Sample sizes for proportional hazards survival studies with arbitrary patient entry and loss to follow-up distributions

1992 ◽  
Vol 11 (8) ◽  
pp. 1103-1113 ◽  
Author(s):  
Nigel A. Yateman ◽  
Allan M. Skene
Keyword(s):  
Proportional Hazards ◽  
Sample Sizes ◽  
Loss To Follow Up ◽  
Survival Studies

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 Faktor risiko loss to follow up terapi ARV pada pasien HIV

2017 ◽  
Vol 33 (4) ◽  
pp. 173
Author(s):  
Listy Handayani ◽  
Riris Andono Ahmad ◽  
Yanri Wijayanti Subronto
Keyword(s):  
Risk Factors ◽  
Health Insurance ◽  
Antiretroviral Therapy ◽  
Protective Factor ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Loss To Follow Up ◽  
Kaplan Meier ◽  
Adherence Counseling

Risk factors for loss to follow up of antiretroviral therapy in HIV patientsPurposeThis study aimed to determine risk factors for loss to follow-up of antiretroviral therapy among HIV-infected patients in Dr. Sardjito Yogyakarta, 2011-2014.MethodsA retrospective cohort study was conducted involving 499 HIV patients. Observations were conducted for four years using medical records. Data analysis was performed using Kaplan-Meier and Cox proportional hazards regression tests.ResultsThere were 190 loss to follow-up patients. Risk factors for loss to follow-up of ARV therapy were: a student (AHR = 2.42; 95% CI = 1.20-4.89), the distance ≥ 10 km (AHR = 1.58; 95% CI = 1:09 to 2:31), using health insurance (AHR = 1.67; 95% CI = 1:11 to 2:51) and homosexual as a protective factor of loss to follow-up of antiretroviral therapy (HR = 0:49; 95% CI = 0.30-0.80).ConclusionBeing a college student, the distance between home and ARV service ≥10 km and using health insurance were the risk factors for loss to follow-up of ARV treatment. Adherence counseling for students, cooperation with the drug taking supervisor and decentralization ARV service, as well as effective and efficient services for patients who use health insurance need to be strengthened.

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

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]

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 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.

Loss to follow up among men who have sex with men and heterosexual men living with HIV in Haiti

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
W Dunbar ◽  
N Sohler ◽  
Y Coppieters
Keyword(s):  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Hiv Treatment ◽  
Hiv Care ◽  
Rank Test ◽  
Heterosexual Men ◽  
Loss To Follow Up ◽  
Sex With Men ◽  
Living With Hiv

Abstract Background Despite the benefits of adherence in HIV medication, health systems are struggling to keep all categories of patients in care due to loss to follow up (LTFU). Men who have sex with men (MSM) are at higher risk of HIV infection and also face several barriers to reach treatment, it is hypothesized that they may also have higher incidence of dropping-off. This study aims to determine whether MSM living with HIV have a greater risk of LTFU compared with heterosexual men and to identify the risk factors for the two groups. Methods A retrospective matched cohort study of electronic medical record data from 554 patients living with HIV and enrolled in care between 2015 and 2018 at a Port-au-Prince-based HIV clinic was performed. The 125 MSM and 429 heterosexual patients were matched on gender age and enrolment date. The primary outcome was LTFU defined as not refilling an ART prescription for a period of 90 days. MSM and heterosexual men was compared using t-tests and chi-square tests. The Kaplan-Meier technique was used to estimate time to LTFU after initiation of ART and the Cox Proportional Hazards regression model was used to determine predictors of LTFU. Results The sample had a mean age of 31.1 years (SD 8.0) for MSM and 32.4 years (SD 7.7) for heterosexual men. LTFU was significantly more common among the MSM group than the heterosexual group (MSM 48.8%, heterosexual men 34.7%; p = 0.012). Factors associated with LTFU were greater amongst younger patients, with lower educational and economic level. The median time to LTFU for MSM was 679 days and 1110 days for heterosexual men. The log rank test showed that this is statistically significant at p = 0.001. Conclusions This study showed that the risk of LTFU is significantly higher and the time to LTFU is significantly shorter for MSM relative to heterosexual men. Identifying predictors to LTFU in HIV clinical settings and providing appropriate services and supports are important steps in addressing this issue. Key messages Men who have sex with men continue to face barriers to effective HIV treatment in Haiti. Adapted interventions are needed to improve HIV care for Men who have sex with Men in Haiti.

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