Main Effects

	BASEII					EAS					ELSA					HRS					ILSE					LBLS					LBC					MAP					MAS					MIDUS					NAS					OATS					ROS					SLS					WLS
Term	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p
Intercept	-3.63	0.26	0.03	-13.97	< .001	1.14	0.08	3.14	13.82	< .001	-4.35	0.10	0.01	-45.12	< .001	-2.68	0.03	0.07	-90.75	< .001	-1.09	0.12	0.34	-8.77	< .001	1.92	0.1	6.85	19.61	< .001	0.59	0.06	1.80	9.44	< .001	-4.58	0.32	0.01	-14.37	< .001	2.01	0.10	7.48	19.21	< .001	-3.26	0.09	0.04	-37.96	< .001	-1.32	0.09	0.27	-14.03	< .001	-3.16	0.22	0.04	-14.11	< .001	-6.72	0.83	0.00	-8.07	< .001	-2.33	0.10	0.10	-22.74	< .001	0.22	0.02	1.24	10.81	< .001
Neur	-0.14	0.25	0.87	-0.57	.569	0.07	0.09	1.07	0.74	.460	0.04	0.10	1.04	0.44	.660	0.12	0.03	1.12	3.90	< .001	-0.27	0.13	0.76	-2.05	.040	-0.29	0.1	0.75	-2.88	.004	-0.09	0.06	0.91	-1.40	.160	0.08	0.34	1.08	0.24	.812	0.05	0.11	1.05	0.43	.666	0.28	0.08	1.32	3.57	< .001	0.07	0.10	1.08	0.72	.470	0.09	0.22	1.09	0.39	.696	-1.07	0.72	0.34	-1.48	.138	-0.18	0.12	0.83	-1.53	.125	-0.08	0.02	0.92	-3.78	< .001
Con	-0.39	0.23	0.68	-1.70	.089	0.01	0.09	1.01	0.09	.932	-0.15	0.09	0.86	-1.66	.097	0.01	0.03	1.01	0.23	.821	-0.05	0.13	0.96	-0.35	.725	-0.23	0.1	0.79	-2.22	.027	0.13	0.06	1.14	2.04	.041	0.04	0.34	1.05	0.13	.897	-0.06	0.11	0.94	-0.57	.570	-0.18	0.08	0.84	-2.32	.020	-0.14	0.10	0.87	-1.34	.180	-0.30	0.22	0.74	-1.38	.168	-0.79	0.45	0.45	-1.73	.083	-0.17	0.12	0.85	-1.41	.159	-0.03	0.02	0.97	-1.57	.116
N	620.00					798.00					8647.00					19206.00					351.00					962.00					1143.00					982.00					879.00					4009.00					683.00					535.00					1394.00					1194.00					10206.00

