Sample Size In Clinical Trials Research Articles

Design of randomized clinical trials with a binary endpoint: Conditional versus unconditional analyses of a two-by-two table.

When designing a randomized clinical trial to compare two treatments, the sample size required to have desired power with a specified type 1 error depends on the hypothesis testing procedure. With a binary endpoint (e.g., response), the trial results can be displayed in a 2 × 2 table. If one does the analysis conditional on the number of positive responses, then using Fisher's exact test has an actual type 1 error less than or equal to the specified nominal type 1 error. Alternatively, one can use one of many unconditional "exact" tests that also preserve the type 1 error and are less conservative than Fisher's exact test. In particular, the unconditional test of Boschloo is always at least as powerful as Fisher's exact test, leading to smaller required sample sizes for clinical trials. However, many statisticians have argued over the years that the conditional analysis with Fisher's exact test is the only appropriate procedure. Since having smaller clinical trials is an extremely important consideration, we review the general arguments given for the conditional analysis of a 2 × 2 table in the context of a randomized clinical trial. We find the arguments not relevant in this context, or, if relevant, not completely convincing, suggesting the sample-size advantage of the unconditional tests should lead to their recommended use. We also briefly suggest that since designers of clinical trials practically always have target null and alternative response rates, there is the possibility of using this information to improve the power of the unconditional tests.

Effect size varies based on calculation method and may affect interpretation of treatment effect: an illustration using randomised clinical trials in osteoarthritis

BackgroundTo illustrate how (standardised) effect sizes (ES) vary based on calculation method and to provide considerations for improved reporting.MethodsData from three trials of tanezumab in subjects with osteoarthritis were analyzed. ES of tanezumab versus comparator for WOMAC Pain (outcome) was defined as least squares difference between means (mixed model for repeated measures analysis) divided by a pooled standard deviation (SD) of outcome scores. Three approaches to computing the SD were evaluated: Baseline (the pooled SD of WOMAC Pain values at baseline [pooled across treatments]); Endpoint (the pooled SD of these values at the time primary endpoints were assessed); and Median (the median pooled SD of these values based on the pooled SDs across available timepoints). Bootstrap analyses were used to compute 95% confidence intervals (CI).ResultsES (95% CI) of tanezumab 2.5 mg based on Baseline, Endpoint, and Median SDs in one study were − 0.416 (− 0.796, − 0.060), − 0.195 (− 0.371, − 0.028), and − 0.196 (− 0.373, − 0.028), respectively; negative values indicate pain improvement. This pattern of ES differences (largest with Baseline SD, smallest with Endpoint SD, Median SD similar to Endpoint SD) was consistent across all studies and doses of tanezumab.ConclusionDifferences in ES affect interpretation of treatment effect. Therefore, we advocate clearly reporting individual elements of ES in addition to its overall calculation. This is particularly important when ES estimates are used to determine sample sizes for clinical trials, as larger ES will lead to smaller sample sizes and potentially underpowered studies.Trial RegistrationClinicaltrials.gov NCT02697773, NCT02709486, and NCT02528188.

Visual Pathway Outcomes Based on Sample Size in Clinical Neuroprotection Research

This cohort study calculates clinical trial sample sizes powered by visual pathway outcomes of acute optic neuritis in neuroprotection research.

The use of melatonin in the treatment of irritable bowel syndrome: a systematic review of randomized placebo-controlled clinical trials

Introduction. One of the most prevalent functional disorders of the gastrointestinal system, irritable bowel syndrome (IBS), significantly affects patients’ quality of life. At the moment, only symptomatic drugs are used to treat this disorder. Although using melatonin can be thought of as a form of pathogenetic therapy, its efficacy has not yet been conclusively demonstrated. This systematic review of randomized placebo controlled clinical trials reviews current data on the evaluation of the efficacy of melatonin in patients with IBS. Material and research methods. The search and selection of publications was carried out in the PubMed, ScienceDirect, ClinicalTrials.gov, Cochrane Library, eLibrary, medRxiv and International Clinical Trials Registry Platform databases from their publication until October 30, 2022. Results. The inclusion criteria were met by four randomized placebo-controlled trials, totaling 155 participants, and these trials were included in the subsequent analysis. All investigations revealed a considerable reduction in the frequency and severity of bloating, as well as a tendency for other intestinal symptoms to regress. Two trials reported an improvement in patients’ quality of life, albeit one of the studies did not find these changes to be statistically significant, and the other did not assess them. This review demonstrates that melatonin is effective in treating IBS by lowering the frequency and severity of the condition’s symptoms in the vast majority of published RCTs. The poor number and sample size of clinical trials, along with their inadequate methodological quality, prevent us from drawing any firm conclusions about the contribution of melatonin to the improvement of IBS patients’ quality of life. To confirm melatonin’s significance in the treatment of IBS patients, larger studies are required to assess its efficacy and safety when used in various doses.

