }  Low  0  36  19.6  19.9  24.5  36  
β _{ v }  Low  0  24  19.0  19.4  25.2  36  
β _{ eq }  High  0  36  15.2  15.7  23.1  36  
β _{ v }  High  0  24  16.0  16.4  23.9  36 
The mean numbers of included additional covariates are given for each method or strategy for sets of scenarios stratified for β_{ k } and amount of censoring in the bottom rows of Tables 2, 3 and 4 and for each of the 108 simulated scenarios in Additional file 7: Table S1 (for K=12), Additional file 8: Table S2 (for K=24) and Additional file 9: Table S3 (for K=36).
The type I error probabilities for the biomarker–treatment interaction term, which are presented in the lines indicated with no interaction (Table 1) and in the upper parts of Tables 2, 3 and 4 for the scenarios with no interaction, were acceptable for almost all methods and strategies, when K=12 further (potential) prognostic variables were considered. Only for strategy AIC _{ A } an unacceptably high probability of type I errors (defined as larger than 7%) was observed for one setting (Table 2). For scenarios with K=24 (potential) prognostic variables, increased type I error probabilities were observed for each method for at least one scenario, except for AIC _{ B }. For AIC _{ A }, type I error probabilities above 7% were observed for six of the 12 settings (Table 3) and for scenarios with a high proportion of censored observations (60% to 70%) when scenarios with different β_{ k } and Σ were aggregated (Table 1). When K = 36 potential predictors were considered, an increased type I error probability was observed for AIC _{ A } for all scenarios. For main, significance and full, elevated false positive rates were obtained for three to five scenarios with a high proportion of censored observations. For the true model, only two scenarios with a high proportion of censored observations led to rejection of the null hypothesis in more than 7% of the observed simulation runs (Σ_{ 2 }, β_{ eq } and Σ_{ 1 }, β_{ eq }). For all other scenarios, the observed type I error probabilities were between 5% and 7%. For the strategy AIC _{ B }, all observed type I error probabilities were between 5% and 7%.
For the main model, regression coefficients for the biomarker–treatment effect were underestimated when a true biomarker–treatment interaction was present (Fig. 2), with the largest bias observed for scenarios with Σ=Σ_{ 2 } (see second rows of Additional file 1: Figure S1A and Figure S1B, Additional file 2: Figure S2A and Figure S2B, Additional file 3: Figure S3A and Figure S3B, Additional file 4: Figure S4A and Figure S4B, Additional file 5: Figure S5A and Figure S5B, and Additional file 6: Figure S6A and Figure S6B). This also led to a loss of power, which was reduced as compared to the true model for most of the scenarios (Tables 1, 2, 3 and 4, green dots in Additional file 1: Figure S1, Additional file 2: Figure S2, Additional file 3: Figure S3, Additional file 4: Figure S4, Additional file 5: Figure S5 and Additional file 6: Figure S6). Generally, the highest power was observed for the true model. The power for AIC _{ A } cannot be interpreted adequately for most of the scenarios due to its increased type I error probabilities. The full model is identical to the true model for β_{ k }=β_{ eq }, as all covariates are truly associated with the outcome. For β_{ k }=β_{ v }, the power of the full model was similar to the power of the true model for K=12 and K=24 in our simulation runs, but was slightly lower for simulations with K=36. The strategy AIC _{ B }, which appears to have an adequate false positive rate, showed (slightly) lower power than the true model for (almost) all of the scenarios. A slightly decreased power was also observed for the strategy including all covariates that were significantly associated with the outcome (significance). The type I error probability was acceptable for most scenarios with a small or moderate number of potential predictors (K=12 and K=24), but an increased type I error probability was observed for scenarios with many potential predictors (K=36).
Coverage was adequate for most of the models and strategies. For main, the coverage was reduced for some scenarios due to biased estimates. For AIC _{ A }, the coverage was under 93% for 52 of the 108 scenarios (48.1%), indicating standard errors for the regression coefficient of interest were underestimated following the variable selection procedure.
