scholarly journals Impact of loss-to-follow-up on cancer survival estimates for small populations: a simulation study using Hospital-Based Cancer Registries in Japan

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e033510 ◽  
Author(s):  
Ayako Okuyama ◽  
Matthew Barclay ◽  
Cong Chen ◽  
Takahiro Higashi
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Cancer Survival ◽  
Cancer Registries ◽  
Secondary Outcome ◽  
Cancer Site ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Variable Bias

ObjectivesThe accuracy of the ascertainment of vital status impacts the validity of cancer survival. This study assesses the potential impact of loss-to-follow-up on survival in Japan, both nationally and in the samples seen at individual hospitals.DesignSimulation studySetting and participantsData of patients diagnosed in 2007, provided by the Hospital-Based Cancer Registries of 177 hospitals throughout Japan.Primary and secondary outcome measuresWe performed simulations for each cancer site, for sample sizes of 100, 1000 and 8000 patients, and for loss-to-follow-up ranging from 1% to 5%. We estimated the average bias and the variation in bias in survival due to loss-to-follow-up.ResultsThe expected bias was not associated with the sample size (with 5% loss-to-follow-up, about 2.1% for the cohort including all cancers), but a smaller sample size led to more variable bias. Sample sizes of around 100 patients, as may be seen at individual hospitals, had very variable bias: with 5% loss-to-follow-up for all cancers, 25% of samples had a bias of <1.02% and 25% of samples had a bias of > 3.06%.ConclusionSurvival should be interpreted with caution when loss-to-follow-up is a concern, especially for poor-prognosis cancers and for small-area estimates.

scholarly journals Developing a composite outcome measure for frailty prevention trials – rationale, derivation and sample size comparison with other candidate measures

2020 ◽  
Author(s):  
Miles D. Witham ◽  
James Wason ◽  
Richard M Dodds ◽  
Avan A Sayer
Keyword(s):  
Sample Size ◽  
Transition Probabilities ◽  
Adverse Outcomes ◽  
Transition Rates ◽  
Composite Outcome ◽  
Composite Measure ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Prevention Trials

Abstract Introduction Frailty is the loss of ability to withstand a physiological stressor, and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty. Methods We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates, and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes. Results The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome were between 1000 and 7200 for transition from prefrailty to frailty alone, 250 to 1600 for transition to the composite measure, and 75 to 350 using the composite measure with an ordinal logistic regression approach. Conclusion Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.

scholarly journals Developing a composite outcome measure for frailty prevention trials – rationale, derivation and sample size comparison with other candidate measures

2019 ◽  
Author(s):  
Miles D. Witham ◽  
James Wason ◽  
Richard M Dodds ◽  
Avan A Sayer
Keyword(s):  
Sample Size ◽  
Transition Probabilities ◽  
Adverse Outcomes ◽  
Transition Rates ◽  
Composite Outcome ◽  
Composite Measure ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Prevention Trials

Abstract Introduction Frailty is the loss of ability to withstand a physiological stressor, and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty. Methods We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates, and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes. Results The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome were between 1000 and 7200 for transition from prefrailty to frailty alone, 250 to 1600 for transition to the composite measure, and 75 to 350 using the composite measure with an ordinal logistic regression approach. Conclusion Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.

scholarly journals Developing a composite outcome measure for frailty prevention trials – rationale, derivation and sample size comparison with other candidate measures

2020 ◽  
Author(s):  
Miles D. Witham ◽  
James Wason ◽  
Richard M Dodds ◽  
Avan A Sayer
Keyword(s):  
Sample Size ◽  
Transition Probabilities ◽  
Adverse Outcomes ◽  
Transition Rates ◽  
Composite Outcome ◽  
Composite Measure ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Prevention Trials