Rosie, a Health Education Question-and-Answer Chatbot for New Mothers: Randomized Pilot Study.

Stark disparities exist in maternal and child outcomes and there is a need to provide timely and accurate health information. In this pilot study, we assessed the feasibility and acceptability of a health chatbot for new mothers of color. Rosie, a question-and-answer chatbot, was developed as a mobile app and is available to answer questions about pregnancy, parenting, and child development. From January 9, 2023, to February 9, 2023, participants were recruited using social media posts and through engagement with community organizations. Inclusion criteria included being aged ≥14 years, being a woman of color, and either being currently pregnant or having given birth within the past 6 months. Participants were randomly assigned to the Rosie treatment group (15/29, 52% received the Rosie app) or control group (14/29, 48% received a children's book each month) for 3 months. Those assigned to the treatment group could ask Rosie questions and receive an immediate response generated from Rosie's knowledgebase. Upon detection of a possible health emergency, Rosie sends emergency resources and relevant hotline information. In addition, a study staff member, who is a clinical social worker, reaches out to the participant within 24 hours to follow up. Preintervention and postintervention tests were completed to qualitatively and quantitatively evaluate Rosie and describe changes across key health outcomes, including postpartum depression and the frequency of emergency room visits. These measurements were used to inform the clinical trial's sample size calculations. Of 41 individuals who were screened and eligible, 31 (76%) enrolled and 29 (71%) were retained in the study. More than 87% (13/15) of Rosie treatment group members reported using Rosie daily (5/15, 33%) or weekly (8/15, 53%) across the 3-month study period. Most users reported that Rosie was easy to use (14/15, 93%) and provided responses quickly (13/15, 87%). The remaining issues identified included crashing of the app (8/15, 53%), and users were not satisfied with some of Rosie's answers (12/15, 80%). Mothers in both the Rosie treatment group and control group experienced a decline in depression scores from pretest to posttest periods, but the decline was statistically significant only among treatment group mothers (P=.008). In addition, a low proportion of treatment group infants had emergency room visits (1/11, 9%) compared with control group members (3/13, 23%). Nonetheless, no between-group differences reached statistical significance at P<.05. Rosie was found to be an acceptable, feasible, and appropriate intervention for ethnic and racial minority pregnant women and mothers of infants owing to the chatbot's ability to provide a personalized, flexible tool to increase the timeliness and accessibility of high-quality health information to individuals during a period of elevated health risks for the mother and child. ClinicalTrials.gov NCT06053515; https://clinicaltrials.gov/study/NCT06053515.

Immunotherapy for mucosal melanoma

Abstract Mucosal melanoma (MM) is extremely rare in Caucasians, whereas it is the second predominant melanoma subtype in Asian and other non-Caucasian populations. Distinct from cutaneous melanoma in terms of epidemiology, biology, and molecular characteristics, MM is characterized by more aggressive biological behavior, lower mutational burden, more chromosomal structure variants, and poorer prognosis. Because of the rarity of MM, its biological features are not fully understood, and potential novel therapies are less well depicted. Whereas immunotherapy has shown encouraging efficacy for cutaneous melanoma, its efficacy in MM is unclear due to limited sample sizes in clinical trials. Thus, in this review, we describe the epidemiological, clinical, and molecular features of MM and summarize the efficacies of different immunotherapies for MM, including immune checkpoint inhibitors, vaccines, oncolytic virus therapy, adoptive T-cell therapy, and various combination therapies.

Phenotype and natural history of mitochondrial membrane protein-associated neurodegeneration.

