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Stephe Urgess - One of the best experts on this subject based on the ideXlab platform.

  • identifying the odds ratio Estimated by a two stage Instrumental Variable analysis with a logistic regression model
    Statistics in Medicine, 2013
    Co-Authors: Stephe Urgess
    Abstract:

    An adjustment for an uncorrelated covariate in a logistic regression changes the true value of an odds ratio for a unit increase in a risk factor. Even when there is no variation due to covariates, the odds ratio for a unit increase in a risk factor also depends on the distribution of the risk factor. We can use an Instrumental Variable to consistently Estimate a causal effect in the presence of arbitrary confounding. With a logistic outcome model, we show that the simple ratio or two-stage Instrumental Variable Estimate is consistent for the odds ratio of an increase in the population distribution of the risk factor equal to the change due to a unit increase in the instrument divided by the average change in the risk factor due to the increase in the instrument. This odds ratio is conditional within the strata of the Instrumental Variable, but marginal across all other covariates, and is averaged across the population distribution of the risk factor. Where the proportion of variance in the risk factor explained by the instrument is small, this is similar to the odds ratio from a RCT without adjustment for any covariates, where the intervention corresponds to the effect of a change in the population distribution of the risk factor. This implies that the ratio or two-stage Instrumental Variable method is not biased, as has been suggested, but Estimates a different quantity to the conditional odds ratio from an adjusted multiple regression, a quantity that has arguably more relevance to an epidemiologist or a policy maker, especially in the context of Mendelian randomization.

Simon G Thompson - One of the best experts on this subject based on the ideXlab platform.

  • bias due to participant overlap in two sample mendelian randomization
    Genetic Epidemiology, 2016
    Co-Authors: Stephen Burgess, Neil M Davies, Simon G Thompson
    Abstract:

    Mendelian randomization analyses are often performed using summarized data. The causal Estimate from a one-sample analysis (in which data are taken from a single data source) with weak Instrumental Variables is biased in the direction of the observational association between the risk factor and outcome, whereas the Estimate from a two-sample analysis (in which data on the risk factor and outcome are taken from non-overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case-control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one-sample Instrumental Variable Estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case-control setting, if risk factor measurements are only included for the control participants, unbiased Estimates are obtained even in a one-sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide Estimates that exclude case participants from case-control samples.

Pia R Kamstrup - One of the best experts on this subject based on the ideXlab platform.

  • lipoprotein a concentrations isoform size and risk of type 2 diabetes a mendelian randomisation study
    The Lancet Diabetes & Endocrinology, 2013
    Co-Authors: Pia R Kamstrup, Borge G Nordestgaard
    Abstract:

    Summary Background Low concentrations of lipoprotein(a) in plasma are associated with increased risk of type 2 diabetes, but whether this association is causal is unclear. Variations in the LPA gene affect lipoprotein(a) isoform size and concentrations in plasma. We therefore did a Mendelian randomisation study to investigate whether large isoform size, low concentrations in plasma, or both, are causally associated with type 2 diabetes. Methods We assessed data for adults from the Danish general population enrolled in the Copenhagen City Heart Study and the Copenhagen General Population Study, with and without type 2 diabetes. Eligible participants had data for lipoprotein(a) concentrations in plasma, LPA kringle IV type 2 (KIV-2) sums of repeats (affecting both isoform size and plasma concentrations), and carrier status for the LPA single-nucleotide polymorphism rs10455872 (mainly affecting concentrations in plasma). Findings 77 901 individuals had lipoprotein(a) data, of whom 28 567 (36·7%) had all three measurements. Low concentrations of lipoprotein(a) in plasma were associated with risk of type 2 diabetes, with adjusted odds ratios of 1·26 (1·09–1·45), 1·17 (1·01–1·36), 1·04 (0·90–1·21), and 1·05 (95% CI 0·90–1·22), respectively, for quintiles 1–4, compared with quintile 5 concentrations. High KIV-2 sums of repeats were associated with risk of type 2 diabetes (adjusted odds ratio 1·16, 95% CI 1·05–1·28) for KIV-2 quintile 5 versus quintiles 1–4 combined. Being a carrier of rs10455872 did not affect risk of type 2 diabetes. For a halving of lipoprotein(a) concentrations, the Instrumental Variable Estimate of the causal odds ratio for type 2 diabetes was 1·15 (95% CI 1·05–1·27) for KIV-2 sum of repeats and 0·99 (0·95–1·03) for rs10455872 genotype. Interpretation Low lipoprotein(a) concentrations alone seem not to be causally associated with type 2 diabetes, but a causal association for large lipoprotein(a) isoform size cannot be excluded. Funding Danish Heart Foundation, Danish Council for Independent Research–Medical Sciences, IMK Almene Fund, and Johan and Lise Boserup's Fund.