Abstract Background: Frailty is the loss of ability to withstand a physiological stressor and is associated with multiple adverse outcomes in older people. Trials to prevent or ameliorate frailty are in their infancy. A range of different outcome measures have been proposed, but current measures require either large sample sizes, long follow-up, or do not directly measure the construct of frailty. Methods: We propose a composite outcome for frailty prevention trials, comprising progression to the frail state, death, or being too unwell to continue in a trial. To determine likely event rates, we used data from the English Longitudinal Study for Ageing, collected 4 years apart. We calculated transition rates between non-frail, prefrail, frail or loss to follow up due to death or illness. We used Markov state transition models to interpolate one- and two-year transition rates and performed sample size calculations for a range of differences in transition rates using simple and composite outcomes. Results: The frailty category was calculable for 4650 individuals at baseline (2226 non-frail, 1907 prefrail, 517 frail); at follow up, 1282 were non-frail, 1108 were prefrail, 318 were frail and 1936 had dropped out or were unable to complete all tests for frailty. Transition probabilities for those prefrail at baseline, measured at wave 4 were respectively 0.176, 0.286, 0.096 and 0.442 to non-frail, prefrail, frail and dead/dropped out. Interpolated transition probabilities were 0.159, 0.494, 0.113 and 0.234 at two years, and 0.108, 0.688, 0.087 and 0.117 at one year. Required sample sizes for a two-year outcome in a two-arm trial were between 1040 and 7242 for transition from prefrailty to frailty alone, 246 to 1630 for transition to the composite measure, and 76 to 354 using the composite measure with an ordinal logistic regression approach. Conclusion: Use of a composite outcome for frailty trials offers reduced sample sizes and could ameliorate the effect of high loss to follow up inherent in such trials due to death and illness.

scholarly journals A Menu-driven Facility for Complex Sample Size Calculation in Randomized Controlled Trials with a Survival or a Binary Outcome

2002 ◽  
Vol 2 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Patrick Royston ◽  
Abdel Babiker
Keyword(s):  
Sample Size ◽  
Sample Size Calculation ◽  
Binary Outcome ◽  
Test Statistic ◽  
Logrank Test ◽  
Loss To Follow Up ◽  
Patient Allocation ◽  
Hazard Ratios ◽  
Event Distribution

We present a menu-driven Stata program for the calculation of sample size or power for complex clinical trials with a survival time or a binary outcome. The features supported include up to six treatment arms, an arbitrary time-to-event distribution, fixed or time-varying hazard ratios, unequal patient allocation, loss to follow-up, staggered patient entry, and crossover of patients from their allocated treatment to an alternative treatment. The computations of sample size and power are based on the logrank test and are done according to the asymptotic distribution of the logrank test statistic, adjusted appropriately for the design features.

scholarly journals Comparing Current and Emerging Practice Models For the Extrapolation of Survival Data: A Simulation Study and Case-Study

2021 ◽  
Author(s):  
Benjamin Kearns ◽  
Matt D. Stevenson ◽  
Kostas Triantafyllopoulos ◽  
Andrea Manca
Keyword(s):  
Simulation Study ◽  
Current Practice ◽  
Additive Models ◽  
Survival Models ◽  
Sample Sizes ◽  
Practice Models ◽  
Long Term Follow Up

Abstract BackgroundEstimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations.MethodsBoth a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up.ResultsThe emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period. ConclusionsGood within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.

scholarly journals Adaptive design with bayesian informed interim decisions: application to a randomized trial of mechanical circulatory support

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
R Mukherjee ◽  
N Muehlemann ◽  
A Bhingare ◽  
G W Stone ◽  
C Mehta
Keyword(s):  
Sample Size ◽  
Hazard Rate ◽  
Mechanical Circulatory Support ◽  
Predictive Power ◽  
Adaptive Design ◽  
Proportional Hazards ◽  
Circulatory Support ◽  
Sample Sizes ◽  
Large Sample

