scholarly journals A comparison of one‐stage vs two‐stage individual patient data meta‐analysis methods: A simulation study

2018 ◽  
Vol 9 (3) ◽  
pp. 417-430 ◽  
Author(s):  
Evangelos Kontopantelis
Keyword(s):  
Simulation Study ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Two Stage ◽  
One Stage ◽  
Analysis Methods

Weniger Erwerbsminderungsrenten nach der kardiologischen Rehabilitation durch intensivierte Nachsorge?

2021 ◽  
Author(s):  
Sarah Schröer ◽  
Wolfgang Mayer-Berger ◽  
Claudia Pieper
Keyword(s):  
Fixed Effects ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Odds Ratios ◽  
Two Stage ◽  
Kardiologische Rehabilitation ◽  
One Stage

Zusammenfassung Ziel Ziel war es die Daten aus 3 randomisierten kontrollierten Studien, in denen Nachsorgekonzepte im Rahmen der kardiologischen Rehabilitation evaluiert wurden, in Form einer Pooling-Studie zusammenzufassen, um stärker belastbare Erkenntnisse über den nachsorgeassoziierten weiteren Verlauf der Patienten und Patientinnen im Anschluss an die Rehabilitation zu gewinnen. Nachfolgend werden die Auswirkungen von poststationärer Nachsorge auf das Erwerbsminderungsrisiko kardiologischer Rehabilitanden und Rehabilitandinnen vorgestellt. Methodik Aus 3 randomisierten kontrollierten Primärstudien (SeKoNa, Sinko, OptiHyp), in denen als Intervention jeweils ein intensiviertes (telefongestütztes) poststationäres Nachsorgekonzept mit einer unbehandelten Kontrollgruppe verglichen wurde, stehen umfangreiche Daten zu soziodemografischen, klinischen und diagnostischen Charakteristika auf Individualebene zur Verfügung. Mittels einer im August 2019 durchgeführten Sekundärdatenanalyse von Routinedaten der Deutschen Rentenversicherung Rheinland wurden als primäre Outcomeparameter Mortalität (alle Ursachen), bewilligte Erwerbsminderungsrenten sowie bewilligte Anträge auf eine erneute kardiologische Rehabilitation zum individuellen Stichtag 3 Jahre nach Rehabilitationsende als Endpunkte erhoben. Die Daten wurden als Meta-Analyse für individuelle Patientendaten (Individual Patient Data Meta-Analysis IPD-MA) unter Verwendung klassischer meta-analytischer Techniken (One-Stage Approach mittels gemischter Modelle und Two-Stage Approach mit inverser Varianzschätzung als Fixed Effects Modell) gepoolt und über Risiko-Odds-Ratios vergleichend ausgewertet. Ergebnisse Das Gesamtkollektiv besteht aus insgesamt 1058 kardiologischen Rehabilitanden und Rehabilitandinnen, die im Zeitraum zwischen 2004 und 2015 stationäre rehabilitative Leistungen der Deutschen Rentenversicherung Rheinland in der kardiologischen Rehabilitationseinrichtung Klinik Roderbirken in Leichlingen in Anspruch genommen haben. Die gepoolte Interventionsgruppe (poststationäre Nachsorge) und die gepoolte Kontrollgruppe (Standardbehandlung) unterschieden sich zum Ausgangspunkt (Entlassung nach 3-wöchiger Rehabilitation) nicht. Hinweise auf statistische Heterogenität liegt nicht vor. Drei Jahre nach Rehabilitationsende betrug die inzidente Erwerbsminderungsrentenquote 11,8% der Gesamtstichprobe. Bei Teilnahme an einem poststationären Nachsorgekonzept war das Erwerbsminderungsrisiko gegenüber der Kontrollgruppe um rund 60% reduziert (OR: 0,43; 95% CI: 0,36–0,51). Schlussfolgerung Rehabilitation und Wiedereingliederung gewinnen weiter an Bedeutung, um die Gefahr von gesundheitlich bedingten vorzeitigen Erwerbsausstiegen mit erheblichen sozioökonomischen Folgen für Betroffene und das Sozialversicherungssystem zu vermeiden. Nachsorgeaktivitäten, die Rehabilitationserfolge über Dauer einer mehrwöchigen Rehabilitation hinaus erhalten, unterstützen die Prävention von gesundheitlich bedingten vorzeitigen Erwerbsminderungsrenten effektiv und nachhaltig und sollten das bestehende Rehabilitationsangebot komplettieren. Aus unseren Ergebnissen folgern wir, dass Nachsorge lange genug (mindestens ein Jahr) und im persönlichen Kontakt erfolgen muss.

