Discussion
deaths, death, tumor
REGRESSION, TUMOR GROWTH, TUMOR
In MCTs, antitumor efficacy is evaluated by modeling tumor growth using statistical models for longitudinal data. These models must handle the early dropout induced by the death of mice to avoid survivor bias in analyses over time. Using an example of a MCT with a high rate of dropout due to death during the follow-up, as well as a simulation study, we compared the results of two statistical modeling methods, mixed models and joint models, described in the literature for the analysis of this type of data. We showed, with the analysis of the real dataset and the associated complementary analysis, that the joint model can lead to biased estimates of the antitumor efficacy of a treatment in the presence of a high proportion of dropouts that were explained by the observations, that is MAR. Moreover, the additional simulation study confirmed that the bias is due to the presence of the MAR missing dropout mechanism and showed that this bias is all the higher in the presence of competing dropout mechanisms MAR and MNAR. This problem has previously been identified, in an Human Immunodeficiency Virus (HIV) context, in simulation studies in the presence of different study dropout mechanisms (MAR, MNAR and competing dropout mechanisms MAR and MNAR with a predominance of MAR dropouts; refs. Joint models including observations to predict death in addition to the current level of biomarker (in our use case tumor volume) have been developed to tackle this problem of biased estimation for MAR situations (In the context of MCT analysis, it is therefore important to record the reasons for dropouts and especially the causes of death, to inform the choice of the statistical model to be used for the analysis of this type of data in particular with many dropouts. In the context of many dropouts due to mouse sacrifices related to too large and therefore unethical tumor size, we recommend using linear mixed models. Furthermore, in the case where in each treatment arm there are several mice from the same PDX [MCT setting as opposed to the single mouse trial (1 mouse per PDX in each treatment arm)], we recommend the use of nested random effects linear mixed models.For the additional analysis, we created a database containing only MNAR study dropouts by making the tumor growth measurements leading to death unobservable. An alternative would have been to create a database with MNAR study dropouts by randomly drawing MNAR study dropouts from the original dataset. However, in the original database, given the low number of deaths leading to MNAR study dropouts (For our analyses, we assumed an exponential tumor growth by applying a logarithmic transformation to the data. We have shown that within our framework of the statistical regression model with change in slopes, there was a good fit of the model to the data under this assumption. However, it should be noted that other more complex forms of tumor growth and/or more complex treatment effects could be estimated using mechanistic models (In conclusion, the joint models are often recommended in the literature to deal with informative (MNAR) or noninformative (MAR) study dropout. However, these models are valid only under the assumption of a correct specification of the study dropout mechanism. Here we illustrate that this methodology can provide biased estimates under certain MAR study dropout mechanisms and under competitive risk of MAR and MNAR study dropouts. We recommend that a linear mixed model is preferred for MCT analyses.