Abstract Background Cardiovascular trials increasingly require large sample sizes and long follow-up periods. Several approaches have been developed to optimize sample size such as adaptive group sequential trials, samples size re-estimation based on the promising zone, and the win ratio. Traditionally, the log-rank or the Cox proportional hazards model is used to test for treatment effects, based on a constant hazard rate and proportional hazards alternatives, which however, may not always hold. Large sample sizes and/or long follow up periods are especially challenging for trials evaluating the efficacy of acute care interventions. Purpose We propose an adaptive design wherein using interim data, Bayesian computation of predictive power guides the increase in sample size and/or the minimum follow-up duration. These computations do not depend on the constant hazard rate and proportional hazards assumptions, thus yielding more robust interim decision making for the future course of the trial. Methods PROTECT IV is designed to evaluate mechanical circulatory support with the Impella CP device vs. standard of care during high-risk PCI. The primary endpoint is a composite of all-cause death, stroke, MI or hospitalization for cardiovascular causes with initial minimum follow-up of 12 months and initial enrolment of 1252 patients with expected recruitment in 24 months. The study will employ an adaptive increase in sample size and/or minimum follow-up at the Interim analysis when ∼80% of patients have been enrolled. The adaptations utilize extensive simulations to choose a new sample size up to 2500 and new minimal follow-up time up to 36 months that provides a Bayesian predictive power of 85%. Bayesian calculations are based on patient-level information rather than summary statistics therefore enabling more reliable interim decisions. Constant or proportional hazard assumptions are not required for this approach because two separate Piece-wise Constant Hazard Models with Gamma-priors are fitted to the interim data. Bayesian predictive power is then calculated using Monte-Carlo methodology. Via extensive simulations, we have examined the utility of the proposed design for situations with time varying hazards and non-proportional hazards ratio such as situations of delayed treatment effect (Figure) and crossing of survival curves. The heat map of Bayesian predictive power obtained when the interim Kaplan-Meier curves reflected delayed response shows that for this scenario an optimal combination of increased sample size and increased follow-up time would be needed to attain 85% predictive power. Conclusion A proposed adaptive design with sample size and minimum follow-up period adaptation based on Bayesian predictive power at interim looks allows for de-risking the trial of uncertainties regarding effect size in terms of control arm outcome rate, hazard ratio, and recruitment rate. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Abiomed, Inc Figure 1

scholarly journals Sample size and classification error for Bayesian change-point models with unlabelled sub-groups and incomplete follow-up

2016 ◽  
Vol 27 (5) ◽  
pp. 1476-1497 ◽  
Author(s):  
Simon R White ◽  
Graciela Muniz-Terrera ◽  
Fiona E Matthews
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Change Point ◽  
Fixed Effect ◽  
Classification Error ◽  
Extended Model ◽  
Ecological Processes ◽  
Effect Change ◽  
Change Point Models

Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up. Through a simulation study, we consider the relationship of sample size to the estimates of the underlying shape, the existence of a change-point, and the classification-error of sub-group labels. We use a Bayesian framework to account for the missing labels, and the analysis of each simulation is performed using standard Markov chain Monte Carlo techniques. Our simulation study is inspired by cognitive decline as measured by the Mini-Mental State Examination, where our extended model is appropriate due to the commonly observed mixture of individuals within studies who do or do not exhibit accelerated decline. We find that even for studies of modest size ( n = 500, with 50 individuals observed past the change-point) in the fixed effect setting, a change-point can be detected and reliably estimated across a range of observation-errors.

Impact of immune checkpoint inhibitor (CPI) and EGFR tyrosine kinase inhibitor (TKI) sequence on time to treatment failure (TTF) among EGFR plus NSCLC treated in a community-based cancer research network.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 9099-9099
Author(s):  
Carissa Jones ◽  
Rebecca Lachs ◽  
Emma Sturgill ◽  
Amanda Misch ◽  
Caressa Lietman ◽  
...  
Keyword(s):  
Kinase Inhibitor ◽  
Survival Rates ◽  
Research Network ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Kaplan Meier ◽  
Oncogenic Driver ◽  
The Impact