Interaction, no covariates

	BASEII					EAS					ELSA					HRS					ILSE					LBLS					LBC					MAP					MAS					MIDUS					NAS					OATS					ROS					SLS					WLS
Term	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p
Intercept	-3.63	0.26	0.03	-13.79	< .001	1.20	0.09	3.31	13.26	< .001	-4.35	0.10	0.01	-44.49	< .001	-2.67	0.03	0.07	-88.80	< .001	-1.09	0.13	0.34	-8.49	< .001	2.00	0.11	7.41	18.61	< .001	0.56	0.06	1.75	8.89	< .001	-4.50	0.32	0.01	-14.12	< .001	1.95	0.11	7.01	17.32	< .001	-3.26	0.09	0.04	-37.70	< .001	-1.31	0.10	0.27	-13.08	< .001	-3.12	0.23	0.04	-13.54	< .001	-6.58	0.84	0.00	-7.84	< .001	-2.31	0.11	0.10	-21.12	< .001	0.23	0.02	1.25	11.01	< .001
Neur	-0.14	0.25	0.87	-0.56	.576	0.06	0.09	1.06	0.63	.531	0.04	0.10	1.04	0.39	.693	0.12	0.03	1.12	3.95	< .001	-0.27	0.13	0.76	-2.03	.042	-0.31	0.10	0.73	-3.05	.002	-0.08	0.06	0.93	-1.17	.243	0.02	0.34	1.02	0.06	.955	0.09	0.11	1.09	0.77	.440	0.29	0.08	1.34	3.69	< .001	0.06	0.10	1.07	0.61	.540	0.24	0.23	1.27	1.01	.311	-0.94	0.80	0.39	-1.17	.241	-0.18	0.12	0.84	-1.48	.140	-0.08	0.02	0.92	-3.95	< .001
Con	-0.39	0.23	0.68	-1.69	.090	-0.03	0.10	0.97	-0.31	.755	-0.15	0.09	0.86	-1.58	.113	0.00	0.03	1.00	-0.14	.891	-0.05	0.13	0.96	-0.35	.730	-0.30	0.11	0.74	-2.73	.006	0.13	0.07	1.14	1.92	.055	-0.01	0.34	0.99	-0.03	.977	-0.13	0.12	0.88	-1.05	.293	-0.21	0.08	0.81	-2.53	.011	-0.14	0.10	0.87	-1.40	.161	-0.43	0.24	0.65	-1.81	.070	-0.55	0.74	0.58	-0.74	.458	-0.16	0.12	0.85	-1.40	.161	-0.04	0.02	0.96	-1.73	.084
Neur x con	0.00	0.21	1.00	-0.01	.992	-0.11	0.07	0.89	-1.61	.108	-0.02	0.08	0.98	-0.23	.822	0.05	0.03	1.05	1.68	.093	0.01	0.12	1.01	0.04	.966	0.16	0.07	1.17	2.15	.032	-0.14	0.05	0.87	-2.62	.009	0.43	0.33	1.54	1.32	.186	-0.19	0.14	0.83	-1.38	.168	0.06	0.07	1.07	0.94	.348	0.05	0.11	1.05	0.44	.663	0.34	0.21	1.41	1.62	.105	0.32	0.75	1.37	0.42	.676	0.05	0.09	1.06	0.62	.537	0.04	0.02	1.04	2.22	.027
N	620.00					798.00					8647.00					19206.00					351.00					962.00					1143.00					982.00					879.00					4009.00					683.00					535.00					1394.00					1194.00					10206.00

