Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses

PURPOSE

A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses.

METHODS

PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions.

RESULTS

Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] v 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; P = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; P = .01).

CONCLUSION

Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.

Facedown Positioning in Macular Hole Surgery: A Systematic Review and Individual Participant Data Meta-Analysis

TOPIC

To assess the anatomical and visual effects of facedown positioning (FDP) advice in patients undergoing vitrectomy with gas tamponade for idiopathic full-thickness macular holes (FTMHs), and to explore differential treatment effects by macular hole size and FDP duration.

CLINICAL RELEVANCE

The necessity and duration of FDP for FTMH closure remain contentious, with no consensus guidelines.

METHODS

Prospectively registered systematic review and individual patient data (IPD) meta-analysis of randomised controlled trials comparing FDP to no FDP (nFDP) across the MEDLINE, Embase, and Cochrane Library databases and clinical trial registries from January 2000 to March 2023 (CRD42023395152). All adults with idiopathic FTMHs undergoing vitrectomy with gas tamponade were included. The main outcomes were primary macular hole closure and post-operative visual acuity at 6 months or nearest time point.

RESULTS

Of 8 eligible trials, 5 contributed IPD for 379 eyes and were included in our analysis. The adjusted odds ratio (OR) for primary closure with FDP versus nFDP was 2.41 (95% CI 0.98 to 5.93, P = 0.06) [GRADE: Low], translating to a relative risk (RR) of 1.08 (1.00 to 1.11) and a number needed to treat (NNT) of 15. The FDP group exhibited a mean improvement in post-operative visual acuity of -0.08 logMAR (-0.13 to -0.02, P = 0.006) [GRADE: Low] compared to the nFDP group. Benefits were more certain in participants with larger holes of minimum linear diameter ≥ 400 μm: adjusted OR for closure ranged from 1.13 to 10.12 (P = 0.030) (NNT 12), with a mean visual acuity improvement of -0.18 to -0.01 logMAR (P = 0.022). Each additional day of FDP was associated with improved odds of anatomical success (adjusted OR 1.02 to 1.41, RR 1.00 to 1.02, P = 0.026) and visual acuity improvement (-0.02 logMAR, -0.03 to -0.01, P = 0.002), possibly plateauing at 3 days.

CONCLUSION

This study provides low certainty evidence that FDP improves the anatomical and visual outcomes of macular hole surgery modestly and indicate that the effect may be more substantial for macular holes exceeding 400 μm. The findings support recommending FDP for patients with macular holes exceeding 400 μm pending further investigation.

Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials

Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials

BACKGROUND

Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.

PURPOSE

While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.

METHODS AND CONCLUSIONS

Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes

Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.

Interpreting Randomized Controlled Trials

This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.

Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity

A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.

A systematic review to summarize treatment patterns, guidelines, and characteristics of patients with renal cell carcinoma in the Asia-Pacific region

INTRODUCTION

This systematic review evaluated treatment patterns and guidelines in advanced/metastatic and adjuvant renal cell carcinoma (RCC) in the Asia-Pacific region.

AREAS COVERED

Embase, PubMed, and congresses were searched for observational studies and guidelines in accordance with PRISMA. Records published during 2016-2021 (2019-2021 for congresses) were included.

EXPERT OPINION

Nine studies and three guidelines were identified overall. In advanced/metastatic RCC, the most common treatments were tyrosine kinase inhibitors (TKIs) (notably sunitinib: 33-100%) for first-line, and everolimus (13-85%) or axitinib (2-89%) for second-line therapy. In adjuvant RCC, sunitinib was most used (54%), followed by mammalian target of rapamycin inhibitors (mTORis, 27%) with immunotherapy being less common (16%). The guidelines provided varying recommendations for advanced/metastatic RCC. For first-line in advanced/metastatic clear cell RCC (the most common subtype), guidelines recommended mTORis (everolimus for poor-risk patients) (India, 2016); clinical study enrollment for high-risk patients or TKIs for low- to medium-risk patients (China, 2019); or immunotherapy based on survival benefits over sunitinib; dose adjustment was also recommended to manage TKI toxicities (Hong Kong, 2019). The landscape remained more static in the adjuvant setting, but best practice was uncertain. No clear trends were identified in patient characteristics.

Avelumab First-line Maintenance Therapy for Advanced Urothelial Carcinoma: Comprehensive Clinical Subgroup Analyses from the JAVELIN Bladder 100 Phase 3 Trial

BACKGROUND

In the phase 3 JAVELIN Bladder 100 trial, avelumab first-line (1L) maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) versus BSC alone in patients with advanced urothelial carcinoma (aUC) who were progression-free following 1L platinum-based chemotherapy, leading to regulatory approval in various countries.