9099 Background: The development of CPIs and driver-targeted TKIs has transformed the treatment of NSCLC and increased survival rates. However, the role of CPIs in patients with oncogenic-driven NSCLC remains an area of investigation. We sought to examine the impact of CPI sequence on treatment response among patients with oncogenic-driver mutation-positive NSCLC. Methods: Patients with NSCLC being treated within the Sarah Cannon Research Institute network were identified through Genospace, Sarah Cannon’s clinico-genomic analytics platform. Advanced stage oncogenic-driven tumors (driver+) were defined as those with a record of receiving an FDA-approved TKI targeting EGFR, ALK, RET, ROS1, NTRK, MET, or BRAF. Kaplan-Meier estimates were used to examine TTF (defined as time from therapy start to start of next therapy, death, or loss to follow-up) and overall survival (OS). Results: We identified 12,352 patients with lung cancer and available therapy data (2005-2020), including 2,270 (18%) driver+ patients. Eleven percent (N=245) of driver+ patients received a CPI, including 120 (49%) with CPI prior to TKI, 122 (50%) with CPI post TKI, and 3 (1%) who received CPI both pre and post TKI. The CPI TTF was significantly longer for those who received CPI post TKI compared to those who received it prior (Table). EGFR+ tumors accounted for 82% (N=1,867) of driver+ patients, 10% of whom (N=188) received a CPI. Of the EGFR+/CPI+ patients, 78 patients (41%) received CPI prior to TKI, 107 (57%) received CPI post TKI, and 3 (2%) received CPI both pre and post TKI. EGFR+ tumors exposed to a CPI post TKI had a longer CPI TTF compared to patients who received it prior (Table). In contrast, there was no difference in length of benefit from TKI if it was received pre vs. post CPI (Table). There was also no difference in OS based on sequence of TKI and CPI (p=0.88). Larger sample sizes are needed for analysis of additional driver-stratified cohorts. Conclusions: Patients with oncogenic-driven NSCLC benefited from CPI longer when it was administered after TKI compared to before. Importantly, therapy sequence only affected length of benefit from CPIs and did not affect length of benefit from TKIs. This effect was present in EGFR+ NSCLC, but sample sizes were too small to determine if the same is true for other oncogenic-drivers. Therapy sequence had no impact on OS, indicating the presence of additional clinical, therapeutic, and/or genomic factors contributing to disease progression. Continued research is needed to better understand markers of CPI response in driver+ NSCLC.[Table: see text]

A Simulation Study on the Performance of Different Reliability Estimation Methods

2019 ◽  
Author(s):  
Ashley Edwards ◽  
Keanan Joyner ◽  
Chris Schatschneider
Keyword(s):  
Lower Bound ◽  
Sample Size ◽  
Internal Consistency ◽  
Simulation Study ◽  
Reliability Estimation ◽  
Estimation Methods ◽  
Continuous Data ◽  
Alpha Omega ◽  
Accurate Reflection

The accuracy of certain internal consistency estimators have been questioned in recent years. The present study tests the accuracy of six reliability estimators (Cronbach’s alpha, Omega, Omega Hierarchical, Revelle’s Omega, and Greatest Lower Bound) in 140 simulated conditions of unidimensional continuous data with uncorrelated errors with varying sample sizes, number of items, population reliabilities, and factor loadings. Under these conditions, alpha and omega yielded the most accurate estimations of the population reliability simulated. Alpha consistently underestimated population reliability and demonstrated evidence for itself as a lower bound. Greater underestimations for alpha were observed when tau equivalence was not met, however, underestimations were small and still provided more accurate estimates than all of the estimators except omega. Estimates of reliability were shown to be impacted by sample size, degree of violation of tau equivalence, population reliability and number of items in a scale. Under the conditions simulated here, estimates quantified by alpha and omega yielded the most accurate reflection of population reliability values. A follow-up regression comparing alpha and omega revealed alpha to be more sensitive to degree of violation of tau equivalence whereas omega was impacted greater by sample size and number of items, especially when population reliability was low.

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