The DOI matches its Crossref record; 82 of the 99 references it cites were found in a registry or an index.
Review of statistical methods for analysing healthcare resources and costs
Open accessAbstract
We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“exponential weighting has previously been used in both medical42–44 and non-medical45 46 error/risk analysis.”
From Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice (2025) · cites this paper as [43]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression Analysis of Count Data: Author Index
Cameron, A., & Trivedi, P. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511814365 · Cited by 6,406
Hilbe, J. M. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511973420 · Cited by 3,352
Smearing Estimate: A Nonparametric Retransformation Method
Duan, N.-H. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10478017 · Cited by 2,289 · cited 4×
“The four-part model in Duan (1983) (see Section 3.1.6) is also in essence a data components model.”
BackgroundMethodInfluentialEconometric models based on count data. Comparisons and applications of some estimators and tests
Cameron, A., & Trivedi, P. (1986) · Journal of Applied Econometrics · doi:10.1002/jae.3950010104 · Cited by 1,991 · cited 1×
“Extended approaches based on the Poisson distribution by Cameron and colleagues (Cameron and Johansson, 1997; Cameron and Trivedi, 1986) (negative binomial and variance linear in the mean, Poisson polynomial model) and Grootendorst (1995) (zero-inflated Poisson and negative binomial) are used to…”
MethodEstimating log models: to transform or not to transform?
Manning, W. G., & Mullahy, J. (2001) · Journal of Health Economics · doi:10.1016/s0167-6296(01)00086-8 · Cited by 1,790 · cited 14×
“tion of the variance function could lead to inefficiencies, the mean function estimates are usually robust (Manning and Mullahy, 2001).”
InfluentialA Comparison of Alternative Models for the Demand for Medical Care
Duan, N.-H., Manning, W., Morris, C., & Newhouse, J. (1983) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1983.10509330 · Cited by 1,410 · cited 13×
“mishandling of both non-users and significant users of health services) (Duan et al., 1983).”
BackgroundMethodInfluentialThe logged dependent variable, heteroscedasticity, and the retransformation problem.
Manning, W. (1998) · Journal of Health Economics · doi:10.1016/s0167-6296(98)00025-3 · Cited by 959
Buntin, M., & Zaslavsky, A. (2004) · Journal of Health Economics · doi:10.1016/j.jhealeco.2003.10.005 · Cited by 792 · cited 5×
“…reviewed included an informal comparison of different methods and approaches applied to the same data (Austin et al., 2003; Briggs and Gray, 1998; Buntin and Zaslavsky, 2004; Chen and Zhou, 2006; Cooper et al., 2007; Lipscomb et al., 1998; Manning and Mullahy, 2001; O’Hagan and Stevens, 2003;…”
BackgroundMethodInfluentialMuch ado about two: reconsidering retransformation and the two-part model in health econometrics.
Mullahy, J. (1998) · Journal of Health Economics · doi:10.1016/s0167-6296(98)00030-7 · Cited by 791 · cited 2×
“In this regard, Mullahy (1998) proposes two estimators using exponential conditional mean specifications.”
MethodHow should cost data in pragmatic randomised trials be analysed?
Thompson, S. G. (2000) · BMJ · doi:10.1136/bmj.320.7243.1197 · Cited by 772 · cited 2×
“Two common approaches to directly evaluate uncertainty in costs, effects and/or cost-effectiveness in randomised trials are the Central Limit Theorem and the bootstrap approach (Barber and Thompson, 2000; O’Hagan and Stevens, 2003; Thompson and Barber, 2000).”
BackgroundMethodSelf-citationGeneralized modeling approaches to risk adjustment of skewed outcomes data
Manning, W. G., Basu, A., & Mullahy, J. (2005) · Journal of Health Economics · doi:10.1016/j.jhealeco.2004.09.011 · Cited by 760
Econometric Analysis of Count Data
Friedl, H. (2002) · doi:10.1198/jasa.2002.s459 · Cited by 760 · cited 1×
“General methods for count data have been extensively described elsewhere (Cameron and Trivedi, 1998; Winkelmann, 2008).”
