scholarly journals Conflating marginal and conditional treatment effects: Comments on “Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study”

2021 ◽  
Vol 40 (11) ◽  
pp. 2753-2758
Author(s):  
Antonio Remiro‐Azócar ◽  
Anna Heath ◽  
Gianluca Baio
Keyword(s):  
Simulation Study ◽  
Treatment Effects ◽  
Indirect Comparisons ◽  
Population Adjustment

scholarly journals Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study

2020 ◽  
Vol 39 (30) ◽  
pp. 4885-4911
Author(s):  
David M. Phillippo ◽  
Sofia Dias ◽  
A. E. Ades ◽  
Nicky J. Welton
Keyword(s):  
Simulation Study ◽  
Indirect Comparisons ◽  
Population Adjustment

scholarly journals Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals

2019 ◽  
Vol 35 (03) ◽  
pp. 221-228 ◽  
Author(s):  
David M. Phillippo ◽  
Sofia Dias ◽  
Ahmed Elsada ◽  
A. E. Ades ◽  
Nicky J. Welton
Keyword(s):  
Cross Validation ◽  
Individual Patient Data ◽  
Statistical Significance ◽  
Health Technology ◽  
Effective Sample Size ◽  
Simulation Studies ◽  
The United Kingdom ◽  
Treatment Comparisons ◽  
Indirect Comparisons ◽  
Population Adjustment

AbstractObjectivesIndirect comparisons via a common comparator (anchored comparisons) are commonly used in health technology assessment. However, common comparators may not be available, or the comparison may be biased due to differences in effect modifiers between the included studies. Recently proposed population adjustment methods aim to adjust for differences between study populations in the situation where individual patient data are available from at least one study, but not all studies. They can also be used when there is no common comparator or for single-arm studies (unanchored comparisons). We aim to characterise the use of population adjustment methods in technology appraisals (TAs) submitted to the United Kingdom National Institute for Health and Care Excellence (NICE).MethodsWe reviewed NICE TAs published between 01/01/2010 and 20/04/2018.ResultsPopulation adjustment methods were used in 7 percent (18/268) of TAs. Most applications used unanchored comparisons (89 percent, 16/18), and were in oncology (83 percent, 15/18). Methods used included matching-adjusted indirect comparisons (89 percent, 16/18) and simulated treatment comparisons (17 percent, 3/18). Covariates were included based on: availability, expert opinion, effective sample size, statistical significance, or cross-validation. Larger treatment networks were commonplace (56 percent, 10/18), but current methods cannot account for this. Appraisal committees received results of population-adjusted analyses with caution and typically looked for greater cost effectiveness to minimise decision risk.ConclusionsPopulation adjustment methods are becoming increasingly common in NICE TAs, although their impact on decisions has been limited to date. Further research is needed to improve upon current methods, and to investigate their properties in simulation studies.

scholarly journals What is a multiple treatments meta-analysis?

2012 ◽  
Vol 21 (2) ◽  
pp. 151-153 ◽  
Author(s):  
A. Cipriani ◽  
C. Barbui ◽  
C. Rizzo ◽  
G. Salanti
Keyword(s):  
Evidence Based Medicine ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Evidence Based ◽  
Relative Efficacy ◽  
Analytical Technique ◽  
Multiple Treatments ◽  
Meta Analyses ◽  
Based Medicine ◽  
Indirect Comparisons

Standard meta-analyses are an effective tool in evidence-based medicine, but one of their main drawbacks is that they can compare only two alternative treatments at a time. Moreover, if no trials exist which directly compare two interventions, it is not possible to estimate their relative efficacy. Multiple treatments meta-analyses use a meta-analytical technique that allows the incorporation of evidence from both direct and indirect comparisons from a network of trials of different interventions to estimate summary treatment effects as comprehensively and precisely as possible.

scholarly journals Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kim Jachno ◽  
Stephane Heritier ◽  
Rory Wolfe
Keyword(s):  
Clinical Trials ◽  
Simulation Study ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Proportional Hazards ◽  
Time Dependent ◽  
Time To Event ◽  
Baseline Hazard ◽  
Event Rates ◽  
Analytical Approaches

Abstract Background Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard. Methods A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes. Results In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels. Conclusions Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

scholarly journals Estimating the Power of Indirect Comparisons: A Simulation Study

PLoS ONE ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. e16237 ◽  
Author(s):  
Edward J. Mills ◽  
Isabella Ghement ◽  
Christopher O'Regan ◽  
Kristian Thorlund
Keyword(s):  
Simulation Study ◽  
Indirect Comparisons

scholarly journals Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

2012 ◽  
Vol 22 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Richard Wyss ◽  
Cynthia J. Girman ◽  
Robert J. LoCasale ◽  
M. Alan Brookhart ◽  
Til Stürmer
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Simulation Study ◽  
Treatment Effects ◽  
Multiple Outcomes ◽  
Selection For
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