'Thursday's child has far to go'-interpreting subgroups and the STAMPEDE trial

Randomised controlled trial of adjunctive triamcinolone acetonide in eyes undergoing vitreoretinal surgery following open globe trauma: The ASCOT study

BACKGROUND/AIMS

To investigate the clinical effectiveness of adjunctive triamcinolone acetonide (TA) given at the time of vitreoretinal surgery following open globe trauma (OGT).

METHODS

A phase 3, multicentre, double-masked randomised controlled trial of patients undergoing vitrectomy following OGT comparing adjunctive TA (intravitreal and subtenons) against standard care (2014-2020). The primary outcome was the proportion of patients with at least 10 Early Treatment Diabetic Retinopathy Study (ETDRS) letter improvement in corrected visual acuity (VA) at 6 months. Secondary outcomes included: change in ETDRS, retinal detachment (RD) secondary to PVR, retinal reattachment, macular reattachment, tractional RD, number of operations, hypotony, elevated intraocular pressure and quality of life.

RESULTS

280 patients were randomised over 75 months, of which 259 completed the study. 46.9% (n=61/130) of patients in the treatment group had a 10-letter improvement in VA compared with 43.4% (n=56/129) of the control group (difference 3.5% (95% CI -8.6% to 15.6%), OR=1.03 (95% CI 0.61 to 1.75), p=0.908)). Secondary outcome measures also failed to show any treatment benefit. For two of the secondary outcome measures, stable complete retinal and macular reattachment, outcomes were worse in the treatment group compared with controls, respectively, 51.6% (n=65/126) vs 64.2% (n=79/123), OR=0.59 (95% CI 0.36 to 0.99), and 54.0% (n=68/126) vs 66.7% (n=82/123), OR=0.59 (95% CI 0.35 to 0.98), for TA vs control.

CONCLUSION

The use of combined intraocular and sub-Tenons capsule TA is not recommended as an adjunct to vitrectomy surgery following OGT.

TRIAL REGISTRATION NUMBER

NCT02873026.

Precision rehabilitation for aphasia by patient age, sex, aphasia severity, and time since stroke? A prespecified, systematic review-based, individual participant data, network, subgroup meta-analysis

BACKGROUND

Stroke rehabilitation interventions are routinely personalized to address individuals' needs, goals, and challenges based on evidence from aggregated randomized controlled trials (RCT) data and meta-syntheses. Individual participant data (IPD) meta-analyses may better inform the development of precision rehabilitation approaches, quantifying treatment responses while adjusting for confounders and reducing ecological bias.

AIM

We explored associations between speech and language therapy (SLT) interventions frequency (days/week), intensity (h/week), and dosage (total SLT-hours) and language outcomes for different age, sex, aphasia severity, and chronicity subgroups by undertaking prespecified subgroup network meta-analyses of the RELEASE database.

METHODS

MEDLINE, EMBASE, and trial registrations were systematically searched (inception-Sept2015) for RCTs, including ⩾ 10 IPD on stroke-related aphasia. We extracted demographic, stroke, aphasia, SLT, and risk of bias data. Overall-language ability, auditory comprehension, and functional communication outcomes were standardized. A one-stage, random effects, network meta-analysis approach filtered IPD into a single optimal model, examining SLT regimen and language recovery from baseline to first post-intervention follow-up, adjusting for covariates identified a-priori. Data were dichotomized by age (⩽/> 65 years), aphasia severity (mild-moderate/ moderate-severe based on language outcomes' median value), chronicity (⩽/> 3 months), and sex subgroups. We reported estimates of means and 95% confidence intervals. Where relative variance was high (> 50%), results were reported for completeness.