MethodAn Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care
Pohlmeier, W., & Ulrich, V. (1995) · The Journal of Human Resources · doi:10.2307/146123 · Cited by 581 · cited 2×
“, 1983; Leung and Yu, 1996), GLM or OLS models have been employed in the second part to evaluate mean costs, and truncated-at-zero Poisson, negative binomial (Grootendorst, 1995; Pohlmeier and Ulrich, 1995), or truncated Poisson-lognormal models (Winkelmann, 2004) to evaluate resource use (for a wider range of hurdle…”
MethodOn the choice between sample selection and two-part models
Leung, S., & Yu, S. (1996) · Journal of Econometrics · doi:10.1016/0304-4076(94)01720-4 · Cited by 528 · cited 3×
“Log-linear (Duan et al., 1983; Leung and Yu, 1996), GLM or OLS models have been employed in the second part to evaluate mean costs, and truncated-at-zero Poisson, negative binomial (Grootendorst, 1995; Pohlmeier and Ulrich, 1995), or truncated Poisson-lognormal models (Winkelmann, 2004) to evaluate…”
BackgroundMethodModeling risk using generalized linear models.
Blough, D., Madden, C., & Hornbrook, M. (1999) · Journal of Health Economics · doi:10.1016/s0167-6296(98)00032-0 · Cited by 473 · cited 5×
“…generalized linear models (GLMs) a mean function (between the linear predictor and the mean) and a variance function (between the mean and variance on the original scale) are specified and the parameters are estimated given these structural assumptions (see general theory in Blough et al. (1999)).”
BackgroundInfluentialRobust Inference for Generalized Linear Models
Cantoni, E., & Ronchetti, E. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753209004 · Cited by 404 · cited 1×
“Furthermore in the case of unknown separation (latent mixture models) and more than two components the identification of all components might be difficult because of the increasing overlap of distributions (Cameron and Trivedi, 2009).”
BackgroundMonte Carlo evidence on the choice between sample selection and two-part models
Manning, W., Duan, N.-H., & Rogers, W. (1987) · Journal of Econometrics · doi:10.1016/0304-4076(87)90081-9 · Cited by 403 · cited 2×
“The benefits of this proposition have not been observed in the case of the RAND HIE and are unlikely to hold generally in randomised studies when the interest is in the estimated mean (Duan et al., 1984; Maddala, 1985; Manning et al., 1987).”
BackgroundMethodThe structure of demand for health care: latent class versus two-part models.
Deb, P., & Trivedi, P. (2002) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00008-5 · Cited by 380 · cited 1×
“These include approaches based on mixtures of distributions, non-parametric density approximation, data components, model averaging and Markov chain methods.”
MethodBasu, A., & Rathouz, P. (2005) · Biostatistics · doi:10.1093/biostatistics/kxh020 · Cited by 357 · cited 1×
“…al., 1999), the estimation for the link and variance and the incorporation of the uncertainty from this estimation (including underlying heteroscedasticity) in the final parameter estimates is rarely done; for an exception see the Extended Estimating Equations approach in Basu and Rathouz (2005).”
BackgroundChoosing Between the Sample-Selection Model and the Multi-Part Model
Duan, N.-H., Manning, W., Morris, C., & Newhouse, J. (1984) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1984.10509396 · Cited by 350 · cited 1×
“The benefits of this proposition have not been observed in the case of the RAND HIE and are unlikely to hold generally in randomised studies when the interest is in the estimated mean (Duan et al., 1984; Maddala, 1985; Manning et al., 1987).”
MethodAnalysis of repeated measures data with clumping at zero
Tooze, J., Grunwald, G., & Jones, R. H. (2002) · Statistical Methods in Medical Research · doi:10.1191/0962280202sm291ra · Cited by 315 · cited 1×
“An extended two-part model, which explicitly models and estimates the correlation between the logistic and lognormal part for repeated measures data, is suggested by Tooze (Tooze et al., 2002) and is shown to outperform the model with uncorrelated random effects.”