In the last rows of Tables 2, 3 and 4, the mean numbers of additionally included covariates are summarized for each method/strategy stratified for the amount of censoring and β_{ k } (which determines the number of truly prognostic variables). It was observed that for our settings, the procedure including variables that were significantly associated with the outcome in univariate Cox models selected more variables than the AICbased methods, and that slightly more variables were chosen with AIC _{ B } than with AIC _{ A }. For scenarios with β_{ k }=β_{ eq }, the true and full models were identical by definition. More detailed information on the numbers of covariates included are given in Additional file 7: Table S1, Additional file 8: Table S2 and Additional file 9: Table S3.
The optimization algorithm for numerical maximization of the partial loglikelihood of the Cox regression model for estimating the regression coefficients did not converge for some simulation runs. The problem especially occurred for AIC _{ A }. Over all 108,000 simulation runs (108 scenarios × 1,000 runs per scenario), the estimation algorithm did not converge 11 times (0.010%) for main, twice (0.002%) for true, 895 times (0.829%) for AIC _{ A }, 27 times (0.025%) for AIC _{ B }, three times (0.003%) for significance and no times (0%) for full.
Discussion
The ultimate goal in individualized or tailored medicine is to find the best treatment for each individual based on the patient’s characteristics like age, sex, comorbidities, disease history and molecular and genetic information, which are often referred to as biomarkers. The existence and detection of a biomarker–treatment interaction can be considered as a requirement for such treatment individualization [2], and consequently an interaction between the biomarker of interest and treatment has to be established in a first step, e.g. by finding statistically significant and clinically relevant interactions based on data from (multiple) randomized clinical trials. Decision rules for treatment selection based on the characteristics of a certain patient have to be investigated and established afterwards, also considering the benefits and costs of the application of a certain treatment strategy for a given patient.
To detect relevant associations and interactions, it is well known that splitting a quantitative variable into different categories, leading to a comparison of treatment effects between different subgroups, will result in a loss of information and will consequently decrease the probability of detecting a true biomarker–treatment interaction [25]. So, using all the quantitative information is recommended for analysis of biomarker–treatment interactions [7]. To estimate a treatment effect in a randomized clinical trial, the inclusion of relevant prognostic variables is recommended [10] to increase the precision of the estimate and consequently the probability of detecting real group differences. For this article, we performed a simulation study to investigate whether the probability of detecting a biomarker–treatment interaction in data derived from a randomized clinical trial can be improved by including further potentially prognostic variables in a Cox regression model for timetoevent data. Different settings for the strength of interaction between the biomarker and the treatment, the correlation between the biomarker of interest and other potential predictors, the strength of association between the predictors and outcome, the number of (potential) further predictors, and the number of events and censored observations were considered. When a biomarker–treatment interaction is assessed using data from a randomized clinical trial, obviously the best choice is to include in the final model all covariates truly associated with the outcome, which was covered by the true model in our simulation study. As this true model often is not known in practice, especially in investigations including molecular or genetic information, more flexible approaches might be needed. So, we also investigated strategies using datadriven variable selection procedures based on AIC [14] or on the results of Cox regression models with single covariates.
In our simulation study, we observed that including the correct prognostic variables leads to an increased probability of detecting a true biomarker–treatment interaction and reduced bias of the estimated interaction effect, with the magnitude of improvement depending on the strength of association between the prognostic variables and the outcome and between the prognostic variables and the biomarker of interest. In contrast, including too many variables per event can lead to the opposite effect and increased probabilities of false positives. This problem is well known for multiple regression models [15, 26]. Our results support the rule of ten, which was proposed for predictive modelling [27], since the type I error probability was increased for the biomarker of interest, even for the true model, when a large number of covariates was considered. The simulation study also revealed that ignoring relevant prognostic factors leads to biased estimates for the biomarker–treatment interaction effect, which has been described for estimating the group effect from a randomized clinical trial using a Cox regression model [11]. Generally, the datadriven selection of prognostic variables by an inclusion procedure based on the AIC after including the main effects of the biomarker of interest, the treatment and their interaction in the model increases the type I error probabilities and reduces the confidence interval coverage. This was not observed in a strategy that selected the relevant prognostic variables in a first step and added the biomarker main effect and the biomarker–treatment interaction afterwards (called AIC _{ B } in our article). In our simulated scenarios, the strategy including all covariates that were found to be significantly associated with the outcome performed similarly to that approach. Automated variable selection procedures are criticized in the literature for various reasons (see e.g. [28]). Based on the results of our simulation study, we strongly discourage using an automated variable selection procedure to choose additional prognostic variables after including the biomarker–treatment interaction of interest, as this may lead to unreliable results.