Interaction, demographics

	BASEII					EAS					ELSA					HRS					ILSE					LBLS					LBC					MAP					MAS					MIDUS					NAS					OATS					ROS					SLS					WLS
Term	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p
Intercept	-2.74	0.41	0.06	-6.68	< .001	2.46	0.49	11.76	5.02	< .001	-3.57	0.22	0.03	-16.49	< .001	-2.09	0.06	0.12	-34.79	< .001	-20.85	828.99	0.00	-0.03	.980	6.87	2.35	965.05	2.93	.003	1.06	0.14	2.88	7.37	< .001	-4.72	1.55	0.01	-3.05	.002	1.93	0.66	6.87	2.93	.003	-2.69	0.11	0.07	-24.75	< .001	-0.88	0.21	0.42	-4.21	< .001	-0.08	1.23	0.92	-0.06	.948	-3.70	2.37	0.02	-1.56	.118	-2.46	0.21	0.09	-11.93	< .001	0.43	0.04	1.53	12.01	< .001
Age	-0.51	0.27	0.60	-1.90	.057	-0.15	0.08	0.86	-1.76	.079	-0.28	0.11	0.76	-2.52	.012	-0.34	0.03	0.71	-10.66	< .001	0.09	0.15	1.10	0.62	.535	-0.15	0.10	0.86	-1.54	.124	-0.25	0.07	0.78	-3.72	< .001	0.08	0.32	1.09	0.25	.799	-0.03	0.11	0.97	-0.32	.745	-0.80	0.10	0.45	-7.93	< .001	-0.27	0.10	0.76	-2.64	.008	-0.83	0.33	0.43	-2.49	.013	-0.81	0.74	0.45	-1.10	.273	-0.03	0.11	0.97	-0.28	.778	-0.09	0.02	0.91	-4.26	< .001
Gender	-0.85	0.52	0.43	-1.64	.102	-0.68	0.18	0.51	-3.71	< .001	-0.92	0.21	0.40	-4.42	< .001	-0.26	0.06	0.77	-4.27	< .001	19.62	828.98	332833619.44	0.02	.981	-0.82	0.21	0.44	-3.90	< .001	-0.24	0.13	0.79	-1.83	.067	-0.50	0.69	0.61	-0.73	.466	0.60	0.22	1.82	2.66	.008	-1.02	0.18	0.36	-5.81	< .001						-0.28	0.47	0.76	-0.59	.555	-0.97	1.31	0.38	-0.74	.458	0.29	0.21	1.34	1.38	.169	-0.23	0.04	0.80	-5.57	< .001
Edu	-0.21	0.26	0.81	-0.83	.408	0.14	0.09	1.15	1.66	.097	0.31	0.11	1.37	2.90	.004	0.17	0.03	1.18	5.16	< .001	-0.04	0.17	0.96	-0.26	.795	0.19	0.11	1.21	1.83	.067	0.09	0.07	1.10	1.37	.172	0.64	0.29	1.89	2.21	.027	0.10	0.11	1.10	0.87	.386	-0.43	0.09	0.65	-4.73	< .001	-0.45	0.12	0.64	-3.70	< .001	-0.63	0.27	0.53	-2.34	.019	0.72	0.59	2.05	1.22	.221	0.25	0.11	1.28	2.32	.020	0.09	0.02	1.09	4.23	< .001
Neur	-0.14	0.27	0.87	-0.52	.602	-0.01	0.10	0.99	-0.10	.923	0.06	0.10	1.06	0.56	.574	0.10	0.03	1.11	3.27	.001	0.12	0.17	1.13	0.68	.495	-0.23	0.11	0.80	-2.13	.034	-0.07	0.07	0.93	-1.02	.310	0.18	0.36	1.19	0.50	.619	0.06	0.12	1.06	0.47	.637	0.21	0.08	1.23	2.53	.011	0.08	0.11	1.08	0.74	.461	0.10	0.24	1.11	0.42	.672	-0.66	0.77	0.52	-0.85	.394	-0.17	0.12	0.85	-1.40	.161	-0.05	0.02	0.95	-2.56	.011
Con	-0.33	0.24	0.72	-1.39	.164	-0.02	0.10	0.98	-0.17	.866	-0.20	0.10	0.82	-2.03	.043	-0.06	0.03	0.95	-1.77	.077	-0.10	0.17	0.91	-0.59	.558	-0.25	0.11	0.78	-2.24	.025	0.12	0.07	1.13	1.82	.070	-0.03	0.35	0.97	-0.08	.935	-0.13	0.12	0.88	-1.05	.296	-0.17	0.08	0.84	-2.04	.042	-0.20	0.10	0.82	-1.91	.056	-0.50	0.25	0.60	-2.03	.042	-0.32	0.66	0.72	-0.49	.623	-0.18	0.12	0.84	-1.53	.127	-0.03	0.02	0.97	-1.23	.219
Neur x con	-0.02	0.23	0.98	-0.07	.940	-0.14	0.07	0.87	-1.95	.051	0.00	0.08	1.00	0.04	.965	0.06	0.03	1.06	2.04	.042	0.00	0.16	1.00	0.00	.996	0.18	0.08	1.19	2.29	.022	-0.13	0.05	0.88	-2.41	.016	0.47	0.35	1.60	1.34	.181	-0.17	0.14	0.84	-1.25	.212	0.04	0.07	1.04	0.50	.615	0.03	0.11	1.03	0.29	.769	0.31	0.22	1.36	1.38	.169	0.24	0.65	1.27	0.37	.708	0.06	0.09	1.06	0.72	.474	0.04	0.02	1.04	2.16	.031
N	620.00					798.00					8647.00					19206.00					351.00					962.00					1143.00					982.00					879.00					4009.00					683.00					535.00					1394.00					1194.00					10206.00