MethodThe Distribution of Health Care Costs and Their Statistical Analysis for Economic Evaluation
Briggs, A., & Gray, A. (1998) · Journal of Health Services Research and Policy · doi:10.1177/135581969800300410 · Cited by 288 · cited 4×
“20: 897–916 (2011) DOI: 10.1002/hec (Austin et al., 2003; Basu, 2005; Briggs et al., 2005; Briggs and Gray, 1998; Chen and Zhou, 2006; Deb and Burgess, 2003; Dinh and Zhou, 2006; Duan et al., 1983; Dudley et al., 1993; Gilleskie and Mroz, 2004; O’Hagan and Stevens, 2003; Thompson and Barber, 2000;…”
BackgroundMethodInfluentialSelf-citationUsing Generalized Linear Models to Assess Medical Care Costs
Blough, D., & Ramsey, S. (2000) · Health Services & Outcomes Research Methodology · doi:10.1023/a:1012597123667 · Cited by 225 · cited 2×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
BackgroundMethodHealth Care Reform and the Number of Doctor Visits - an Econometric Analysis
Winkelmann, R. (2004) · Social Science Research Network · doi:10.1002/jae.764 · Cited by 203 · cited 3×
“…employed in the second part to evaluate mean costs, and truncated-at-zero Poisson, negative binomial (Grootendorst, 1995; Pohlmeier and Ulrich, 1995), or truncated Poisson-lognormal models (Winkelmann, 2004) to evaluate resource use (for a wider range of hurdle models please refer to Hilbe (2007)).”
BackgroundMethodNixon, R., Wonderling, D., & Grieve, R. (2010) · Health Economics · doi:10.1002/hec.1477 · Cited by 194
Comparing alternative models: log vs Cox proportional hazard?
Basu, A., Manning, W., & Mullahy, J. (2004) · Health Economics · doi:10.1002/hec.852 · Cited by 183 · cited 5×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
BackgroundMethodInfluentialThe demand for episodes of mental health services.
Keeler, E., Manning, W., & Wells, K. B. (1988) · Journal of Health Economics · doi:10.1016/0167-6296(88)90021-5 · Cited by 174 · cited 1×
“A Weibull survival model was suggested for the hazard of initiating mental health service use as duration on treatment (time from initiating treatment to end of year) was indicative of cost (Keeler et al., 1988).”
MethodFitting the distributions of length of stay by parametric models.
Marazzi, A., Paccaud, F., Ruffieux, C., & Beguin, C. (1998) · Medical Care · doi:10.1097/00005650-199806000-00014 · Cited by 161 · cited 2×
“Two and three-parameter lognormal and log-logistic distributions are used by Nixon and Thompson (Nixon and Thompson, 2004; Thompson and Nixon, 2005) to model costs, and lognormal and Weibull models are used by Marazzi et al. (1998) to model length of stay in hospital.”
MethodFaddy, M., Graves, N., & Pettitt, A. (2009) · Value in Health · doi:10.1111/j.1524-4733.2008.00421.x · Cited by 152 · cited 1×
“An implementation of the approach in evaluating mean length of stay shows better in-sample fit of a phase-type distribution compared with lognormal and gamma models (Faddy et al., 2009).”
MethodLatent class versus two-part models in the demand for physician services across the European Union.
Jimenez-Martin, S., Labeaga, J., & Martínez-Granado, M. (2002) · Health Economics · doi:10.1002/hec.672 · Cited by 150 · cited 2×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
MethodCameron, A., Li, T., Trivedi, P., & Zimmer, D. M. (2004) · doi:10.1111/j.1368-423x.2004.00144.x · Cited by 145 · cited 1×
“…include: a bivariate Poisson distribution (Cameron and Johansson, 1998), negative binomial marginal distribution and copula functions (Cameron et al., 2004), independent Poisson distributions with conditional mean functions that depend on correlated latent effects (Chib and…”
BackgroundIssues for the Next Generation of Health Care Cost Analyses
Basu, A., & Manning, W. (2009) · Medical Care · doi:10.1097/mlr.0b013e31819c94a1 · Cited by 144 · cited 1×
“Recently, Basu and Manning (2009) concluded, ‘No current method is optimal or dominant for all cost applications.”
BackgroundA comparison of several regression models for analysing cost of CABG surgery
Austin, P. C., Ghali, W. A., & Tu, J. V. (2003) · Statistics in Medicine · doi:10.1002/sim.1442 · Cited by 142 · cited 7×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
BackgroundMethodInfluentialMunkin, M. K., & Trivedi, P. (1999) · doi:10.1111/1368-423x.00019 · Cited by 136
Deb, P., & Holmes, A. (2000) · Health Economics · doi:10.1002/1099-1050(200009)9:6 · Cited by 128
Standard errors for the retransformation problem with heteroscedasticity.