OBJECTIVE

To analyze clinically relevant subgroups from JAVELIN Bladder 100.

DESIGN, SETTING, AND PARTICIPANTS

Patients with unresectable locally advanced or metastatic UC without progression on 1L gemcitabine + cisplatin or carboplatin were randomized to receive avelumab + BSC (n = 350) or BSC alone (n = 350). Median follow-up was >19 mo in both arms (data cutoff October 21, 2019). This trial is registered on ClinicalTrials.gov as NCT02603432.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

OS (primary endpoint) and PFS were analyzed in protocol-specified and post hoc subgroups using the Kaplan-Meier method and Cox proportional hazards models.

RESULTS AND LIMITATIONS

Hazard ratios (HRs) for OS with avelumab + BSC versus BSC alone were <1.0 across all subgroups examined, including patients treated with 1L cisplatin + gemcitabine (HR 0.69, 95% confidence interval [CI] 0.50-0.93) or carboplatin + gemcitabine (HR 0.64, 95% CI 0.46-0.90), patients with PD-L1+ tumors treated with carboplatin + gemcitabine (HR 0.67, 95% CI 0.39-1.14), and patients whose best response to chemotherapy was a complete response (HR 0.80, 95% CI 0.46-1.37), partial response (HR 0.62, 95% CI 0.46-0.84), or stable disease (HR 0.70, 95% CI 0.46-1.06). Observations were similar for PFS. Limitations include the smaller size and post hoc evaluation without multiplicity adjustment for some subgroups.

CONCLUSIONS

Analyses of OS and PFS in clinically relevant subgroups were consistent with results for the overall population, further supporting avelumab 1L maintenance as standard-of-care treatment for patients with aUC who are progression-free following 1L platinum-based chemotherapy.

PATIENT SUMMARY

In the JAVELIN Bladder 100 study, maintenance treatment with avelumab helped many different groups of people with advanced cancer of the urinary tract to live longer.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned

Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Less is More? First Impressions From COSMIC-313

The COSMIC-313 phase 3 randomized controlled trial tested the triplet combination of cabozantinib with nivolumab and ipilimumab in comparison with nivolumab plus ipilimumab control as fist-line systemic therapy in metastatic clear cell renal cell carcinoma. The first results presented at the 2022 European Society of Medical Oncology Congress are a milestone for the renal cell carcinoma field because they signal the advent of triplet combinations as potential treatment options for our patients. The present commentary highlights some considerations and potential next steps based on these first impressions.

Association Between Sites of Metastasis and Outcomes With Immune Checkpoint Inhibitors in Advanced Urothelial Carcinoma

BACKGROUND

Sites of metastasis have prognostic significance in advanced urothelial carcinoma (aUC), but more information is needed regarding outcomes based on metastatic sites in patients treated with immune checkpoint inhibitors (ICI). We hypothesized that presence of liver/bone metastases would be associated with worse outcomes with ICI.

METHODS

We identified a retrospective cohort of patients with aUC across 26 institutions, collecting demographics, clinicopathological, treatment, and outcomes information. Outcomes were compared with logistic (observed response rate; ORR) and Cox (progression-free survival; PFS, overall survival; OS) regression between patients with/without metastasis beyond lymph nodes (LN) and those with/without bone/liver/lung metastasis. Analysis was stratified by 1st or 2nd+ line.

RESULTS

We identified 917 ICI-treated patients: in the 1st line, bone/liver metastases were associated with shorter PFS (Hazard ratio; HR: 1.65 and 2.54), OS (HR: 1.60 and 2.35, respectively) and lower ORR (OR: 0.48 and 0.31). In the 2nd+ line, bone/liver metastases were associated with shorter PFS (HR: 1.71 and 1.62), OS (HR: 1.76 and 1.56) and, for bone-only metastases, lower ORR (OR: 0.29). In the 1st line, LN-confined metastasis was associated with longer PFS (HR: 0.53), OS (HR:0.49) and higher ORR (OR: 2.97). In the 2nd+ line, LN-confined metastasis was associated with longer PFS (HR: 0.47), OS (HR: 0.54), and higher ORR (OR: 2.79); all associations were significant.

CONCLUSION

Bone and/or liver metastases were associated with worse, while LN-confined metastases were associated with better outcomes in patients with aUC receiving ICI. These findings in a large population treated outside clinical trials corroborate data from trial subset analyses.

