| 61. |
- Fahlén, Jessica, 1973-
(författare)
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Essays on spatial point processes and bioinformatics
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of two separate parts. The first part consists of one paper and considers problems concerning spatial point processes and the second part includes three papers in the field of bioinformatics. The first part of the thesis is based on a forestry problem of estimating the number of trees in a region by using the information in an aerial photo, showing the area covered by the trees. The positions of the trees are assumed to follow either a binomial point process or a hard-core Strauss process. Furthermore, discs of equal size are used to represent the tree-crowns. We provide formulas for the expectation and the variance of the relative vacancy for both processes. The formulas are approximate for the hard-core Strauss process. Simulations indicate that the approximations are accurate. The second part of this thesis focuses on pre-processing of microarray data. The microarray technology can be used to measure the expression of thousands of genes simultaneously in a single experiment. The technique is used to identify genes that are differentially expressed between two populations, e.g. diseased versus healthy individuals. This information can be used in several different ways, for example as diagnostic tools and in drug discovery. The microarray technique involves a number of complex experimental steps, where each step introduces variability in the data. Pre-processing aims to reduce this variation and is a crucial part of the data analysis. Paper II gives a review over several pre-processing methods. Spike-in data are used to describe how the different methods affect the sensitivity and bias of the experiment. An important step in pre-processing is dye-normalization. This normalization aims to remove the systematic differences due to the use of different dyes for coloring the samples. In Paper III a novel dye-normalization, the MC-normalization, is proposed. The idea behind this normalization is to let the channels’ individual intensities determine the correction, rather than the average intensity which is the case for the commonly used MA-normalization. Spike-in data showed that the MC-normalization reduced the bias for the differentially expressed genes compared to the MA-normalization. The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). In Paper IV we used tongue-cancer microRNA-microarray data to study the effect of FFPE-storage. We suggest that the microRNAs are not equally affected by the storage time and propose a novel procedure to remove this bias. The procedure improves the ability of the analysis to detect differentially expressed microRNAs.
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| 62. |
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| 63. |
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| 64. |
- Fowler, Philip, 1986-
(författare)
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Methods for improving covariate balance in observational studies
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment.The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias.Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects.The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.
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| 65. |
- Fowler, Philip, et al.
(författare)
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Study protocol for the evaluation of a vocational rehabilitation
- 2017
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Ingår i: Observational Studies. ; 3, s. 1-27
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a study protocol for the evaluation of a vocational rehabilitation, namely a collaboration between the Swedish Social Insurance Agency and the Public Employment Service, where individuals needing support to regain work ability were called to a joint assessment meeting. This protocol describes a matching study design using a lasso algorithm, where we do not have access to outcome data on work ability for the treated. The matching design is based on a collection of health and socio-economic covariates measured at baseline. We also have access to a prognosis made by caseworkers on the expected length of the individual sick leave. This prognosis variable is, we argue, a proxy variable for potential unmeasured confounders. We present results showing balance achieved on observed covariates.
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| 66. |
- Genbäck, Minna, 1985-, et al.
(författare)
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Bounds and sensitivity analysis when estimating average treatment effects with imputation and double robust estimators
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- When estimating average causal effects of treatments with observational data, scientists often rely on the assumption of unconfoundedness. We propose a sensitivity analysis for imputation estimators and doubly robust estimators, based on bounds (defining an identification interval) for the causal effect of interest, which allow for unobserved confounders. The bounds are derived from the bias of the estimators, expressed as a function of a sensitivity parameter. We describe how such bounds can take into account sampling variation, thereby yielding an uncertainty interval. We are also able to contrast the size of potential bias due to violation of the unconfoundedness assumption, to the misspecification of the models used to explain outcome with the observed covariates. While the latter bias can in principle be made arbitrarily small with increasing sample size (by increasing the flexibility of the models used), the bias due to unobserved confounding does not disappear with increasing sample size. Through numerical experiments we illustrate the relative size of the biases due to unobserved confounders and model misspecification, as well as the empirical coverage of the uncertainty interval on which the proposed sensitivity analysis is based.