Ai, C.-R., & Norton, E. C. (2000) · Journal of Health Economics · doi:10.1016/s0167-6296(00)00046-1 · Cited by 120 · cited 3×
“Thus, the approaches for back transformation are dominated by Duan’s non-parametric smearing when the error term is homoskedastic (Duan, 1983) and its variants when the error term is heteroskedastic across the compared groups (Ai and Norton, 2000; Duan et al., 1983; Manning, 1998).”
MethodO'Hagan, A., & Stevens, J. W. (2003) · Health Economics · doi:10.1002/hec.699 · Cited by 119 · cited 2×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
MethodSelf-citationDudley, R., Harrell, F. E. J., Smith, L., Mark, D., Califf, R., Pryor, D., Glower, D., Lipscomb, J., & Hlatky, M. (1993) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(93)90074-b · Cited by 112 · cited 8×
“such as the semiparametric Cox proportional hazards model (Austin et al., 2003; Basu et al., 2004; Dudley et al., 1993; Lipscomb et al., 1998) and the parametric Weibull proportional hazards model (Dudley et al.”
BackgroundMethodInfluentialA comparison of alternative models of prescription drug utilization.
Grootendorst, P. V. (1995) · Health Economics · doi:10.1002/hec.4730040304 · Cited by 109
Parametric modelling of cost data in medical studies
Nixon, R., & Thompson, S. (2004) · Statistics in Medicine · doi:10.1002/sim.1744 · Cited by 108 · cited 1×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
MethodSelf-citationParametric modelling of cost data: some simulation evidence.
Briggs, A., Nixon, R., Dixon, S., & Thompson, S. (2005) · Health Economics · doi:10.1002/hec.941 · Cited by 102
Self-citationThe Retransformed Mean after a Fitted Power Transformation
Taylor, J. M. G. (1986) · doi:10.1080/01621459.1986.10478246 · Cited by 100
Sharp, S. J., & Thompson, S. G. (2000) · Statistics in Medicine · doi:10.1002/1097-0258(20001215)19:23 · Cited by 99
Self-citationTwo-part multiple spell models for health care demand
Silva, J. S. S., & Windmeijer, F. (2001) · doi:10.1920/wp.ifs.1999.9902 · Cited by 97 · cited 1×
“Santos-Silva and Windmeijer suggest more appropriate modelling of spells of care and number of visits per spell through a Poisson model for the first part (number of spells) and a logarithmic model for the second part (number of visits per spell) (Santos-Silva and Windmeijer, 2001).”
BackgroundA robust approach for skewed and heavy-tailed outcomes in the analysis of health care expenditures.
Cantoni, E., & Ronchetti, E. (2006) · Journal of Health Economics · doi:10.1016/j.jhealeco.2005.04.010 · Cited by 93 · cited 1×
“…al., 2005), more complex specification of the mean–variance relationships (Basu, 2005; Basu et al., 2006; Blough et al., 1999) and GLM estimators that are more robust to outliers (and potentially more suitable for very heavy-tailed data) (Cantoni and Ronchetti, 2001; Cantoni and Ronchetti, 2006).”
BackgroundHow Sensitive Are Cost-Effectiveness Analyses to Choice of Parametric Distributions?
Thompson, S., & Nixon, R. (2005) · Medical decision making · doi:10.1177/0272989x05276862 · Cited by 92
Self-citationGeneralized hurdle count data regression models
Gurmu, S. (1998) · doi:10.1016/s0165-1765(97)00295-4 · Cited by 89 · cited 1×
“A hurdle model with a generalised logistic model in the first part is proposed by Gurmu (1998) and a modified second part in the two-part model is suggested by Mullahy (1998).”
MethodBasu, A., Arondekar, B., & Rathouz, P. (2006) · Health Economics · doi:10.1002/hec.1099 · Cited by 83 · cited 5×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
BackgroundMethodInfluentialSequence Effects, Health Profiles, and the QALY Model
Krabbe, P., & Bonsel, G. (1998) · Medical decision making · doi:10.1177/0272989x9801800207 · Cited by 80
Let Them Eat Cake: A Note on Comparing Alternative Models of the Demand for Medical Care
Hay, J., & Olsen, R. J. (1984) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1984.10509395 · Cited by 80 · cited 1×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
MethodHill, S. C., & Miller, G. (2009) · Health Economics · doi:10.1002/hec.1498 · Cited by 75 · cited 1×
“In circumstances where few of these issues exist then a sample size in the hundreds may be large enough, but where these issues are all present then a sample size of thousands may nevertheless be too small.”