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses

2008 ◽  
Vol 24 (03) ◽  
pp. 358-361 ◽  
Author(s):  
Laura Koopman ◽  
Geert J. M. G. van der Heijden ◽  
Arno W. Hoes ◽  
Diederick E. Grobbee ◽  
Maroeska M. Rovers
Keyword(s):  
Individual Patient Data ◽  
Meta Analysis ◽  
Acute Otitis Media ◽  
Patient Data ◽  
Published Data ◽  
Summary Statistics ◽  
Two Stage ◽  
Data Set ◽  
Meta Analyses ◽  
Subgroup Effects

Objectives:Individual patient data (IPD) meta-analyses have been proposed as a major improvement in meta-analytic methods to study subgroup effects. Subgroup effects of conventional and IPD meta-analyses using identical data have not been compared. Our objective is to compare such subgroup effects using the data of six trials (n= 1,643) on the effectiveness of antibiotics in children with acute otitis media (AOM).Methods:Effects (relative risks, risk differences [RD], and their confidence intervals [CI]) of antibiotics in subgroups of children with AOM resulting from (i) conventional meta-analysis using summary statistics derived from published data (CMA), (ii) two-stage approach to IPD meta-analysis using summary statistics derived from IPD (IPDMA-2), and (iii) one-stage approach to IPD meta-analysis where IPD is pooled into a single data set (IPDMA-1) were compared.Results:In the conventional meta-analysis, only two of the six studies were included, because only these reported on relevant subgroup effects. The conventional meta-analysis showed larger (age < 2 years) or smaller (age ≥ 2 years) subgroup effects and wider CIs than both IPD meta-analyses (age < 2 years: RDCMA-21 percent, RDIPDMA-1-16 percent, RDIPDMA-2-15 percent; age ≥2 years: RDCMA-5 percent, RDIPDMA-1-11 percent, RDIPDMA-2-11 percent). The most important reason for these discrepant results is that the two studies included in the conventional meta-analysis reported outcomes that were different both from each other and from the IPD meta-analyses.Conclusions:This empirical example shows that conventional meta-analyses do not allow proper subgroup analyses, whereas IPD meta-analyses produce more accurate subgroup effects. We also found no differences between the one- and two-stage meta-analytic approaches.

scholarly journals Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Loukia M. Spineli ◽  
Katerina Papadimitropoulou ◽  
Chrysostomos Kalyvas
Keyword(s):  
Mixture Model ◽  
Simulation Study ◽  
Meta Analysis ◽  
Credible Interval ◽  
Outcome Data ◽  
Systematic Bias ◽  
Two Stage ◽  
Pattern Mixture Model ◽  
One Stage ◽  
The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance (τ2), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and τ2 in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher τ2 estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and τ2 when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

scholarly journals Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

2020 ◽  
Author(s):  
Loukia Maria Spineli ◽  
Katerina Papadimitropoulou ◽  
Chrysostomos Kalyvas
Keyword(s):  
Empirical Study ◽  
Mixture Model ◽  
Simulation Study ◽  
Meta Analysis ◽  
Outcome Data ◽  
Systematic Bias ◽  
Two Stage ◽  
Pattern Mixture Model ◽  
One Stage ◽  
The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The advantages of the one-stage over the two-stage approach have been documented extensively in the literature. Little is known how these approaches behave in the presence of missing outcome data (MOD) which are ubiquitous in trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis Bayesian framework to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches with respect to the relative treatment effects of several competing interventions and the between-trial variance ( {\tau }^{2} ). We categorised the networks according to the extent and balance of MOD in the included trials. To complement the empirical study, we conducted a simulation study to compare the competing approaches regarding bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and {\tau }^{2} in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. Furthermore, in these networks, the empirical results revealed slightly higher {\tau }^{2} estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and {\tau }^{2} when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

scholarly journals Meta-analysis of Gaussian individual patient data: Two-stage or not two-stage?

2018 ◽  
Vol 37 (9) ◽  
pp. 1419-1438 ◽  
Author(s):  
Tim P. Morris ◽  
David J. Fisher ◽  
Michael G. Kenward ◽  
James R. Carpenter
Keyword(s):  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Two Stage

scholarly journals Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

2020 ◽  
Author(s):  
Loukia Maria Spineli ◽  
Katerina Papadimitropoulou ◽  
Chrysostomos Kalyvas
Keyword(s):  
Mixture Model ◽  
Simulation Study ◽  
Meta Analysis ◽  
Credible Interval ◽  
Outcome Data ◽  
Systematic Bias ◽  
Two Stage ◽  
Pattern Mixture Model ◽  
One Stage ◽  
The One

Abstract Background: Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately.Methods: We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance ( ), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and in the presence of moderate and large MOD.Results: The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and when compared with the two-stage approach for large MOD.Conclusions: Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

scholarly journals Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

2020 ◽  
Author(s):  
Loukia Maria Spineli ◽  
Katerina Papadimitropoulou ◽  
Chrysostomos Kalyvas
Keyword(s):  
Mixture Model ◽  
Simulation Study ◽  
Meta Analysis ◽  
Credible Interval ◽  
Outcome Data ◽  
Systematic Bias ◽  
Two Stage ◽  
Pattern Mixture Model ◽  
One Stage ◽  
The One

Abstract Background: Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately.Methods: We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance ( ), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and in the presence of moderate and large MOD.Results: The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and when compared with the two-stage approach for large MOD.Conclusions: Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

Sign in / Sign up

Export Citation Format

Share Document