The significance of sarcomatoid and rhabdoid dedifferentiation in renal cell carcinoma

Dedifferentiation in renal cell carcinoma (RCC), either sarcomatoid or rhabdoid, is an infrequent event that may occur heterogeneously in the setting of any RCC histology and is associated with poor outcomes. Sarcomatoid dedifferentiation is associated with inferior survival with angiogenesis targeted therapy and infrequent responses to cytotoxic chemotherapy. However, immune checkpoint therapy has significantly improved outcomes for patients with sarcomatoid dedifferentiation. Biologically, sarcomatoid dedifferentiation has increased programmed death-ligand 1 (PD-L1) expression and an inflamed tumor microenvironment, in addition to other distinct molecular alterations. Less is known about rhabdoid dedifferentiation from either a clinical, biological, or therapeutic perspective. In this focused review, we will discuss the prognostic implications, outcomes with systemic therapy, and underlying biology in RCC with either sarcomatoid or rhabdoid dedifferentiation present.

A Causal Framework for Making Individualized Treatment Decisions in Oncology

Simple Summary

Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps.

Abstract

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

The Big Data Paradox in Clinical Practice

The big data paradox is a real-world phenomenon whereby as the number of patients enrolled in a study increases, the probability that the confidence intervals from that study will include the truth decreases. This occurs in both observational and experimental studies, including randomized clinical trials, and should always be considered when clinicians are interpreting research data. Furthermore, as data quantity continues to increase in today's era of big data, the paradox is becoming more pernicious. Herein, I consider three mechanisms that underlie this paradox, as well as three potential strategies to mitigate it: (1) improving data quality; (2) anticipating and modeling patient heterogeneity; (3) including the systematic error, not just the variance, in the estimation of error intervals.

Missing the trees for the forest: most subgroup analyses using forest plots at the ASCO annual meeting are inconclusive

Background:

Oncologists often refer to forest plots to determine which patient subgroups may be more likely to benefit from a therapy tested in a randomized clinical trial (RCT). We sought to empirically determine the information content of subgroup comparisons from forest plots of RCTs.

Methods:

We assessed all forest plots from RCTs of therapeutic interventions presented orally at the American Society of Clinical Oncology Annual Meetings in 2020 and 2021. Subgroups were considered as showing evidence of treatment effect heterogeneity in forest plots when their confidence intervals (CIs) did not overlap with the vertical line corresponding to the main effect observed in the overall RCT cohort. Subgroups were considered as showing evidence of treatment effect homogeneity in forest plots when their CIs did not meaningfully differ, within 80–125% equivalence range, with the values compatible with the main effect. All other subgroups were considered as inconclusive.

Results:

A total of 99 forest plots were presented, and only 24.2% contained one or more subgroups suggestive of treatment effect heterogeneity. A total of 81 forest plots provided enough information to evaluate treatment effect heterogeneity and homogeneity. These 81 forest plots represented a total of 1344 individual subgroups, of which 57.2% were inconclusive, 41.1% showed evidence of treatment effect homogeneity, and 1.6% yielded evidence suggestive of treatment effect heterogeneity.

Conclusion:

The majority of subgroup comparisons were inconclusive in this empirical analysis of forest plots used in oncology RCTs. Different strategies should be considered to improve the estimation and representation of subgroup-specific effects.

Evolving Role of Adjuvant Systemic Therapy for Kidney and Urothelial Cancers

The role of adjuvant therapy in renal cell carcinoma and urothelial carcinoma is rapidly evolving. To date, the U.S. Food and Drug Administration has approved sunitinib and pembrolizumab in the adjuvant setting for renal cell carcinoma and nivolumab for urothelial carcinoma based on disease-free survival benefit. The U.S. Food and Drug Administration held a joint workshop with the National Cancer Institute and the Society of Urologic Oncology in 2017 to harmonize design elements, including eligibility and radiologic assessments across adjuvant trials in renal cell carcinoma and urothelial carcinoma. Considerations from the discussion at these workshops led the U.S. Food and Drug Administration to draft guidances to help inform subsequent adjuvant trial design for renal cell carcinoma and urothelial carcinoma. Patient-centered decision-making is crucial when determining therapeutic choices in the adjuvant setting; utility functions can be used to help quantify each patient's goals, values, and risk/benefit trade-offs to ensure that the decision regarding adjuvant therapy is informed by their preferences and the evolving outcomes data.

Medicine before and after David Cox

Herein we recount the legacy of Sir David Roxbee Cox (15 July 1924 - 18 January 2022) from the perspective of practicing clinicians. His-pioneering work in developing the logistic and Cox proportional hazard regression models revolutionized the analysis and interpretation of categorical and time-to-event survival outcomes in modern medicine. This legacy is an inspiration for all those who follow on Sir David Cox's path.