BackgroundNonparametric confidence intervals for the one- and two-sample problems.
Zhou, X.-H., & Dinh, P. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi002 · Cited by 73 · cited 3×
“…to account for skewness and have been shown to provide more efficient estimates but these modifications are subject to a degree of subjectivity in the choice of transformation and do not generally allow for adjustment for covariates (Chen and Zhou, 2006; Dinh and Zhou, 2006; Zhou and Dinh, 2005).”
BackgroundMethodA bivariate zero-inflated negative binomial regression model for count data with excess zeros
Wang, P.-M. (2003) · doi:10.1016/s0165-1765(02)00262-8 · Cited by 69 · cited 2×
“…2001), a multivariate overdispersed Poisson mixture model (Gurmu and Elder, 2000), bivariate Poisson-lognormal mixture and bivariate negative binomial regression models (Munkin and Trivedi, 1999), and a bivariate zero-inflated binomial regression for count data with excess zeros (Wang, 2003).”
MethodGeneralized bivariate count data regression models
Gurmu, S., & Elder, J. (2000) · doi:10.1016/s0165-1765(00)00225-1 · Cited by 68 · cited 2×
“, 2004), independent Poisson distributions with conditional mean functions that depend on correlated latent effects (Chib and Winkelmann, 2001), a multivariate overdispersed Poisson mixture model (Gurmu and Elder, 2000), bivariate Poisson-lognormal mixture and bivariate negative binomial regression models (Munkin and…”
MethodBasu, A. (2005) · doi:10.1177/1536867x1501500204 · Cited by 64 · cited 4×
“20: 897–916 (2011) DOI: 10.1002/hec (Austin et al., 2003; Basu, 2005; Briggs et al., 2005; Briggs and Gray, 1998; Chen and Zhou, 2006; Deb and Burgess, 2003; Dinh and Zhou, 2006; Duan et al., 1983; Dudley et al., 1993; Gilleskie and Mroz, 2004; O’Hagan and Stevens, 2003; Thompson and Barber, 2000;…”
BackgroundMethodInfluentialAn application of mixture distributions in modelization of length of hospital stay
Atienza, N., García-Heras, J., Muñoz-Pichardo, J., & Villa, R. (2008) · Statistics in Medicine · doi:10.1002/sim.3029 · Cited by 62 · cited 2×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
BackgroundMethodInterval estimates for the ratio and difference of two lognormal means
Chen, Y.-H., & Zhou, X.-H. (2006) · Statistics in Medicine · doi:10.1002/sim.2504 · Cited by 54 · cited 4×
“…included an informal comparison of different methods and approaches applied to the same data (Austin et al., 2003; Briggs and Gray, 1998; Buntin and Zaslavsky, 2004; Chen and Zhou, 2006; Cooper et al., 2007; Lipscomb et al., 1998; Manning and Mullahy, 2001; O’Hagan and Stevens, 2003; Zhou, 2002).”
BackgroundMethodInfluentialThe truncated mean of an asymmetric distribution
Marazzi, A., & Ruffieux, C. (1999) · doi:10.1016/s0167-9473(99)00018-3 · Cited by 54 · cited 2×
“Two and three-parameter lognormal and log-logistic distributions are used by Nixon and Thompson (Nixon and Thompson, 2004; Thompson and Nixon, 2005) to model costs, and lognormal and Weibull models are used by Marazzi et al. (1998) to model length of stay in hospital.”
BackgroundCooper, N. J., Lambert, P. C., Abrams, K. R., & Sutton, A. J. (2006) · Health Economics · doi:10.1002/hec.1141 · Cited by 52 · cited 1×
“…included an informal comparison of different methods and approaches applied to the same data (Austin et al., 2003; Briggs and Gray, 1998; Buntin and Zaslavsky, 2004; Chen and Zhou, 2006; Cooper et al., 2007; Lipscomb et al., 1998; Manning and Mullahy, 2001; O’Hagan and Stevens, 2003; Zhou, 2002).”