An obvious limitation of our study is that we observed only a moderate number of different scenarios with three correlation structures, three strengths of interaction between the biomarker and treatment, two strengths/structures of association between the additional prognostic variables and treatment, two censoring distributions, three numbers of (potential) prognostic variables, and a fixed number of 500 observations, due to limited time and space. All these aspects influenced the results and other settings may have led to different findings and consequently recommendations. In particular, the number of observed events, which is more important than the total sample size for a timetoevent outcome, was varied only by choosing two different censoring proportions, but it has a major impact on the power of the interaction test. We also investigated only a small number of strategies for inclusion or selection of further covariates based on the AIC and significant associations with the outcome. Other strategies (like backward selection), other criteria (like the Bayesian information criterion [29]) or other procedures for variable selection (like the least absolute shrinkage and selection operator [30]) were not considered. Furthermore, we considered only normally distributed biomarkers and linear associations and interactions in our simulations and fitted Cox regression models assuming linear associations and timeconstant effects to our data. Recently introduced methods for estimating nonlinear interactions, like local partial likelihood estimation [31], multivariable fractional polynomials for interaction [8] or the modified covariate approach [9], were not investigated.
It has to be considered that in our scenario, only one prespecified biomarker of interest is assessed. It was identified as being of interest e.g. in an observational study or was found to be relevant for a similar kind of disease. If more than one biomarker is investigated, multiplicity issues arise that have to be adequately considered [32]. When an analysis is an additional analysis to a standard group comparison for a randomized clinical trial, it can only be exploratory in nature. Nevertheless, the method used for statistical analysis should be specified a priori to generate reliable results and avoid problems of datadredging and selective reporting, and consequently generating unreliable results and increased falsepositive rates [33]. Further algorithms or strategies should be used in sensitivity analyses to assess the stability of the observed results. If the investigation of a biomarker–treatment interaction is of major importance for a clinical trial, this should be considered in the design stage and consequently in the sample size calculation.
Conclusions
Based on the results of our simulation study, we recommend considering prognostic covariates in regression models when estimating biomarker–treatment interactions, as the power for detecting true interactions can be increased. However, including too many variables can lead to unreliable results. The choice of variables included should be based on prior information and subject knowledge. Automatic variable selection procedures have to be handled with care.
Declarations
Acknowledgments
Not applicable
Funding
This work was supported by the German Research Foundation (DFG) and the Technische Universität München within the funding programme Open Access Publishing.
Availability of data and materials
No patient data were used when writing this article. The R code for generating the data sets used in the simulation study, for applying the approaches and strategies described, and for analysing the results obtained in the simulation study can be obtained from the first author upon reasonable request.
