TY - CHAP
T1 - Stroke Treatment Academic Industry Roundtable Recommendations for Individual Data Pooling Analyses in Stroke
AU - Lees, Kennedy R.
AU - Khatri, Pooja
AU - Alexandrov, Andrei V.
AU - Bivard, Andrew
AU - Boltze, Johannes
AU - Broderick, Joseph P.
AU - Campbell, Bruce C.V.
AU - Creighton, Francis M.
AU - Fiorella, David
AU - Furlan, Anthony J.
AU - Gorelick, Philip B.
AU - Hess, David C.
AU - Kim, Won Ki
AU - Latour, Lawrence L.
AU - Liebeskind, David S.
AU - Luby, Marie
AU - Lyden, Patrick
AU - Lynch, John Kylan
AU - Marshall, Randolph S.
AU - Menon, Bijoy K.
AU - Muir, Keith W.
AU - Palesch, Yuko
AU - Peng, Helen
AU - Pryor, Kent E.
AU - Mocco, J.
AU - Rasmussen, Peter
AU - Sacco, Ralph L.
AU - Schwamm, Lee H.
AU - Smith, Eric E.
AU - Solberg, Yoram
AU - Vagal, Achala
AU - Warach, Steven
AU - Wechsler, Lawrence R.
AU - Wintermark, Max
AU - Yoo, Albert J.
AU - Zander, Kay M.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Pooled analysis of individual patient data from stroke trials can deliver more precise estimates of treatment effect, enhance power to examine prespecified subgroups, and facilitate exploration of treatment-modifying influences. Analysis plans should be declared, and preferably published, before trial results are known. For pooling trials that used diverse analytic approaches, an ordinal analysis is favored, with justification for considering deaths and severe disability jointly. Because trial pooling is an incremental process, analyses should follow a sequential approach, with statistical adjustment for iterations. Updated analyses should be published when revised conclusions have a clinical implication. However, caution is recommended in declaring pooled findings that may prejudice ongoing trials, unless clinical implications are compelling. All contributing trial teams should contribute to leadership, data verification, and authorship of pooled analyses. Development work is needed to enable reliable inferences to be drawn about individual drug or device effects that contribute to a pooled analysis, versus a class effect, if the treatment strategy combines >= 2 such drugs or devices. Despite the practical challenges, pooled analyses are powerful and essential tools in interpreting clinical trial findings and advancing clinical care.
AB - Pooled analysis of individual patient data from stroke trials can deliver more precise estimates of treatment effect, enhance power to examine prespecified subgroups, and facilitate exploration of treatment-modifying influences. Analysis plans should be declared, and preferably published, before trial results are known. For pooling trials that used diverse analytic approaches, an ordinal analysis is favored, with justification for considering deaths and severe disability jointly. Because trial pooling is an incremental process, analyses should follow a sequential approach, with statistical adjustment for iterations. Updated analyses should be published when revised conclusions have a clinical implication. However, caution is recommended in declaring pooled findings that may prejudice ongoing trials, unless clinical implications are compelling. All contributing trial teams should contribute to leadership, data verification, and authorship of pooled analyses. Development work is needed to enable reliable inferences to be drawn about individual drug or device effects that contribute to a pooled analysis, versus a class effect, if the treatment strategy combines >= 2 such drugs or devices. Despite the practical challenges, pooled analyses are powerful and essential tools in interpreting clinical trial findings and advancing clinical care.
U2 - 10.1161/STROKEAHA.116.012966
DO - 10.1161/STROKEAHA.116.012966
M3 - Chapter
C2 - 27406108
SN - 0039-2499
T3 - Stroke
SP - 2154
EP - 2159
BT - Stroke
PB - Lippincott Williams and Wilkins
ER -