MethodAdaptively truncated maximum likelihood regression with asymmetric errors
Marazzi, A., & Yohai, V. (2004) · doi:10.1016/j.jspi.2003.06.011 · Cited by 52
Lu, Z.-D., Hui, Y. V., & Lee, A. H. (2003) · Biometrics · doi:10.1111/j.0006-341x.2003.00117.x · Cited by 45 · cited 1×
“More robust estimation procedures via alternatives to maximum likelihood have been recently suggested (Lu et al., 2003).”
MethodA flexible approach for estimating the effects of covariates on health expenditures.
Gilleskie, D. B., & Mroz, T. (2000) · Journal of Health Economics · doi:10.3386/w7942 · Cited by 44 · cited 1×
“…and kurtosis in data; (2) incorporate adjustment for covariates and extend to cost-effectiveness analysis and (3) be applicable to data sets of small to moderate sample sizes usually available in randomised studies, where these issues were not explicitly considered by the authors themselves.”
MethodImproving Risk Adjustment for Medicare Capitated Reimbursement Using Nonlinear Models
Veazie, P., Manning, W., & Kane, R. (2003) · Medical Care · doi:10.1097/01.mlr.0000065127.88685.7d · Cited by 41 · cited 2×
“For these methods the individual healthcare resource use or cost data is transformed in order to overcome problems of skewness and comparison of means on the transformed scale (Briggs and Gray, 1998) or OLS type approaches (as above) to model the transformed dependent variable are employed (Ai and Norton, 2000; Duan e…”
MethodUse of Bayesian Markov Chain Monte Carlo Methods to Model Cost-of-Illness Data
Cooper, N., Sutton, A., Mugford, M., & Abrams, K. (2003) · Medical decision making · doi:10.1177/0272989x02239653 · Cited by 35 · cited 1×
“A Bayesian implementation of a two-part model in WinBUGS is illustrated by Cooper et al. (2003).”
MethodEstimating the costs for a group of geriatric patients using the Coxian phase‐type distribution
Marshall, A., Shaw, B., & McClean, S. (2004) · Statistics in Medicine · doi:10.1002/sim.2728 · Cited by 33 · cited 1×
“An approach based on a finite Markov chain is suggested to estimate resource use over different phases of health care (Coxian phase-type distribution) and evaluate total cost by attaching unit costs to these phases (Marshall et al., 2007).”
MethodNonparametric Statistical Methods for Cost‐Effectiveness Analyses
Dinh, P., & Zhou, X.-H. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00509.x · Cited by 30 · cited 1×
“The four-part model in Duan (1983) (see Section 3.”
BackgroundInferences about population means of health care costs
Zhou, X. (2002) · Statistical Methods in Medical Research · doi:10.1191/0962280202sm290ra · Cited by 29 · cited 1×
“General methods for count data have been extensively described elsewhere (Cameron and Trivedi, 1998; Winkelmann, 2008).”
MethodCOMPUTING MARGINAL EFFECTS IN THE BOX–COX MODEL
Abrevaya, J. (2002) · Econometric Reviews · doi:10.1081/etc-120015789 · Cited by 27 · cited 2×
“Alternative approaches of direct numerical integration over fully parameterized error distributions are proposed by Abrevaya (2002) and Ai and Norton (2000), while Ai and Norton (2008) propose a more general semi-parametric approach.”
MethodLambert, P., Billingham, L., Cooper, N., Sutton, A., & Abrams, K. (2008) · Health Economics · doi:10.1002/hec.1243 · Cited by 25 · cited 6×
“Lambert et al., also illustrate the possibility to increase the flexibility using a two-part model for cost components with excess zeros (Lambert et al., 2008).”
BackgroundMethodInfluentialSmooth quantile ratio estimation
Dominici, F., Cope, L., Naiman, D., & Zeger, S. (2005) · doi:10.1093/biomet/92.3.543 · Cited by 21 · cited 2×
“Regression of the quantiles of costs (Wang and Zhou, 2010) has been shown to have competitive and more robust performance compared to the smooth quantile ratio estimator (Dominici et al., 2005), maximum likelihood estimator, generalised linear models based on quasilikelihood (Blough and Ramsey, 2000), and the internal…”
A Bayesian model averaging approach for cost-effectiveness analyses.