Interaction, all covariates

	BASEII					EAS					ELSA					HRS					ILSE					LBLS					LBC					MAP					MAS					MIDUS					NAS					OATS					ROS					SLS					WLS
Term	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p	Est	SE	OR	t value	p
Intercept	-2.87	0.44	0.06	-6.59	< .001	2.53	0.50	12.58	5.04	< .001	-3.67	0.22	0.03	-16.38	< .001	-2.16	0.06	0.12	-34.69	< .001	-21.02	827.07	0.00	-0.03	.980	6.98	2.37	1079.51	2.95	.003	1.01	0.15	2.75	6.96	< .001	-4.97	2.82	0.01	-1.76	.078	1.87	0.66	6.46	2.82	.005	-2.72	0.12	0.07	-23.28	< .001	-0.85	0.21	0.43	-4.07	< .001	-0.29	1.24	0.75	-0.24	.813	-4.06	2.46	0.02	-1.65	.100	-2.48	0.21	0.08	-11.70	< .001	0.43	0.04	1.54	12.01	< .001
Age	-0.51	0.27	0.60	-1.86	.062	-0.15	0.09	0.86	-1.80	.071	-0.26	0.11	0.77	-2.39	.017	-0.33	0.03	0.72	-10.22	< .001	0.11	0.15	1.11	0.70	.485	-0.16	0.10	0.85	-1.60	.110	-0.22	0.07	0.80	-3.30	.001	0.06	0.61	1.06	0.10	.919	-0.02	0.11	0.98	-0.21	.836	-0.79	0.10	0.46	-7.75	< .001	-0.29	0.10	0.75	-2.76	.006	-0.85	0.33	0.43	-2.56	.010	-0.73	0.76	0.48	-0.97	.333	-0.01	0.11	0.99	-0.06	.952	-0.09	0.02	0.91	-4.22	< .001
Gender	-0.77	0.54	0.46	-1.44	.149	-0.72	0.19	0.49	-3.84	< .001	-0.83	0.21	0.43	-3.89	< .001	-0.19	0.06	0.82	-3.10	.002	19.63	827.06	334684585.58	0.02	.981	-0.78	0.23	0.46	-3.43	.001	-0.21	0.13	0.81	-1.62	.105	-2.16	1.20	0.12	-1.81	.071	0.62	0.23	1.85	2.73	.006	-1.00	0.19	0.37	-5.36	< .001						0.01	0.50	1.01	0.03	.978	-0.97	1.34	0.38	-0.73	.468	0.26	0.22	1.30	1.18	.239	-0.24	0.04	0.79	-5.69	< .001
Edu	-0.27	0.26	0.77	-1.01	.311	0.12	0.10	1.13	1.29	.197	0.25	0.11	1.29	2.26	.024	0.14	0.03	1.15	4.31	< .001	-0.01	0.17	0.99	-0.04	.966	0.18	0.11	1.20	1.62	.105	0.09	0.07	1.09	1.26	.209	0.66	0.41	1.93	1.59	.111	0.06	0.12	1.07	0.54	.590	-0.44	0.09	0.65	-4.72	< .001	-0.45	0.12	0.64	-3.74	< .001	-0.64	0.28	0.53	-2.28	.022	0.71	0.63	2.03	1.12	.262	0.19	0.11	1.21	1.67	.095	0.08	0.02	1.08	3.50	< .001
Agree	0.08	0.27	1.08	0.29	.771	0.07	0.10	1.07	0.70	.482	-0.21	0.12	0.81	-1.76	.079	-0.17	0.04	0.85	-4.44	< .001	0.04	0.16	1.04	0.24	.812	-0.12	0.11	0.89	-1.04	.297	0.17	0.08	1.19	2.23	.026											-0.07	0.10	0.94	-0.68	.494	0.04	0.14	1.04	0.31	.759	-0.40	0.27	0.67	-1.49	.137	-0.22	0.62	0.81	-0.35	.729	-0.01	0.11	0.99	-0.05	.962	0.00	0.02	1.00	0.05	.959
Extra	-0.39	0.26	0.68	-1.48	.138	0.15	0.10	1.16	1.55	.122	0.15	0.14	1.16	1.10	.273	0.18	0.04	1.19	4.35	< .001	0.18	0.18	1.20	0.98	.328	0.06	0.11	1.06	0.54	.591	0.10	0.08	1.11	1.29	.198	-0.30	0.55	0.74	-0.55	.579						0.19	0.11	1.21	1.76	.078	-0.12	0.12	0.88	-1.03	.304	-0.39	0.25	0.68	-1.54	.124	0.39	0.74	1.47	0.53	.598	-0.16	0.12	0.85	-1.34	.181	0.05	0.02	1.05	2.43	.015
Open	0.24	0.28	1.27	0.84	.398	0.05	0.10	1.05	0.45	.656	0.23	0.13	1.26	1.69	.092	0.13	0.04	1.13	3.20	.001	-0.13	0.17	0.88	-0.77	.440	0.11	0.10	1.11	1.03	.303	-0.03	0.08	0.97	-0.34	.734						-0.17	0.13	0.85	-1.24	.213	0.10	0.10	1.10	0.94	.347	0.02	0.13	1.02	0.12	.902	-0.06	0.24	0.95	-0.24	.812	0.11	0.67	1.12	0.17	.864	0.23	0.12	1.25	1.81	.071	0.03	0.02	1.03	1.15	.250
Neur	-0.18	0.27	0.84	-0.65	.515	-0.08	0.11	0.92	-0.72	.470	0.09	0.10	1.10	0.90	.368	0.13	0.03	1.14	4.15	< .001	0.18	0.18	1.19	0.96	.335	-0.23	0.12	0.80	-1.94	.052	-0.13	0.08	0.88	-1.71	.087	-0.91	0.70	0.40	-1.30	.193	0.10	0.12	1.11	0.81	.416	0.25	0.08	1.28	2.95	.003	0.07	0.11	1.07	0.62	.535	-0.13	0.27	0.87	-0.50	.616	-0.64	0.87	0.52	-0.74	.461	-0.19	0.13	0.83	-1.50	.134	-0.04	0.02	0.96	-1.74	.082
Con	-0.36	0.25	0.70	-1.42	.154	-0.07	0.10	0.93	-0.70	.485	-0.27	0.12	0.76	-2.34	.019	-0.10	0.04	0.90	-2.85	.004	-0.11	0.17	0.89	-0.66	.507	-0.24	0.12	0.79	-2.01	.045	0.13	0.07	1.13	1.69	.092	-0.92	0.50	0.40	-1.83	.067	-0.18	0.13	0.84	-1.38	.167	-0.22	0.09	0.80	-2.45	.014	-0.20	0.14	0.82	-1.46	.145	-0.35	0.27	0.71	-1.29	.197	-0.31	0.66	0.73	-0.48	.631	-0.15	0.12	0.86	-1.27	.205	-0.04	0.02	0.96	-1.71	.086
Neur x con	-0.02	0.23	0.98	-0.10	.918	-0.15	0.07	0.86	-2.05	.041	0.01	0.08	1.01	0.14	.886	0.06	0.03	1.06	2.18	.029	0.00	0.16	1.00	0.02	.982	0.18	0.08	1.19	2.24	.025	-0.14	0.05	0.87	-2.61	.009	-0.10	0.33	0.90	-0.31	.760	-0.13	0.14	0.88	-0.95	.344	0.04	0.07	1.04	0.56	.578	0.03	0.11	1.03	0.27	.788	0.38	0.24	1.46	1.57	.117	0.22	0.64	1.25	0.34	.730	0.07	0.09	1.07	0.78	.434	0.04	0.02	1.04	2.23	.026
N	620.00					798.00					8633.00					19175.00					351.00					962.00					1143.00					653.00					879.00					4007.00					683.00					535.00					1394.00					1194.00					10181.00

