Statistical controversies in clinical research: building the bridge to phase II-efficacy estimation in dose-expansion cohorts.

BACKGROUND: Regulatory agencies and others have expressed concern about the uncritical use of dose expansion cohorts (DECs) in phase I oncology trials. Nonetheless, by several metrics-prevalence, size, and number-their popularity is increasing. Although early efficacy estimation in defined populations is a common primary endpoint of DECs, the types of designs best equipped to identify efficacy signals have not been established. METHODS: We conducted a simulation study of six phase I design templates with multiple DECs: three dose-assignment/adjustment mechanisms multiplied by two analytic approaches for estimating efficacy after the trial is complete. We also investigated the effect of sample size and interim futility analysis on trial performance. Identifying populations in which the treatment is efficacious (true positives) and weeding out inefficacious treatment/populations (true negatives) are competing goals in these trials. Thus, we estimated true and false positive rates for each design. RESULTS: Adaptively updating the MTD during the DEC improved true positive rates by 8-43% compared with fixing the dose during the DEC phase while maintaining false positive rates. Inclusion of an interim futility analysis decreased the number of patients treated under inefficacious DECs without hurting performance. CONCLUSION: A substantial gain in efficiency is obtainable using a design template that statistically models toxicity and efficacy against dose level during expansion. Design choices for dose expansion should be motivated by and based upon expected performance. Similar to the common practice in single-arm phase II trials, cohort sample sizes should be justified with respect to their primary aim and include interim analyses to allow for early stopping.

Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

In clinical trials, an intermediate marker measured after randomization can often provide early information about the treatment effect on the final outcome of interest. We explore the use of recurrence time as an auxiliary variable for estimating the treatment effect on overall survival in phase three randomized trials of colon cancer. A multi-state model with an incorporated cured fraction for recurrence is used to jointly model time to recurrence and time to death. We explore different ways in which the information about recurrence time and the assumptions in the model can lead to improved efficiency. Estimates of overall survival and disease-free survival can be derived directly from the model with efficiency gains obtained as compared to Kaplan-Meier estimates. Alternatively, efficiency gains can be achieved by using the model in a weaker way in a multiple imputation procedure, which imputes death times for censored subjects. By using the joint model, recurrence is used as an auxiliary variable in predicting survival times. We demonstrate the potential use of the proposed methods in shortening the length of a trial and reducing sample sizes.

Multi-state models for colon cancer recurrence and death with a cured fraction.

In cancer clinical trials, patients often experience a recurrence of disease prior to the outcome of interest, overall survival. Additionally, for many cancers, there is a cured fraction of the population who will never experience a recurrence. There is often interest in how different covariates affect the probability of being cured of disease and the time to recurrence, time to death, and time to death after recurrence. We propose a multi-state Markov model with an incorporated cured fraction to jointly model recurrence and death in colon cancer. A Bayesian estimation strategy is used to obtain parameter estimates. The model can be used to assess how individual covariates affect the probability of being cured and each of the transition rates. Checks for the adequacy of the model fit and for the functional forms of covariates are explored. The methods are applied to data from 12 randomized trials in colon cancer, where we show common effects of specific covariates across the trials.

Predicting treatment effects using biomarker data in a meta-analysis of clinical trials.

A biomarker (S) measured after randomization in a clinical trial can often provide information about the true endpoint (T) and hence the effect of treatment (Z). It can usually be measured earlier and more easily than T and as such may be useful to shorten the trial length. A potential use of S is to completely replace T as a surrogate endpoint to evaluate whether the treatment is effective. Another potential use of S is to serve as an auxiliary variable to help provide information and improve the inference on the treatment effect prediction when T is not completely observed. The objective of this report is to focus on its role as an auxiliary variable and to identify situations when S can be useful to increase efficiency in predicting the treatment effect in a new trial in a multiple-trial setting. Both S and T are continuous. We find that higher efficiency gain is associated with higher trial-level correlation but not individual-level correlation when only S, but not T is measured in a new trial; but, the amount of information recovery from S is usually negligible. However, when T is partially observed in the new trial and the individual-level correlation is relatively high, there is substantial efficiency gain by using S. For design purposes, our results suggest that it is often important to collect markers that have high adjusted individual-level correlation with T and at least a small amount of data on T. The results are illustrated using simulations and an example from a glaucoma clinical trial.