Conigliani, C., & Tancredi, A. (2009) · Health Economics · doi:10.1002/hec.1404 · Cited by 20 · cited 3×
“Conigliani and Tancredi (2006; 2009) showed that the performance of Bayesian model averaging depends on what models are averaged over and whether there was a model included that fits the data well; otherwise an approach based on mixtures of distributions seemed more appropriate in terms of coverage.”
BackgroundA Semiparametric Derivative Estimator in Log Transformation Models
Ai, C.-R., & Norton, E. C. (2008) · doi:10.1111/j.1368-423x.2008.00252.x · Cited by 20
Dominici, F., & Zeger, S. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi031 · Cited by 19
Estimation of the retransformed conditional mean in health care cost studies
Wang, H., & Zhou, X. (2010) · doi:10.1093/biomet/asp072 · Cited by 16 · cited 2×
“Regression of the quantiles of costs (Wang and Zhou, 2010) has been shown to have competitive and more robust performance compared to the smooth quantile ratio estimator (Dominici et al., 2005), maximum likelihood estimator, generalised linear models based on quasilikelihood (Blough and Ramsey,…”
BackgroundMethodAn illustration of the modelling of cost and efficacy data from a clinical trial
Hahn, S., & Whitehead, A. (2003) · Statistics in Medicine · doi:10.1002/sim.1459 · Cited by 16 · cited 2×
“The applications published to date refer to components of costs or resource use modelled as bivariate or multivariate normally or lognormally distributed data (Hahn and Whitehead, 2003; Lambert et al., 2008).”
BackgroundPairwise comparisons of the means of skewed data
Tu, W., & Zhou, X.-H. (2000) · doi:10.1016/s0378-3758(99)00206-2 · Cited by 16
Semi‐parametric modelling for costs of health care technologies
Conigliani, C., & Tancredi, A. (2005) · Statistics in Medicine · doi:10.1002/sim.2012 · Cited by 13 · cited 3×
“A second is models which allow for potentially heavy upper tails, while allowing flexible (or even non-parametric) distributions for the bulk of the data (Conigliani and Tancredi, 2005).”
BackgroundMethodBootstrap tests for robust means of asymmetric distributions with unequal shapes
Marazzi, A. (2002) · doi:10.1016/s0167-9473(01)00088-3 · Cited by 8 · cited 2×
“Two and three-parameter lognormal and log-logistic distributions are used by Nixon and Thompson (Nixon and Thompson, 2004; Thompson and Nixon, 2005) to model costs, and lognormal and Weibull models are used by Marazzi et al. (1998) to model length of stay in hospital.”
BackgroundECONOMETRIC MODELS OF EVENT COUNTS
Trivedi, P. K. (1997) · Journal of Applied Econometrics · doi:10.1002/(sici)1099-1255(199705)12:3 · Cited by 7
Survival models for cost data: The forgotten additive approach
Pagano, E., Petrinco, M., Desideri, A., Bigi, R., Merletti, F., & Gregori, D. (2008) · Statistics in Medicine · doi:10.1002/sim.3215 · Cited by 7 · cited 1×
“Another survival type approach suggested in the literature is the Aalen regression model with additive hazard function (Pagano et al., 2008).”
A test for proportional hazards assumption within the class of exponential conditional mean models
Basu, A., & Manning, W. (2006) · Health Services & Outcomes Research Methodology · doi:10.1007/s10742-006-0002-3 · Cited by 4 · cited 2×
“This model shows good performance when the proportional hazards assumption holds; a test for proportional hazards is suggested by Basu and Manning (2006).”
BackgroundMethodHollenbeak, C. (2005) · Statistics in Medicine · doi:10.1002/sim.2172 · Cited by 3
Flagged first, then by citations
17 of the 99 references couldn’t be matched to a known paper.