Code

Preparation

The following packages were used to generate this table:

library(papaja)
library(tidyverse)
library(tools)
library(psych)
library(knitr)
library(kableExtra)
library(here)

The files needed for this table are available at osf.io/mzfu9 in the Individual Study Output folder. First we load the individual study analysis objects.

study.names = c("BASEII", "EAS","ELSA", "HRS", "ILSE", "LBC",
                "LBLS", "MAP", "MAS","MIDUS","NAS", "OATS", 
                "ROS","SLS","WLS")

lapply(here(paste0("behavior/", study.names, "_behavior.Rdata")), load, .GlobalEnv)

Then we identify which studies have smoking as an outcome.

#identify which studies have active outcome
drinker.names = lapply(paste0(study.names,"_behavior"), 
                      FUN = function(x) rownames(get(x)$descriptives))
drinker.names = sapply(drinker.names, FUN = function(x) "drinker" %in% x)
drinker.names = study.names[drinker.names]

columns = unite(
  expand.grid(stat = c("estimate", "std.error", "OR", "statistic", "p.value"),
              study = drinker.names),
  columns, study, stat, sep="_")$columns

header = c(" " = 1, "BASEII" = 5, "EAS" = 5, "ELSA" = 5, "HRS" = 5, "ILSE" = 5, 
           "LBLS" = 5, "LBC" = 5, "MAP" = 5, "MAS" = 5, "MIDUS" = 5, "NAS" = 5, "OATS" = 5,  
           "ROS" = 5, "SLS" = 5, "WLS" = 5)