Fooled by Randomness. The Misleading Effect of Treatment Crossover in Randomized Trials of Therapies with Marginal Treatment Benefit

Crossover can bias clinical outcomes of randomized clinical trials by increasing the risk of both type I (false positive) and type II (false negative) errors. To show how crossover can increase type I error, we provide computer simulation and review herein illustrative examples (iniparib, olaratumab) of recently reported RCTs that demonstrated false-positive treatment efficacy signals due to crossover. The ethical issues associated with crossover are also discussed.

Causal Considerations Can Inform the Interpretation of Surprising Associations in Medical Registries

An exploratory analysis of registry data from 2437 patients with advanced gastric cancer revealed a surprising association between astrological birth signs and overall survival (OS) with p = 0.01. After dichotomizing or changing the reference sign, p-values <0.05 were observed for several birth signs following adjustments for multiple comparisons. Bayesian models with moderately skeptical priors still pointed to these associations. A more plausible causal model, justified by contextual knowledge, revealed that these associations arose from the astrological sign association with seasonality. This case study illustrates how causal considerations can guide analyses through what would otherwise be a hopeless maze of statistical possibilities.

Adjuvant Systemic Therapies for Patients with Renal Cell Carcinoma: Choosing Treatment Based on Patient-level Characteristics

Motivated by recent presentation of the KEYNOTE-564 interim results for adjuvant pembrolizumab in clear-cell renal cell carcinoma, we discuss concepts that can guide patient-specific decision-making in selecting individuals for whom adjuvant therapies should be offered.

Impervious to Randomness: Confounding and Selection Biases in Randomized Clinical Trials

The random allocation of therapies in randomized clinical trials is a powerful tool that removes all confounding biases that can affect treatment assignment. However, confounders influencing mediators of the treatment effect are unaffected by randomization and should be considered during trial design and statistical modeling.Examples of such mediators include biomarkers predictive of response to targeted therapies in oncology. Patient selection for such biomarkers is prudent in clinical trials. Conversely, prognostic information on outcome heterogeneity can be derived from observational datasets that include more representative populations. The fusion of experimental and observational data can then allow patient-specific inferences.

Validation of Prognostic Scores in Patients With Metastatic Urothelial Cancer Enrolling in Phase I Targeted Therapy or Next Generation Immunotherapy Trials

INTRODUCTION

Enrolling patients with metastatic urothelial carcinoma (mUC) in phase I trials provides an opportunity to identify biological drug activity. Developing prognostic scores may aid in patient selection for phase 1 trials.

PATIENTS AND METHODS

We analyzed records of patients with mUC who participated in targeted therapy and immunotherapy phase I clinical trials at MD Anderson Cancer Center (MDACC). The Bellmunt and Bajorin scores were calculated as bladder cancer-specific prognostic scores. The Royal Marsden Hospital (RMH) and MDACC scores were calculated as phase I prognostic scores. Hazard ratios (HR) were calculated using the Cox proportional hazard model. The prognostic value of the Bellmunt, Bajorin, RMH, and MDACC scores were assessed using the Likelihood ratio (LR) χ2 test and the c-index.

RESULTS

Between 2015 and 2019, 43 patients were enrolled in phase I trials and 12 were enrolled in >I trial leading to a total of 57 trial participants (TPs). Ninty-seven percent of TPs received prior platinum therapy and 60% received a prior checkpoint inhibitor. Median overall survival (OS) and progression-free survival (PFS) were significantly shorter with increasing Bajorin, RMH, or MDACC scores, but not with increasing Bellmunt score. The RMH (c-index=0.658, LR χ2=11.8, P=.008) and MDACC scores (c-index =0.66, LR χ2=12.76, P=.01) outperformed the Bajorin score (c-index=0.522, LR χ2=1.22, P=.5) and the Bellmunt score (c-index=0.537, LR χ2=0.36, P=.9) in predicting overall survivalover. The Bajorin, RMH, and MDACC scores, but not the Bellmunt score, were also predictive of progression-free survival (PFS)prog. The RMH and MDACC scores again outperformed the Bajorin scoreand the Bellmunt score for predicting PFS.

CONCLUSION

The RMH and MDACC phase I prognostic scores accurately predicted survival in patients with mUC and outperformed the bladder cancer-specific scores at time of enrollment on phase 1 clinical trials. The RMH and MDACC scores could optimize selection of patients with mUC for phase I clinical trials.