RESULTS

959 IPD (25 RCTs) were analyzed. For working-age participants, greatest language gains from baseline occurred alongside moderate to high-intensity SLT (functional communication 3-to-4 h/week; overall-language and comprehension > 9 h/week); older participants' greatest gains occurred alongside low-intensity SLT (⩽ 2 h/week) except for auditory comprehension (> 9 h/week). For both age-groups, SLT-frequency and dosage associated with best language gains were similar. Participants ⩽ 3 months post-onset demonstrated greatest overall-language gains for SLT at low intensity/moderate dosage (⩽ 2 SLT-h/week; 20-to-50 h); for those > 3 months, post-stroke greatest gains were associated with moderate-intensity/high-dosage SLT (3-4 SLT-h/week; ⩾ 50 hours). For moderate-severe participants, 4 SLT-days/week conferred the greatest language gains across outcomes, with auditory comprehension gains only observed for ⩾ 4 SLT-days/week; mild-moderate participants' greatest functional communication gains were associated with similar frequency (⩾ 4 SLT-days/week) and greatest overall-language gains with higher frequency SLT (⩾ 6 days/weekly). Males' greatest gains were associated with SLT of moderate (functional communication; 3-to-4 h/weekly) or high intensity (overall-language and auditory comprehension; (> 9 h/weekly) compared to females for whom the greatest gains were associated with lower-intensity SLT (< 2 SLT-h/weekly). Consistencies across subgroups were also evident; greatest overall-language gains were associated with 20-to-50 SLT-h in total; auditory comprehension gains were generally observed when SLT > 9 h over ⩾ 4 days/week.

CONCLUSIONS

We observed a treatment response in most subgroups' overall-language, auditory comprehension, and functional communication language gains. For some, the maximum treatment response varied in association with different SLT-frequency, intensity, and dosage. Where differences were observed, working-aged, chronic, mild-moderate, and male subgroups experienced their greatest language gains alongside high-frequency/intensity SLT. In contrast, older, moderate-severely impaired, and female subgroups within 3 months of aphasia onset made their greatest gains for lower-intensity SLT. The acceptability, clinical, and cost effectiveness of precision aphasia rehabilitation approaches based on age, sex, aphasia severity, and chronicity should be evaluated in future clinical RCTs.

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.

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.

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.

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

We argue that well-informed patient-specific decision-making may be carried out as three consecutive tasks: (1) estimating key parameters of a statistical model, (2) using prognostic information to convert these parameters into clinically interpretable values, and (3) specifying joint utility functions to quantify risk-benefit trade-offs between clinical outcomes. Using the management of metastatic clear cell renal cell carcinoma as our motivating example, we explain the role of prognostic covariates that characterize between-patient heterogeneity in clinical outcomes. We show that explicitly specifying the joint utility of clinical outcomes provides a coherent basis for patient-specific decision-making.

Oligometastatic Prostate Cancer Should Be Studied and Treated Differently to High-volume Disease. Con: The Underlying Biology is the Same, So They Should Not Be Treated Differently

The biology of oligo- and polymetastatic disease is likely to be similar. However, the prognosis is different and this may drive different therapy choices. Men should not be deprived of life-prolonging therapies unless the benefits of these therapies are outweighed by the risks from competing causes of mortality.

Abiraterone acetate for early stage metastatic prostate cancer: patient selection and special considerations

In recent years, therapeutic advances, together with new medication sequences and combinations, have improved outcomes for prostate cancer. For a long time, androgen deprivation therapy (ADT) has been the standard of care for newly diagnosed, metastatic prostate cancer, first as a standalone therapy and then in combination with taxane-based regimens. More recently, the addition of abiraterone acetate to ADT to achieve complete androgen blockade has proven beneficial for the treatment of metastatic hormone-resistant prostate cancer and metastatic hormone-sensitive prostate cancer (mHSPC). In this review, we summarize recent findings on the early use of abiraterone in mHSPC and discuss survival benefits as reported in clinical trials. On the basis of existing data, abiraterone in combination with ADT could be considered a new standard of care for patients affected by mHSPC.

Contemporary role of postoperative radiotherapy for prostate cancer

While radical prostatectomy (RP) has provided long-term disease control for the majority of patients with localized prostate cancer (CaP), nearly 30% of all surgical patients have disease progression. For high-risk patients, more than half of men experience disease recurrence within 10 years. Postoperative radiotherapy is the only known potentially curative treatment for a large number of patients following prostatectomy. Lately, there have been several advances with the potential to improve outcomes for patients undergoing postoperative radiotherapy. This article will give an overview of the existing literature and current controversies on: (I) timing of postoperative radiation; (II) use of concomitant androgen deprivation therapy; (III) optimal dose to the prostate bed; (IV) use of hypofractionation; (V) elective treatment of the pelvic lymph nodes; (VI) novel imaging modalities, and (VII) genomic biomarkers.