Main Effects

#sample size
drinker.n = data.frame(study = drinker.names, 
                      term = "N", estimate = 
                        sapply(X = drinker.names, FUN = function(x)
                          get(paste0(x,"_behavior"))$regression$main$drinker$n)) %>%
  mutate(study = paste0(study, "_estimate")) %>%
  spread(key = "study", value = "estimate")

lapply(X = drinker.names, FUN = function(x) get(paste0(x,"_behavior"))$regression$main$drinker$coef) %>%
  map2_df(., drinker.names, ~ mutate(.x, study = .y)) %>%
  mutate(OR = exp(estimate)) %>%
  gather(key = "key", value = "value", -study, -term) %>%
  unite(study_stat, study, key, sep="_") %>%
  filter(!is.infinite(value)) %>%
  spread(key = "study_stat", value = "value") %>%
  gather(key = "key", value = "value", which(grepl("p", names(.)))) %>%
  mutate(sig = value, 
    value = printp(value),
    value = cell_spec(value, "html", 
                      color = ifelse(sig >= .05 | is.na(sig), 
                                     "black", "red"))) %>%
    filter(!grepl("NA", value))%>%
    dplyr::select(-sig) %>%
    spread(key = "key", value = "value") %>%
    full_join(drinker.n) %>%
    mutate(term = gsub("\\(", "", term), 
           term = gsub("\\)", "", term),
           term = toTitleCase(term),
           term = gsub("\\:", " x ", term), 
          term = factor(term, 
                         levels = c("Intercept", "Age", "Gender", "Edu", "Agree", "Extra", "Open", 
                                    "Neur", "Con", "Neur x con", "N"))) %>%
    arrange(term) %>%
    dplyr::select(term, columns) %>%
    kable(.,  booktabs = T, escape = F, digits = 2, format = "html", 
          align=c(rep('c',times=ncol(.))),
          col.names = c("Term", rep(c("Est","SE","OR", "t value", "p"), length(drinker.names)))) %>%
    kable_styling("bordered", full_width = TRUE) %>%
  add_header_above(header = header)

Interaction, no covariates

#sample size
drinker.n = data.frame(study = drinker.names, 
                      term = "N", estimate = 
                        sapply(X = drinker.names, FUN = function(x)
                          get(paste0(x,"_behavior"))$regression$intr$drinker$n)) %>%
  mutate(study = paste0(study, "_estimate")) %>%
  spread(key = "study", value = "estimate")

lapply(X = drinker.names, FUN = function(x) get(paste0(x,"_behavior"))$regression$intr$drinker$coef) %>%
  map2_df(., drinker.names, ~ mutate(.x, study = .y)) %>%
  mutate(OR = exp(estimate)) %>%
  gather(key = "key", value = "value", -study, -term) %>%
  unite(study_stat, study, key, sep="_") %>%
  filter(!is.infinite(value)) %>%
  spread(key = "study_stat", value = "value") %>%
  gather(key = "key", value = "value", which(grepl("p", names(.)))) %>%
  mutate(sig = value, 
    value = printp(value),
    value = cell_spec(value, "html", 
                      color = ifelse(sig >= .05 | is.na(sig), 
                                     "black", "red"))) %>%
    filter(!grepl("NA", value))%>%
    dplyr::select(-sig) %>%
    spread(key = "key", value = "value") %>%
    full_join(drinker.n) %>%
    mutate(term = gsub("\\(", "", term), 
           term = gsub("\\)", "", term),
           term = toTitleCase(term),
           term = gsub("\\:", " x ", term), 
           term = factor(term, 
                       levels = c("Intercept", "Age", "Gender", "Edu", "Agree", "Extra", "Open", 
                                  "Neur", "Con", "Neur x con", "N"))) %>%
  arrange(term) %>%
  dplyr::select(term, columns) %>%
  kable(.,  booktabs = T, escape = F, digits = 2, format = "html", 
        align=c(rep('c',times=ncol(.))),
        col.names = c("Term", rep(c("Est","SE","OR", "t value", "p"), length(drinker.names)))) %>%
  kable_styling("bordered", full_width = TRUE) %>%
add_header_above(header = header)