Authors’ contributions
BH designed and implemented the simulation study and drafted the manuscript. KU critically reviewed the manuscript for intellectual content. Both authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
References
 Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010; 363(4):301–4.View ArticlePubMedGoogle Scholar
 Chen JJ, Lu TP, Chen YC, Lin WJ. Predictive biomarkers for treatment selection: statistical considerations. Biomark Med. 2015; 9(11):1121–35.View ArticlePubMedGoogle Scholar
 Rothwell PM. Subgroup analysis in randomised controlled trials: importance, indications, and interpretation. Lancet. 2005; 365(9454):176–86.View ArticlePubMedGoogle Scholar
 Karapetis CS, KhambataFord S, Jonker DJ, O’Callaghan CJ, Tu D, Tebbutt NC, et al.Kras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med. 2008; 359(17):1757–65.View ArticlePubMedGoogle Scholar
 Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet. 2000; 355(9209):1064–9.View ArticlePubMedGoogle Scholar
 Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. Am J Neuroradiol. 2011; 32(3):437–40.View ArticlePubMedGoogle Scholar
 Royston P, Sauerbrei W. Interactions between treatment and continuous covariates: a step toward individualizing therapy. J Clin Oncol. 2008; 26(9):1397–9.View ArticlePubMedGoogle Scholar
 Royston P, Sauerbrei W. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Stat Med. 2004; 23(16):2509–25.View ArticlePubMedGoogle Scholar
 Tian L, Alizadeh AA, Gentles AJ, Tibshirani R. A simple method for estimating interactions between a treatment and a large number of covariates. J Am Stat Assoc. 2014; 109(508):1517–32.View ArticlePubMedPubMed CentralGoogle Scholar
 Committee for Proprietary Medicinal Products. Points to consider on adjustment for baseline covariates. Stat Med. 2004; 23(5):701.View ArticleGoogle Scholar
 Langner I, Bender R, LenzTönjes R, Küchenhoff H, Blettner M. Bias of maximumlikelihood estimates in logistic and Cox regression models: a comparative simulation study. Sonderforschungsbereich 386. LudwigMaximiliansUniversität München; 2003.Google Scholar
 Harrell F. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. New York: Springer; 2001.View ArticleGoogle Scholar
 Royston P, Sauerbrei W. Multivariable modelbuilding: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables, Vol. 777. Chichester: Wiley; 2008.View ArticleGoogle Scholar
 Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974; 19(6):716–23.View ArticleGoogle Scholar
 Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regressiontype models. Psychosom Med. 2004; 66(3):411–21.PubMedGoogle Scholar
 Cox DR. Regression models and life tables (with discussion). J Royal Stat Soc. 1972; 34:187–220.Google Scholar
 Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York: Springer; 2013.Google Scholar
 Vach W. Regression models as a tool in medical research. Boca Raton: CRC Press; 2012.Google Scholar
 Genz A, Bretz F, Miwa X, Tetsuhisa abd Mi, Leisch F, Scheipl F, Hothorn T. Mvtnorm: multivariate normal and T distributions. R package version 1.05. 2016. http://CRAN.Rproject.org/package=mvtnorm.
 R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2016. https://www.Rproject.org/.Google Scholar
 Polley MYC, Freidlin B, Korn EL, Conley BA, Abrams JS, McShane LM. Statistical and practical considerations for clinical evaluation of predictive biomarkers. J Natl Cancer Inst. 2013; 105(22):1677–83.View ArticlePubMedPubMed CentralGoogle Scholar
 Therneau T. A package for survival analysis in S. Version 2.38. 2015. http://CRAN.Rproject.org/package=survival.
 Venables WN, Ripley BD. Modern applied statistics with S, 4th ed. New York: Springer; 2002. http://www.stats.ox.ac.uk/pub/MASS4.View ArticleGoogle Scholar
 Burton A, Altman DG, Royston P, Holder RL. The design of simulation studies in medical statistics. Stat Med. 2006; 25(24):4279–92.View ArticlePubMedGoogle Scholar
 Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006; 25(1):127–41.View ArticlePubMedGoogle Scholar
 Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol. 1995; 48(12):1495–501.View ArticlePubMedGoogle Scholar
 Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995; 48(12):1503–10.View ArticlePubMedGoogle Scholar
 Sainani KL. Multivariate regression: the pitfalls of automated variable selection. PM&R. 2013; 5(9):791–4.View ArticleGoogle Scholar
 Schwarz G, et al. Estimating the dimension of a model. Annals Stat. 1978; 6(2):461–4.View ArticleGoogle Scholar
 Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol. 1996; 58(1):267–288.Google Scholar
 Liu Y, Jiang W, Chen BE. Testing for treatmentbiomarker interaction based on local partiallikelihood. Stat Med. 2015; 34(27):3516–30.View ArticlePubMedGoogle Scholar
 European Medicines Agency. Guideline on multiplicity issues in clinical trials. 2017. http://www.ema.europa.eu/ema/index.jsp?curl=pages/regulation/general/general_content_001220.jsp&mid=.Google Scholar
 Ioannidis JP. Why most published research findings are false. PLoS Med. 2005; 2(8):124.View ArticleGoogle Scholar
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