@article {931, title = {Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27,247 adults in 15 prospective studies.}, journal = {Int J Epidemiol}, volume = {35}, year = {2006}, month = {2006 Dec}, pages = {1570-8}, abstract = {

BACKGROUND: Within-person variability in measured values of a risk factor can bias its association with disease. The extent of this regression dilution bias for plasma fibrinogen was investigated using repeat measurement data collected at varying time intervals on 27 247 adults in 15 prospective studies.

METHODS: Regression dilution ratios (RDRs) were estimated from a linear regression of repeat measurements on baseline values in each study and for each time interval, and pooled allowing for within- and between-study heterogeneity. RDRs were estimated both without and with adjustment for confounders, and factors were investigated that might influence the RDRs.

RESULTS: The unadjusted overall RDR was 0.51 (95\% CI: 0.47, 0.55), which decreased to 0.46 (95\% CI: 0.42, 0.49) after adjustment for age, sex and measured values of other established vascular risk factors. The RDR did not vary materially by assay method, age, sex or smoking status, but decreased at higher levels of baseline fibrinogen.

CONCLUSION: It is appropriate to use an RDR of 0.5 to correct approximately for regression dilution bias in plasma fibrinogen values; however, this correction factor may produce somewhat conservative hazard ratios in adjusted analyses, at higher fibrinogen concentrations and in follow-up beyond a decade. More generally, the methods described in this report have widespread applicability to quantifying regression dilution bias in repeatability data from multiple prospective studies.

}, keywords = {Adult, Age Factors, Bias, Cardiovascular Diseases, Female, Fibrinogen, Humans, Male, Meta-Analysis as Topic, Prospective Studies, Regression Analysis, Risk Factors, Sex Factors, Smoking, Time Factors}, issn = {0300-5771}, doi = {10.1093/ije/dyl233}, author = {Wood, Angela M and White, Ian and Thompson, Simon G and Lewington, Sarah and Danesh, John} } @article {1079, title = {Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.}, journal = {Stat Med}, volume = {28}, year = {2009}, month = {2009 Apr 15}, pages = {1218-37}, abstract = {

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts

}, keywords = {Cohort Studies, Computer Simulation, Coronary Disease, Data Interpretation, Statistical, Female, Fibrinogen, Humans, Male, Meta-Analysis as Topic, Models, Statistical}, issn = {0277-6715}, doi = {10.1002/sim.3540}, author = {Jackson, Dan and White, Ian and Kostis, J B and Wilson, A C and Folsom, A R and Wu, K and Chambless, L and Benderly, M and Goldbourt, U and Willeit, J and Kiechl, S and Yarnell, J W G and Sweetnam, P M and Elwood, P C and Cushman, M and Psaty, B M and Tracy, R P and Tybjaerg-Hansen, A and Haverkate, F and de Maat, M P M and Thompson, S G and Fowkes, F G R and Lee, A J and Smith, F B and Salomaa, V and Harald, K and Rasi, V and Vahtera, E and Jousilahti, P and D{\textquoteright}Agostino, R and Kannel, W B and Wilson, P W F and Tofler, G and Levy, D and Marchioli, R and Valagussa, F and Rosengren, A and Wilhelmsen, L and Lappas, G and Eriksson, H and Cremer, P and Nagel, D and Curb, J D and Rodriguez, B and Yano, K and Salonen, J T and Nyyss{\"o}nen, K and Tuomainen, T-P and Hedblad, B and Engstrom, G and Berglund, G and Loewel, H and Koenig, W and Hense, H W and Meade, T W and Cooper, J A and De Stavola, B and Knottenbelt, C and Miller, G J and Cooper, J A and Bauer, K A and Rosenberg, R D and Sato, S and Kitamura, A and Naito, Y and Iso, H and Salomaa, V and Harald, K and Rasi, V and Vahtera, E and Jousilahti, P and Palosuo, T and Ducimetiere, P and Amouyel, P and Arveiler, D and Evans, A E and Ferrieres, J and Juhan-Vague, I and Bingham, A and Schulte, H and Assmann, G and Cantin, B and Lamarche, B and Despr{\'e}s, J-P and Dagenais, G R and Tunstall-Pedoe, H and Lowe, G D O and Woodward, M and Ben-Shlomo, Y and Davey Smith, G and Palmieri, V and Yeh, J L and Meade, T W and Rudnicka, A and Brennan, P and Knottenbelt, C and Cooper, J A and Ridker, P and Rodeghiero, F and Tosetto, A and Shepherd, J and Lowe, G D O and Ford, I and Robertson, M and Brunner, E and Shipley, M and Feskens, E J M and Di Angelantonio, E and Kaptoge, S and Lewington, S and Lowe, G D O and Sarwar, N and Thompson, S G and Walker, M and Watson, S and White, I R and Wood, A M and Danesh, J} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after {\textquoteright}recalibration{\textquoteright}, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at {\textquoteright}high{\textquoteright} 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10\%, 52\%, and 41\%, respectively, whereas RRS under-predicted by 10\%. Original versions of algorithms classified 29-39\% of individuals aged >=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24\% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} }