Mitochondrial membrane protein-associated neurodegeneration (MPAN) is an ultraorphan neurogenetic disease from the group of neurodegeneration with brain iron accumulation (NBIA) disorders. Here we report cross-sectional and longitudinal data to define the phenotype, to assess disease progression and to estimate sample sizes for clinical trials. We enrolled patients with genetically confirmed MPAN from the Treat Iron-Related Childhood-Onset Neurodegeneration (TIRCON) registry and cohort study, and from additional sites. Linear mixed-effect modelling (LMEM) was used to calculate annual progression rates for the Unified Parkinson's Disease Rating Scale (UPDRS), Barry-Albright Dystonia (BAD) scale, Schwab and England Activities of Daily Living (SE-ADL) scale and the Pediatric Quality of Life Inventory (PedsQL). We investigated 85 MPAN patients cross-sectionally, with functional outcome data collected in 45. Median age at onset was 9 years and the median diagnostic delay was 5 years. The most common findings were gait disturbance (99%), pyramidal involvement (95%), dysarthria (90%), vision disturbances (82%), with all but dysarthria presenting early in the disease course. After 16 years with the disease, 50% of patients were wheelchair dependent. LMEM showed an annual progression rate of 4.5 points in total UPDRS. The total BAD scale score showed no significant progression over time. The SE-ADL scale and the patient- and parent-reported PedsQL showed a decline of 3.9%, 2.14 and 2.05 points, respectively. No patient subpopulations were identified based on longitudinal trajectories. Our cross-sectional results define the order of onset and frequency of symptoms in MPAN, which will inform the diagnostic process, help to shorten diagnostic delay and aid in counselling patients, parents and caregivers. Our longitudinal findings define the natural history of MPAN, reveal the most responsive outcomes and highlight the need for an MPAN-specific rating approach. Our sample size estimations inform the design of upcoming clinical trials.

Potential of ChatGPT in facilitating research in radiation oncology?

PurposeTo evaluate the potential of the artificial intelligence (AI) chatbot ChatGPT in supporting young clinical scientists with scientific tasks in radio oncological research. Materials and MethodsSeven scientific tasks were to be completed in 3 h by 8 radiation oncologists with different scientific experience working at a university hospital: creation of a scientific synopsis, creation of a research question and corresponding clinical trial hypotheses, writing of the first paragraph of a manuscript introduction, clinical trial sample size calculation, and clinical data analyses (multivariate analysis, boxplot and survival curve). No participant had prior experience with an AI chatbot. All participants were instructed in ChatGPT v3.5 and its use was provided for all tasks. Answers were scored independently by two blinded experts. The subjective value of ChatGPT was rated by each participant. Data were analyzed with regression-, t-test and Spearman correlation (p < 0.05). ResultsParticipants completed tasks 1–3 with an average score of 50% and 4–7 with 56%. Scientific experience, number of original publications and of first/last authorships showed a positive correlation with overall scoring (p = 0.01–0.04). Participants with little to moderate scientific experience scored ChatGPT to be more helpful in solving tasks 4–7 compared to more experienced participants (p = 0.04), with simultaneously presenting lower scorings (p = 0.03). ConclusionsChatGPT did not compensate for differences in scientific experience of young clinical scientists, with less experienced researchers believing false AI-generated scientific results.

At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis causes degeneration of motor neurons, resulting in progressive muscle weakness and impairment in motor function. Promising drug development efforts have accelerated in amyotrophic lateral sclerosis, but are constrained by a lack of objective, sensitive, and accessible outcome measures. Here we investigate the use of wearable sensors, worn on four limbs at home during natural behavior, to quantify motor function and disease progression in 376 individuals with amyotrophic lateral sclerosis. We use an analysis approach that automatically detects and characterizes submovements from passively collected accelerometer data and produces a machine-learned severity score for each limb that is independent of clinical ratings. We show that this approach produces scores that progress faster than the gold standard Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (−0.86 ± 0.70 SD/year versus −0.73 ± 0.74 SD/year), resulting in smaller clinical trial sample size estimates (N = 76 versus N = 121). This method offers an ecologically valid and scalable measure for potential use in amyotrophic lateral sclerosis trials and clinical care.

Meta-analysis of efficacy outcome measures across immune checkpoint inhibitors (ICIs) and targeted therapies (TTs) in melanoma to guide future clinical trial design.