A quantitative real-time PCR assay for the detection of Ramularia collo-cygni from barley (Hordeum vulgare).

AIMS: The aim of this study was to develop a real-time quantitative PCR test to recognize and quantify the DNA levels of the increasingly important barley pathogen Ramularia collo-cygni. METHODS AND RESULTS: The method described uses specifically designed primers and a molecular beacon probe based on an internal transcribed spacer (ITS) sequence. Pathogen extracted from barley leaves could be quantified to the picogram level in both leaves showing symptoms of infection and symptomless barley leaves. CONCLUSIONS: A relationship between R. collo-cygni DNA levels and disease symptoms was established in spring barley under natural infection conditions. SIGNIFICANCE AND IMPACT OF THE STUDY: To our knowledge, this is the first report of a test of this type and makes an important contribution to studies into the life cycle of this pathogen.

Prostate cancer: progression of prostate-specific antigen after external beam irradiation.

This paper is concerned with the development of a stochastic path of prostate-specific antigen (PSA) level after radiation treatment for prostate cancer. PSA is a biomarker for prostate cancer, higher levels of which indicate the seriousness of the cancer progression. Following the deterministic modeling of the data by the previous authors, Cox et al., this paper is concerned with the theoretical knowledge that could be gained by the stochastic modeling in discrete form of the PSA path over time. The expected value of the PSA level is computed and compared with the deterministic model and it is found that they are the same for about the first year after radiation therapy. The American Society for Therapeutic Radiology has set a consensus panel definition of biochemical failure following radiation therapy: the rise in three consecutive levels of PSA is considered to be a failure of the radiation therapy. Knowledge of the path of PSA presented in this paper would be useful in the management of the radiation treatment and in particular assessing quantitatively any clinically based policy for defining recurrence after radiation therapy. Application of the model is illustrated by fitting it to clinical data available in the University of Michigan cancer center.

Influence of 3D-CRT pelvic irradiation on outcome in prostate cancer treated with external beam radiotherapy.

PURPOSE: The role of pelvic irradiation (PRT) in the treatment of prostate cancer remains unclear. We reviewed our institution's experience with three-dimensional conformal external beam radiotherapy (3D-CRT) during the prostate-specific antigen era to determine the influence of PRT on the risk of biochemical recurrence in patients who have a predicted risk of lymph node involvement. METHODS AND MATERIALS: Between March 1985 and January 2001, 1832 patients with clinically localized prostate cancer were treated with definitive 3D-CRT. All treatments involved CT planning to ensure coverage of the intended targets. Treatment consisted of prostate-only treatment, prostate and seminal vesicle treatment, or PRT of lymph nodes at risk followed by a boost. To create relatively homogenous analysis groups, each patient's percentage of risk of lymph node (%rLN) involvement was assigned by matching the patient's T stage, Gleason score, and initial prostate-specific antigen level to the appropriate value as described in the updated Partin tables. Three categories of %rLN involvement were defined: low, 0-5%; intermediate, >5-15%; and high, >15%. Biochemical recurrence was defined as the first occurrence of either the American Society for Therapeutic Radiology and Oncology consensus definition of prostate-specific antigen failure or the initiation of salvage hormonal therapy for any reason. RESULTS: The risk status (%rLN) could be determined for 709 low-risk, 263 intermediate-risk, and 309 high-risk patients. The actuarial freedom from biochemical recurrence (bNED) and the log-rank test for the similarity of the control and treatment survival functions are reported for each risk group. Multivariate analysis demonstrated a statistically significant benefit for the entire population treated with PRT, with a relative risk reduction of 0.72 (95% confidence interval 0.54-0.97). Although the multivariate analysis could not determine the patient population that would most benefit from PRT, the beneficial effect appeared to be most pronounced within the intermediate-risk group. Univariate analysis revealed that the intermediate-risk patients treated with PRT had an improved 2-year bNED rate, 90.1% vs. 80.6% (p = 0.02), and both low-risk and high-risk patients treated with PRT had statistically similar 2-year bNED rates compared with those who did not receive it. CONCLUSION: Pelvic 3D-CRT appears to improve bNED in prostate cancer patients. Additional studies are needed to elucidate the %rLN population for which this treatment should be recommended.