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| 67. |
- Genbäck, Minna, 1985-, et al.
(författare)
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Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation
- 2019
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Ingår i: Biometrics. - : Wiley-Blackwell. - 0006-341X .- 1541-0420. ; 75:2, s. 506-515
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Tidskriftsartikel (refereegranskat)abstract
- Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due to unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by modeling the treatment assignment mechanism and its correlation with the outcome given the observed confounders, allowing us to derive the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R-package implementing the inference proposed is available.
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| 68. |
- Genbäck, Minna, 1985-, et al.
(författare)
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Predictors of decline in self-reported health : addressing non-ignorable dropout in longitudinal studies of ageing
- 2018
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Ingår i: European Journal of Ageing. - : Springer. - 1613-9372 .- 1613-9380. ; 15:2, s. 211-220
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Tidskriftsartikel (refereegranskat)abstract
- Predictors of decline in health in older populations have been investigated in multiple studies before. Most longitudinal studies of aging, however, assume that dropout at follow-up is ignorable (missing at random) given a set of observed characteristics at baseline. The objective of this study was to address non-ignorable dropout in investigating predictors of declining self-reported health (SRH) in older populations (50 years or older) in Sweden, the Netherlands, and Italy. We used the SHARE panel survey, and since only 2895 out of the original 5657 participants in the survey 2004 were followed up in 2013, we studied whether the results were sensitive to the expectation that those dropping out have a higher proportion of decliners in SRH. We found that older age and a greater number of chronic diseases were positively associated with a decline in self-reported health in the three countries studies here. Maximum grip strength was associated with decline in self-reported health in Sweden and Italy, and self-reported limitations in normal activities due to health problems were associated with decline in self-reported health in Sweden. These results were not sensitive to non-ignorable dropout. On the other hand, although obesity was associated with decline in a complete case analysis, this result was not confirmed when performing a sensitivity analysis to non-ignorable dropout. The findings, thereby, contribute to the literature in understanding the robustness of longitudinal study results to non-ignorable dropout while considering three different population samples in Europe.
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| 69. |
- Genbäck, Minna, 1985-
(författare)
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Uncertainty intervals and sensitivity analysis for missing data
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR.The thesis covers different types of linear regression. In Paper I and III we have a continuous outcome, in Paper II a binary outcome, and in Paper IV we allow for mixed effects with a continuous outcome. In Paper III we estimate the effect of a treatment, which can be seen as an example of missing outcome data.
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| 70. |
- Genbäck, Minna, et al.
(författare)
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Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome
- 2015
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Ingår i: Statistical papers. - : Springer-Verlag New York. - 0932-5026 .- 1613-9798. ; 56:3, s. 829-847
-
Tidskriftsartikel (refereegranskat)abstract
- When estimating regression models with missing outcomes, scientists usually have to rely either on a missing at random assumption (missing mechanism is independent from the outcome given the observed variables) or on exclusion restrictions (some of the covariates affecting the missingness mechanism do not affect the outcome). Both these hypotheses are controversial in applications since they are typically not testable from the data. The alternative, which we pursue here, is to derive identification sets (instead of point identification) for the parameters of interest when allowing for a missing not at random mechanism. The non-ignorability of this mechanism is quantified with a parameter. When the latter can be bounded with a priori information, a bounded identification set follows. Our approach allows the outcome to be continuous and unbounded and relax distributional assumptions. Estimation of the identification sets can be performed via ordinary least squares and sampling variability can be incorporated yielding uncertainty intervals achieving a coverage of at least (1-α) probability. Our work is motivated by a study on predictors of body mass index (BMI) change in middle age men allowing us to identify possible predictors of BMI change even when assuming little on the missing mechanism.
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