Significados de inverse variance en inglés

SUA effects were pooled by random-effects models and weighted by inverse variance.

An inverse variance method was used to pool data into a random-effects model.

BP effects were pooled by random-effects models, with trials weighted by inverse variance.

Pooled HRs were computed and weighted using generic inverse variance.

Quantitative data synthesis was done using the generic inverse variance method and the fixed-effects model.

We combined these estimates with those from the published literature using inverse variance effects models.

A meta-analysis of the results was performed using the weighted inverse variance method in METAL.

Generic inverse variance with fixed effects models were used to determine the pooled HR for each outcome.

Hazard ratios for DFS were weighted and pooled using the generic inverse variance and random effects model.

The generic inverse variance method was used to compute the combined hazard ratio (HR) of long-term mortality.

RR for mortality (RA-UIP vs. other RA-ILD) was pooled using inverse variance weighting and random effects models.

Results were combined with inverse variance meta-analysis, and replication of top loci in European and Japanese samples was performed.

Meta-analysis was carried out using the inverse variance-weighted method or the DerSimonian-Laird method after estimating the heterogeneity among the studies.

Where appropriate, we performed meta-analysis using the Mantel-Haenszel method for smoking and inverse variance for weight using a fixed-effect model.

Effect estimates from each study were extracted and combined using the random-effect, generic inverse variance method of DerSimonian and Laird.

Risk estimates were pooled using the generic inverse variance method and expressed as risk ratios (RRs) with 95% CIs.