Propensity weighting plus adjustment in proportional hazards model is not doubly robust Article Swipe

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Erin E. Gabriel , Michael C. Sachs , Ingeborg Waernbaum , Els Goetghebeur , Paul Blanche , Stijn Vansteelandt , Arvid Sjölander , Thomas Scheike ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2310.16207

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline two simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

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Proportional hazards model Propensity score matching Estimator Censoring (clinical trials) Statistics Weighting Mathematics Inverse probability weighting Econometrics Accelerated failure time model Robustness (evolution) Parametric statistics Confounding Medicine Chemistry Gene Radiology Biochemistry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2310.16207
PDF: https://arxiv.org/pdf/2310.16207
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DOI: https://doi.org/10.48550/arxiv.2310.16207

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Title: Propensity weighting plus adjustment in proportional hazards model is not doubly robust

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Language: en

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Publication year: 2023

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Publication date: 2023-10-24

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Authors: Erin E. Gabriel, Michael C. Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul Blanche, Stijn Vansteelandt, Arvid Sjölander, Thomas Scheike

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Concepts: Proportional hazards model, Propensity score matching, Estimator, Censoring (clinical trials), Statistics, Weighting, Mathematics, Inverse probability weighting, Econometrics, Accelerated failure time model, Robustness (evolution), Parametric statistics, Confounding, Medicine, Chemistry, Gene, Radiology, Biochemistry

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abstract_inverted_index.exposure	37, 154
abstract_inverted_index.flexible	106
abstract_inverted_index.general,	65
abstract_inverted_index.methods,	15
abstract_inverted_index.modeled,	220
abstract_inverted_index.modeling	14
abstract_inverted_index.provided	213
abstract_inverted_index.survival	12, 134, 202, 231
abstract_inverted_index.unbiased	43
abstract_inverted_index.Recently,	0
abstract_inverted_index.censoring	160, 215
abstract_inverted_index.correctly	58, 172, 219
abstract_inverted_index.estimator	34
abstract_inverted_index.inference	244
abstract_inverted_index.intention	28
abstract_inverted_index.mechanism	216
abstract_inverted_index.specified	173
abstract_inverted_index.weighting	129
abstract_inverted_index.consistent	144
abstract_inverted_index.difference	203
abstract_inverted_index.estimation	227, 242
abstract_inverted_index.estimator,	70
abstract_inverted_index.estimators	195, 240
abstract_inverted_index.mechanisms	161
abstract_inverted_index.parametric	107
abstract_inverted_index.particular	159
abstract_inverted_index.propensity	23, 54, 127, 165
abstract_inverted_index.regression	73
abstract_inverted_index.robustness	90, 184
abstract_inverted_index.simulation	85
abstract_inverted_index.specified.	59
abstract_inverted_index.suggesting	181
abstract_inverted_index.supporting	247
abstract_inverted_index.weighting,	25
abstract_inverted_index.alternative	194
abstract_inverted_index.combination	61, 125
abstract_inverted_index.demonstrate	83
abstract_inverted_index.likelihood,	142
abstract_inverted_index.likelihood.	117
abstract_inverted_index.confounders.	177
abstract_inverted_index.information.	248
abstract_inverted_index.proportional	98, 104, 132
abstract_inverted_index.multivariable	19
abstract_inverted_index.semiparametric	93
abstract_inverted_index.standardization,	74
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	8
sustainable_development_goals[0].id	https://metadata.un.org/sdg/16
sustainable_development_goals[0].score	0.46000000834465027
sustainable_development_goals[0].display_name	Peace, Justice and strong institutions
citation_normalized_percentile