e21520 Background: Systemic treatments (Tx) for unresectable or metastatic melanoma include TTs and ICIs, with TT+ICI combinations under exploration. Heterogeneity in clinical trial sample size and subject characteristics pose obstacles for treatment class comparisons. We applied meta-analysis to elucidate Tx and covariate effects on efficacy outcomes in melanoma. Methods: A PICOS-guided literature search was conducted from 2000-2022 for TT or ICI given as monotherapy or in combination for unresectable or metastatic melanoma. Median progression-free survival (mPFS), median overall survival (mOS), and overall response rate (ORR) were recorded. Random-effects models were fit to endpoints with study arms weighted by variance. Covariate inclusion was based on likelihood-ratio tests (P &lt; 0.05). For simulation, parameter distributions from weighted linear regression (wi ~ nstudy) of mPFS to ORR were sampled (N = 5000) with residual error scaled by mean Phase 3 arm size. ORR uncertainty was modeled by normal approximation. Results: From 134 publications, 100 (mPFS), 73 (mOS), and 111 (ORR) outcomes were retained, from 53, 44, and 63 studies, respectively, and classified by treatment. For all endpoints, Tx classification was an outcome predictor (P&lt; 0.001). BRAFi+MEKi+PD-1 combination therapy had the greatest predicted duration of mPFS (16.7 months [11.8, 23.6]) and ORR (62.8% [53.7%, 71.1%]). mPFS and mOS outcomes were reduced by prior Tx failure and evidence of brain metastases, with mOS also impacted by elevated lactate dehydrogenase. I2 statistics for the final models were 89.0% (mPFS), 73.9% (mOS), and 51.8% (ORR). A robust correlation between ORR and mPFS (R2= 0.77, P &lt; 0.001) was observed across all treatment classes. The predictive power of this relationship was demonstrated by simulating the probability of achieving target Phase 3 mPFS outcomes from ORR derived from as few as 20 patients. Conclusions: This meta-analysis characterizes the shifting space of Tx in unresectable or metastatic melanoma and provides a model-based framework for simulation of efficacy endpoints and trial designs for TT+ICI combinations. ORR-based predictions of trial success illustrate the value of utilizing early endpoints to inform decisions in drug development, across all Tx options in melanoma. Further investigation will include longitudinal PFS and OS modeling and refinement of PFS predictions from tumor growth metrics and clinical benefit rate.

HTA50 HTA Bodies Acceptance of Indirect Treatment Comparisons for Gene Therapies in US, England and Wales, France and Germany

Gene therapies (GTs) often lack comparators in their pivotal clinical trials when they first seek market access, especially when targeting rare diseases where only symptomatic treatment exists. Furthermore, rare diseases represent a challenge in terms of patient population and clinical trial sample size. Therefore, GT manufacturers resort to single-arm trials and develop indirect treatment comparisons (ITCs) for health technology assessment (HTA). Our aim is to understand HTA bodies’ evidence requirements for ITCs for GTs, and identify key drivers and limitations.

Sample size in clinical trials – challenges and approaches

Sample size estimation is a necessary and crucial step in clinical trial research. Statistical requirements, limited patient availability and high financial risk of a clinical trial necessitate the proper calculation of this measure. The aim of this paper is to discuss the reasons why the estimation of the sample size is important and, based on the obtained results, to show how this process may be completed in selected cases. Stochastic simulations based on the Monte Carlo methods approach are applied. Therefore, new challenges facing this area of research are mentioned.

Impact of motion correction on [18F]-MK6240 tau PET imaging

Objective. Positron emission tomography (PET) imaging of tau deposition using [18F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer’s disease (AD) treatment. Approach. After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions’ standard deviations across subjects from motion and non-motion-corrected images. Main results. Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by −49%, −24%, −18%, and −16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively. Significance. The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.

There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

A novel power prior approach for borrowing historical control data in clinical trials.

There has been an increased interest in borrowing information from historical control data to improve the statistical power for hypothesis testing, therefore reducing the required sample sizes in clinical trials. To account for the heterogeneity between the historical and current trials, power priors are often considered to discount the information borrowed from the historical data. However, it can be challenging to choose a fixed power prior parameter in the application. The modified power prior approach, which defines a random power parameter with initial prior to control the amount of historical information borrowed, may not directly account for heterogeneity between the trials. In this paper, we propose a novel approach to pick a power prior based on some direct measures of distributional differences between historical control data and current control data under normal assumptions. Simulations are conducted to investigate the performance of the proposed approach compared with current approaches (e.g. commensurate prior, meta-analytic-predictive, and modified power prior). The results show that the proposed power prior improves the study power while controlling the type I error within a tolerable limit when the distribution of the historical control data is similar to that of the current control data. The method is developed for both superiority and non-inferiority trials and is illustrated with an example from vaccine clinical trials.

Polygenic Effect on Tau Pathology Progression in Alzheimer's Disease.

Polygenic variation accounts for a substantial portion of the risk of Alzheimer's disease (AD), but its effect on the rate of fibrillar-tau accumulation as a key driver of dementia symptoms is unclear. We combined the to-date largest number of genetic risk variants of AD (n=85 lead single-nucleotide polymorphisms [SNPs]) from recent genome-wide association studies (GWAS) to generate a polygenic score (PGS). We assessed longitudinal tau-positron emission tomography (PET), amyloid-PET, and cognition in 231 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using the PGS, together with global amyloid-PET, we predicted the rate of tau-PET increases in Braak-stage regions-of-interest and cognitive decline. We also assessed PGS-risk enrichment effects on the required sample size in clinical trials targeting tau pathology. We found that a higher PGS was associated with higher rates of tau-PET accumulation, in particular at elevated amyloid-PET levels. The tau-PET increases mediated the association between PGS and faster cognitive decline. Risk enrichment through high PGS afforded sample size savings by 34%. Our results demonstrate that the PGS predicts faster tau progression and thus cognitive decline, showing utility to enhance statistical power in clinical trials. ANN NEUROL 2023;93:819-829.