Research works printed but not linked: 20
- Arrow KJ, 1970, Uncertainty and the evaluation of public investment decisions, The American Economic Review, 60, 364
- Basu A, 2005, Extended generalised linear models: simultaneous estimation of flexible link and variance functions, The Stata Journal, 5, 501, 10.1177/1536867X0500500402 · DOI
- Cameron AC, 2009, Microeconometrics Using Stata
- 10.1198/07350010152596673 · DOI
- 10.1002/(SICI)1099-1255(199705)12:3<313::AID-JAE440>3.0.CO;2-G · DOI
- 10.2307/1391852 · DOI
- 10.2307/1391711 · DOI
- 10.1016/j.jhealeco.2003.09.008 · DOI
- 10.1002/(SICI)1099-1255(199705)12:3<225::AID-JAE445>3.0.CO;2-Y · DOI
- 10.2307/1391710 · DOI
- 10.1017/CBO9780511811852 · DOI
- Jones AM, 2000, North‐Holland Handbook of Health Economics
- Maddala GS, 1985, Advances in Health Economics and Health Services Research
- Marazzi A, 2003, Robust parametric means of asymmetric distributions: estimation and testing, Estadistica, 54, 47
- 10.1111/1475-3995.00428 · DOI
- 10.1002/(SICI)1099-1255(199705)12:3<337::AID-JAE438>3.0.CO;2-G · DOI
- 10.1016/S0304-4076(01)00059-8 · DOI
- van de Ven WPMM, 1981, Health, Economics, and Health Economics
- 10.1016/j.jspi.2004.07.009 · DOI
- 10.1016/j.csda.2004.12.001 · DOI
Cited by
Sorted by citations
Antibiotic resistance threats in the United States, 2019
(U.S.), C. F. D. C. A. P. (2019) · doi:10.15620/cdc:82532 · Cited by 5,782
Belotti, F., Deb, P., Manning, W., & Norton, E. (2015) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x1501500102 · Cited by 588
Health-related quality of life measured using the EQ-5D–5L: South Australian population norms
McCaffrey, N., Kaambwa, B., Currow, D. C., & Ratcliffe, J. (2016) · Health and Quality of Life Outcomes · doi:10.1186/s12955-016-0537-0 · Cited by 478
“This model controls for skewness and heteroscedasticity and approximates the distribution of the data based on the modified Park test [53, 54].”
MethodAutomated Identification of Adults at Risk for In-Hospital Clinical Deterioration
Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020) · New England Journal of Medicine · doi:10.1056/nejmsa2001090 · Cited by 314
Economic evaluation alongside randomised controlled trials: design, conduct, analysis, and reporting
Petrou, S., & Gray, A. (2011) · British medical journal · doi:10.1136/bmj.d1548 · Cited by 313
“Although cost data are often right skewed because a few patients use very high amounts of resource, producing a distribution that may violate the assumptions of standard statistical tests, it is not clear that the many alternative approaches suggested, such as bootstrapping, provide more reliable test results.”
Background
Metadata
- Authors
- Mihaylova, B., Briggs, A., O'Hagan, A., & Thompson, S. G.
- Affiliations
- University of Oxford (GB)
- University of Glasgow (GB)
- Department of Probability and Statistics, University of Sheffield, Sheffield, UK
- MRC Biostatistics Unit (GB)
- Corresponding author
- Mihaylova, Borislava
- Venue
- Health Economics
- Publisher
- Wiley
- Published
- 2010-08-27
- Volume
- 20
- Issue
- 8
- Pages
- 897-916
- ISSN
- 1057-9230 (ISSN-L)
- 1099-1050
- PMC
- PMC3470917
- OpenAlex
- W2041616439
- Cited by
- 767
- References
- 99
- Citations by year
- 2026: 35 · 2025: 47 · 2024: 51 · 2023: 48 · 2022: 79 · 2021: 85 · 2020: 77 · 2019: 67 · 2018: 58 · 2017: 47 · 2016: 41 · 2015: 39 · 2014: 30 · 2013: 26 · 2012: 23
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Health Economics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Funders
- British Heart Foundation (RG/08/014/24067)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- health care utilization, health care costs, skewed data, excess zeros, multimodality, generalized linear models, mixture distributions, two-part model, Tobit model, survival analysis, non-parametric methods, data truncation, model averaging, Markov chain analysis, randomised trials, statistical method selection
- MeSH terms
- Data Interpretation, Statistical, Health Resources, Markov Chains, Linear Models, Randomized Controlled Trials as Topic, Health Care Costs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review