Pravastatin In Ischaemic Disease Study Research Articles

Semiparametric methods for multistate survival models in randomised trials

Transform methods have proved effective for networks describing a progression of events. In semi-Markov networks, we calculated the transform of time to a terminating event from corresponding transforms of intermediate steps. Saddlepoint inversion then provided survival and hazard functions, which integrated, and fully utilised, the network data. However, the presence of censored data introduces significant difficulties for these methods. Many participants in controlled trials commonly remain event-free at study completion, a consequence of the limited period of follow-up specified in the trial design. Transforms are not estimable using nonparametric methods in states with survival truncated by end-of-study censoring. We propose the use of parametric models specifying residual survival to next event. As a simple approach to extrapolation with competing alternative states, we imposed a proportional incidence (constant relative hazard) assumption beyond the range of study data. No proportional hazards assumptions are necessary for inferences concerning time to endpoint; indeed, estimation of survival and hazard functions can proceed in a single study arm. We demonstrate feasibility and efficiency of transform inversion in a large randomised controlled trial of cholesterol-lowering therapy, the Long-Term Intervention with Pravastatin in Ischaemic Disease study. Transform inversion integrates information available in components of multistate models: estimates of transition probabilities and empirical survival distributions. As a by-product, it provides some ability to forecast survival and hazard functions forward, beyond the time horizon of available follow-up. Functionals of survival and hazard functions provide inference, which proves sharper than that of log-rank and related methods for survival comparisons ignoring intermediate events.

Changes in Lipoprotein‐Associated Phospholipase A2 Activity Predict Coronary Events and Partly Account for the Treatment Effect of Pravastatin: Results From the Long‐term Intervention with Pravastatin in Ischemic Disease Study

BackgroundLipoprotein‐associated phospholipase A2 (Lp‐PLA2) levels are associated with coronary heart disease (CHD) in healthy individuals and in patients who have had ischemic events.Methods and ResultsThe Long‐term Intervention with Pravastatin in Ischemic Disease (LIPID) study randomized 9014 patients with cholesterol levels of 4.0 to 7.0 mmol/L to placebo or pravastatin 3 to 36 months after myocardial infarction or unstable angina and showed a reduction in CHD and total mortality. We assessed the value of baseline and change in Lp‐PLA2 activity to predict outcomes over a 6‐year follow‐up, the effect of pravastatin on Lp‐PLA2 levels, and whether pravastatin treatment effect was related to Lp‐PLA2 activity change. Lp‐PLA2 was measured at randomization and 1 year, and levels were grouped as quartiles. The prespecified end point was CHD death or nonfatal myocardial infarction. Baseline Lp‐PLA2 activity was positively associated with CHD events (P<0.001) but not after adjustment for 23 baseline factors (P=0.66). In 6518 patients who were event free at 1 year, change in Lp‐PLA2 was a significant independent predictor of subsequent CHD events after adjustment for these risk factors, including LDL cholesterol and LDL cholesterol changes (P<0.001). Pravastatin reduced Lp‐PLA2 by 16% compared with placebo (P<0.001). After adjustment for Lp‐PLA2 change, the pravastatin treatment effect was reduced from 23% to 10% (P=0.26), with 59% of the treatment effect accounted for by changes in Lp‐PLA2. Similar reductions in treatment effect were seen after adjustment for LDL cholesterol change.ConclusionReduction in Lp‐PLA2 activity during the first year was a highly significant predictor of CHD events, independent of change in LDL cholesterol, and may account for over half of the benefits of pravastatin in the LIPID study.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals. We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR = 0.71, 95% CI 0.60-0.83). However, the treatment effect was not significant in women due to smaller sample size (HR = 0.75, 95% CI 0.51-1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p = 0.24 for men and p = 0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6-4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4-13.6). The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors. The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

Statin trials and goals of cholesterol-lowering therapy after AMI

Several randomized clinical trials using statins in the prevention of coronary heart disease (CHD) have demonstrated benefit, both in terms of retardation of the progression of signs of coronary atherosclerosis and in reduced morbidity and mortality rates. Three of these trials have examined the long-term effect of statins in patients with previous myocardial infarction. The Scandinavian Simvastatin Survival Study (4S) showed that a mean reduction of low-density-lipoprotein (LDL) cholesterol by 35% reduced coronary mortality rates by 42% and total mortality rates by 30%. In the Cholesterol and Recurrent Events trial, a 28% reduction in LDL-cholesterol was associated with a reduction in major coronary events of 24%. In the Long Term Intervention with Pravastatin in Ischemic Disease study, the 25% LDL-cholesterol reduction produced a 24% reduction in coronary disease mortality rates and 22% reduction in death from all causes. All event reductions were highly statistically significant. Other trials using statins in patients without signs of CHD have yielded similar risk reductions. Post hoc analysis of the results of the trials have produced diverging indications as to what is the optimal goal of cholesterol lowering. Analysis of the 4S indicates that aggressive treatment aiming at LDL-cholesterol levels lower than the current recommendations of expert panels in the United States and in Europe may yield additional benefit. This strategy finds some support in epidemiological studies and in a study with angiographic end points. Analysis of two trials using pravastatin contradict this and conclude that there is little or no additional benefit of reducing LDL-cholesterol below 125 mg/dL (3.2 mmol/L). Future studies need to address this question prospectively. (Am Heart J 1999;138:S177-S182.)