Relationship between the inclusion/exclusion criteria and sample size in randomized controlled trials for SARS-CoV-2 entry inhibitors

The coronavirus disease 2019 (COVID-19) pandemic that has been ongoing since 2019 is still ongoing and how to control it is one of the international issues to be addressed. Antiviral drugs that reduce the viral load in terms of reducing the risk of secondary infection are important. For the general control of emerging infectious diseases, establishing an efficient method to evaluate candidate therapeutic agents will lead to a rapid response. We evaluated clinical trial designs for viral entry inhibitors that have the potential to be effective pre-exposure prophylactic drugs in addition to reducing viral load after infection. We used a previously developed simulation of clinical trials based on a mathematical model of within-host viral infection dynamics to evaluate sample sizes in clinical trials of viral entry inhibitors against COVID-19. We assumed four measures as outcomes, namely change in log10-transformed viral load from symptom onset, PCR positive ratio, log10-transformed viral load, and cumulative viral load, and then sample sizes were calculated for drugs with 99 % and 95 % antiviral efficacy. Consistent with previous results, we found that sample sizes could be dramatically reduced for all outcomes used in an analysis by adopting inclusion/exclusion criteria such that only patients in the early post-infection period would be included in a clinical trial. A comparison of sample sizes across outcomes demonstrated an optimal measurement schedule associated with the nature of the outcome measured for the evaluation of drug efficacy. In particular, the sample sizes calculated from the change in viral load and from viral load tended to be small when measurements were taken at earlier time points after treatment initiation. For the cumulative viral load, the sample size was lower than that from the other outcomes when the stricter inclusion/exclusion criteria to include patients whose time since onset is earlier than 2 days was used. We concluded that the design of efficient clinical trials should consider the inclusion/exclusion criteria and measurement schedules, as well as outcome selection based on sample size, personnel and budget needed to conduct the trial, and the importance of the outcome regarding the medical and societal requirements. This study provides insights into clinical trial design for a variety of situations, especially addressing infectious disease prevalence and feasible trial sizes. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Genes predisposing tunneled catheter thrombosis in hemodialysis patients: Determination of sample size in clinical trials.

Genes predisposing tunneled catheter thrombosis in hemodialysis patients: Determination of sample size in clinical trials.

Temporal order of clinical and biomarker changes in familial frontotemporal dementia.

Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design

To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical trials. Cross-sectional study. Glaucoma patients with good quality macula and ONH scans enrolled in 2 longitudinal studies, the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovations in Glaucoma Study. Spectralis macula posterior pole scans and ONH circle scans on 3327 pairs of GCIPL/RNFL scans from 1096 eyes (550 patients) were included. Participants were randomly distributed into a training and validation dataset (90%) and a test dataset (10%) by participant. Networks had access to GCIPL and RNFL data from one hemiretina of the probe eye and all data of the fellow eye. The models were then trained to predict the GCIPL or RNFL thickness of the remaining probe eye hemiretina. Mean absolute error (MAE) and squared Pearson correlation coefficient (r2) were used to evaluate model performance. The deep learning model was able to predict superior and inferior GCIPL thicknesses with a global r2 value of 0.90 and 0.86, r2 of mean of 0.90 and 0.86, and mean MAE of 3.72 μm and 4.2 μm, respectively. For superior and inferior RNFL thickness predictions, model performance was slightly lower, with a global r2 of 0.75 and 0.84, r2 of mean of 0.81 and 0.82, and MAE of 9.31 μm and 8.57 μm, respectively. There was only a modest decrease in model performance when predicting GCIPL and RNFL in more severe disease. Using individualized hemiretinal predictions to account for variability across patients, we estimate that a clinical trial can detect a difference equivalent to a 25% treatment effect over 24 months with an 11-fold reduction in the number of patients compared to a conventional trial. Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness. Using an internal control based on these model predictions may help reduce clinical trial sample size requirements and facilitate investigation of new glaucoma neuroprotection therapies. Proprietary or commercial disclosure may be found after the references.