Stephen Burgess - One of the best experts on this subject based on the ideXlab platform.

  • bias due to participant overlap in two sample mendelian randomization
    Genetic Epidemiology, 2016
    Co-Authors: Stephen Burgess, Neil M Davies, Simon G Thompson
    Abstract:

    Mendelian randomization analyses are often performed using summarized data. The causal Estimate from a one-sample analysis (in which data are taken from a single data source) with weak Instrumental Variables is biased in the direction of the observational association between the risk factor and outcome, whereas the Estimate from a two-sample analysis (in which data on the risk factor and outcome are taken from non-overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case-control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one-sample Instrumental Variable Estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case-control setting, if risk factor measurements are only included for the control participants, unbiased Estimates are obtained even in a one-sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide Estimates that exclude case participants from case-control samples.

Borge G Nordestgaard - One of the best experts on this subject based on the ideXlab platform.

  • lipoprotein a concentrations isoform size and risk of type 2 diabetes a mendelian randomisation study
    The Lancet Diabetes & Endocrinology, 2013
    Co-Authors: Pia R Kamstrup, Borge G Nordestgaard
    Abstract:

    Summary Background Low concentrations of lipoprotein(a) in plasma are associated with increased risk of type 2 diabetes, but whether this association is causal is unclear. Variations in the LPA gene affect lipoprotein(a) isoform size and concentrations in plasma. We therefore did a Mendelian randomisation study to investigate whether large isoform size, low concentrations in plasma, or both, are causally associated with type 2 diabetes. Methods We assessed data for adults from the Danish general population enrolled in the Copenhagen City Heart Study and the Copenhagen General Population Study, with and without type 2 diabetes. Eligible participants had data for lipoprotein(a) concentrations in plasma, LPA kringle IV type 2 (KIV-2) sums of repeats (affecting both isoform size and plasma concentrations), and carrier status for the LPA single-nucleotide polymorphism rs10455872 (mainly affecting concentrations in plasma). Findings 77 901 individuals had lipoprotein(a) data, of whom 28 567 (36·7%) had all three measurements. Low concentrations of lipoprotein(a) in plasma were associated with risk of type 2 diabetes, with adjusted odds ratios of 1·26 (1·09–1·45), 1·17 (1·01–1·36), 1·04 (0·90–1·21), and 1·05 (95% CI 0·90–1·22), respectively, for quintiles 1–4, compared with quintile 5 concentrations. High KIV-2 sums of repeats were associated with risk of type 2 diabetes (adjusted odds ratio 1·16, 95% CI 1·05–1·28) for KIV-2 quintile 5 versus quintiles 1–4 combined. Being a carrier of rs10455872 did not affect risk of type 2 diabetes. For a halving of lipoprotein(a) concentrations, the Instrumental Variable Estimate of the causal odds ratio for type 2 diabetes was 1·15 (95% CI 1·05–1·27) for KIV-2 sum of repeats and 0·99 (0·95–1·03) for rs10455872 genotype. Interpretation Low lipoprotein(a) concentrations alone seem not to be causally associated with type 2 diabetes, but a causal association for large lipoprotein(a) isoform size cannot be excluded. Funding Danish Heart Foundation, Danish Council for Independent Research–Medical Sciences, IMK Almene Fund, and Johan and Lise Boserup's Fund.