Tislelizumab plus zanubrutinib for Richter transformation: the phase 2 RT1 trial.

In patients with chronic lymphocytic leukemia, Richter transformation (RT) reflects the development of an aggressive lymphoma that is associated with poor response to chemotherapy and short survival. We initiated an international, investigator-initiated, prospective, open-label phase 2 study in which patients with RT received a combination of the PD-1 inhibitor tislelizumab plus the BTK inhibitor zanubrutinib for 12 cycles. Patients responding to treatment underwent maintenance treatment with both agents. The primary end point was overall response rate after six cycles. Of 59 enrolled patients, 48 patients received at least two cycles of treatment and comprised the analysis population according to the study protocol. The median observation time was 13.9 months, the median age was 67 (range 45-82) years. Ten patients (20.8%) had received previous RT-directed therapy. In total, 28 out of 48 patients responded to induction therapy with an overall response rate of 58.3% (95% confidence interval (CI) 43.2-72.4), including 9 (18.8%) complete reponse and 19 (39.6%) partial response, meeting the study's primary end point by rejecting the predefined null hypothesis of 40% (P = 0.008). Secondary end points included duration of response, progression-free survival and overall survival. The median duration of response was not reached, the median progression-free survival was 10.0 months (95% CI 3.8-16.3). Median overall survival was not reached with a 12-month overall survival rate of 74.7% (95% CI 58.4-91.0). The most common adverse events were infections (18.0%), gastrointestinal disorders (13.0%) and hematological toxicities (11.4%). These data suggest that combined checkpoint and BTK inhibition by tislelizumab plus zanubrutinib is an effective and well-tolerated treatment strategy for patients with RT. ClinicalTrials.gov Identifier: NCT04271956 .

Incomplete Tests of Conditional Association for the Assessment of Model Assumptions.

Many of the models that have been proposed for response data share the assumptions that define the monotone homogeneity (MH) model. Observable properties that are implied by the MH model allow for these assumptions to be tested. For binary response data, the most restrictive of these properties is called conditional association (CA). All the other properties considered can be considered incomplete tests of CA that alleviate the practical limitations encountered when assessing the MH model assumptions using CA. It is found that the assessment of the MH model assumptions with an incomplete test of CA, rather than CA, is generally associated with a substantial loss of information. We also look at the sensitivity of the observable properties to model violation and discuss the implications of the results. It is argued that more research is required about the extent to which the assumptions and the model specifications influence the inferences made from response data.

Remarks and a Correction of Ligtvoet's Treatment of the Isotonic Partial Credit Model.

This note contains some remarks on Ligtvoet's (Psychometrika, 77:479-494, 2012) treatment of the isotonic partial credit model. The Proposition relating to the observable property MWI is shown to be false.

Latent class models for testing monotonicity and invariant item ordering for polytomous items.

Two assumptions that are relevant to many applications using item response theory are the assumptions of monotonicity (M) and invariant item ordering (IIO). A latent class model is proposed for ordinal items with inequality constraints on the class-specific item means. This model is used as a tool for testing for violations of M and IIO. A Gibbs sampling scheme is used for estimating the model parameters. It is shown that the deviance information criterion can be used as an overall test of M and IIO, while posterior predictive checks can be used to test these assumptions at the item level. A real data application illustrates a model-fitting strategy for detecting items that violate M and IIO.

An Isotonic Partial Credit Model for Ordering Subjects on the Basis of Their Sum Scores.

In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable. However, the PCM is very restrictive with respect to the constraints that it imposes on the data. In this paper, sufficient conditions for the stochastic ordering of subjects by their sum score are obtained. These conditions define the isotonic (nonparametric) PCM model. The isotonic PCM is more flexible than the PCM, which makes it useful for a wider variety of tests. Also, observable properties of the isotonic PCM are derived in the form of inequality constraints. It is shown how to obtain estimates of the score distribution under these constraints by using the Gibbs sampling algorithm. A small simulation study shows that the Bayesian p-values based on the log-likelihood ratio statistic can be used to assess the fit of the isotonic PCM to the data, where model-data fit can be taken as a justification of the use of the sum score to order subjects.