Interaction, demographics

#sample size
drinker.n = data.frame(study = drinker.names, 
                      term = "N", estimate = 
                        sapply(X = drinker.names, FUN = function(x)
                          get(paste0(x,"_behavior"))$regression$cov1$drinker$n)) %>%
  mutate(study = paste0(study, "_estimate")) %>%
  spread(key = "study", value = "estimate")

lapply(X = drinker.names, FUN = function(x) get(paste0(x,"_behavior"))$regression$cov1$drinker$coef) %>%
  map2_df(., drinker.names, ~ mutate(.x, study = .y)) %>%
  mutate(OR = exp(estimate)) %>%
  gather(key = "key", value = "value", -study, -term) %>%
  unite(study_stat, study, key, sep="_") %>%
  filter(!is.infinite(value)) %>%
  spread(key = "study_stat", value = "value") %>%
  gather(key = "key", value = "value", which(grepl("p", names(.)))) %>%
  filter(!is.na(value)) %>%
  mutate(sig = value, 
    value = printp(value),
    value = cell_spec(value, "html", 
                      color = ifelse(sig >= .05 | is.na(sig), 
                                     "black", "red"))) %>%
    filter(!grepl("NA", value))%>%
    dplyr::select(-sig) %>%
    spread(key = "key", value = "value") %>%
    full_join(drinker.n) %>%
    mutate(term = gsub("\\(", "", term), 
           term = gsub("\\)", "", term),
           term = toTitleCase(term),
           term = gsub("\\:", " x ", term), 
           term = factor(term, 
                         levels = c("Intercept", "Age", "Gender", "Edu", "Agree", "Extra", "Open", 
                                    "Neur", "Con", "Neur x con", "N"))) %>%
    arrange(term) %>%
    dplyr::select(term, columns) %>%
    kable(.,  booktabs = T, escape = F, digits = 2, format = "html", 
          align=c(rep('c',times=ncol(.))),
          col.names = c("Term", rep(c("Est","SE","OR", "t value", "p"), length(drinker.names)))) %>%
    kable_styling("bordered", full_width = TRUE) %>%
  add_header_above(header = header)

Interaction, all covariates

#sample size
drinker.n = data.frame(study = drinker.names, 
                      term = "N", estimate = 
                        sapply(X = drinker.names, FUN = function(x)
                          get(paste0(x,"_behavior"))$regression$cov2$drinker$n)) %>%
  mutate(study = paste0(study, "_estimate")) %>%
  spread(key = "study", value = "estimate")

lapply(X = drinker.names, FUN = function(x) get(paste0(x,"_behavior"))$regression$cov2$drinker$coef) %>%
  map2_df(., drinker.names, ~ mutate(.x, study = .y)) %>%
  mutate(OR = exp(estimate)) %>%
  gather(key = "key", value = "value", -study, -term) %>%
  unite(study_stat, study, key, sep="_") %>%
  filter(!is.infinite(value)) %>%
  spread(key = "study_stat", value = "value") %>%
  gather(key = "key", value = "value", which(grepl("p", names(.)))) %>%
  filter(!is.na(value)) %>%
  mutate(sig = value, 
    value = printp(value),
    value = cell_spec(value, "html", 
                      color = ifelse(sig >= .05 | is.na(sig), 
                                     "black", "red"))) %>%
  filter(!grepl("NA", value)) %>%
  dplyr::select(-sig) %>%
  spread(key = "key", value = "value") %>%
  full_join(drinker.n) %>%
  mutate(term = gsub("\\(", "", term), 
         term = gsub("\\)", "", term),
         term = toTitleCase(term),
         term = gsub("\\:", " x ", term), 
         term = factor(term, 
                       levels = c("Intercept", "Age", "Gender", "Edu", "Agree", "Extra", "Open", 
                                  "Neur", "Con", "Neur x con", "N"))) %>%
  arrange(term) %>%
  dplyr::select(term, columns) %>%
  kable(.,  booktabs = T, escape = F, digits = 2, format = "html", 
        align=c(rep('c',times=ncol(.))),
        col.names = c("Term", rep(c("Est","SE","OR", "t value", "p"), length(drinker.names)))) %>%
  kable_styling("bordered", full_width = TRUE) %>%
add_header_above(header = header)

Regression results for alcohol consumption

Main Effects

Interaction, no covariates

Interaction, demographics

Interaction, all covariates

Code

Preparation

Main Effects

Interaction, no covariates

Interaction, demographics

Interaction, all covariates